Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 762 appraisals
The Journal of rheumatology
Examining the Role of Wearables in Inflammatory Arthritis Care: A Narrative Literature Review
Rheumatoid arthritis, psoriatic arthritis, and axial spondyloarthritis are types of inflammatory arthritis (IA) characterized by joint pain and stiffness that, despite therapeutic advances, remain difficult to treat. Changes in physical activity (PA) and sleep patterns may provide insights into IA disease course and activity. Wearable accelerometers have been validated as reliable, objective measures of PA and have provided insight into IA disease activity and progression through both PA and sleep metrics. Further, the granular nature of accelerometry data may provide the opportunity to identify early signals of treatment response or disease flares, leading to more effective and personalized therapeutic regimens in patients with IA. In this review, we summarize the current state of wearable technology in IA and explore the potential of wearables to bridge gaps in care.
3 Aug 2026
Read appraisal →Acta obstetricia et gynecologica Scandinavica
ChatGPT in urogynecology: Comparing large language model responses to human experts
INTRODUCTION: Large language models (LLMs) are increasingly used in healthcare, including urogynecology, where stigma may limit open discussion. LLM-based chat platforms may provide a less intimidating and more accessible way for patients to obtain information, but their reliability requires evaluation. This study compared the quality of ChatGPT-generated responses in urogynecology with those provided by a consultant urogynecologist, focusing on understandability, helpfulness, and reassurance. MATERIAL AND METHODS: A cross-sectional survey was conducted among urogynecology patients. After informed consent, participants reviewed responses to six common questions, each answered by ChatGPT and a single consultant. A blinded third-party consultant verified clinical accuracy. Patients rated responses using a 5-point Likert scale across three domains (maximum score 15 per response). Wilcoxon signed-rank tests were used for comparison. RESULTS: A total of 203 patients participated (median age 56 years, interquartile range 46-66). ChatGPT responses received higher total ratings than consultant responses (76 [67-85] vs. 72 [63-80], p < 0.01). Scores were higher for understandability, helpfulness, and reassurance (all p < 0.01). ChatGPT was preferred in four of six questions, one showed no difference, and one favored the consultant. Subgroup analyses showed no significant variation based on patient characteristics. CONCLUSIONS: In this exploratory study, women rated ChatGPT's responses as clearer and more reassuring than consultant answers. These findings reflect patient perceptions in a limited setting and should be interpreted with caution. While LLMs may have a supportive role in patient education, their use must remain secondary to expert clinical care and subject to careful oversight.
3 Aug 2026
Read appraisal →Sleep medicine
Concordance of wearable device sleep metrics with patient-reported sleep quality: A systematic review
BACKGROUND: Commercial wearable devices increasingly monitor sleep, but their concordance with patient-reported sleep quality remains poorly characterized. This systematic review evaluates concordance between wearable sleep metrics and validated subjective measures. METHODS: Following PRISMA guidelines, we searched PubMed/MEDLINE, Embase, and Cochrane Library through October 2025 for studies comparing consumer wrist-worn actigraphy-based devices with validated subjective sleep quality measures in adults (≥18 years). Two reviewers independently performed screening, extraction, and QUADAS-2 quality assessment. GRADE criteria evaluated evidence certainty. RESULTS: Five observational studies (2006 participants) were included. Wearable devices showed poor to moderate agreement with subjective assessments, explaining only 2.5-16.2% variance. Total sleep time moderately correlated with same-day diaries (r = 0.367), but devices failed to capture Pittsburgh Sleep Quality Index scores. Agreement varied substantially by population: good sleepers showed 82.4% concordance versus 39.4% in insomnia patients (p = 0.006). Clinical populations and older adults demonstrated poor agreement. Polysomnography concordance was also poor: sleep efficiency showed fair intraclass correlation coefficient values (0.478-0.570) with systematic overestimation (+1.75% to +7.9%), sleep onset latency correlated poorly (r = 0.033), and wake after sleep onset was underestimated (-7 to -30 min). Evidence certainty ranged from low to moderate. CONCLUSIONS: Commercial wearable sleep trackers demonstrate poor to moderate agreement with validated subjective sleep quality measures, with significant population-specific variation. Device data should complement, not replace, validated subjective assessments, as current technology inadequately captures patient-reported sleep quality and shows systematic bias in objective parameters.
3 Aug 2026
Read appraisal →Journal of medical Internet research
Advancing Human-Centered AI in Clinical Decision Support: Sociocognitive Human-in-the-Loop Study in HIV Care
BACKGROUND: AI-powered clinical decision support systems (CDSS) have shown promise in improving prediction, monitoring, and treatment optimization across clinical domains, including HIV care. However, translating AI outputs derived from electronic health records into clinically meaningful, trustworthy, and actionable decision support remains challenging, underscoring the need for more human-centered and socioecologically grounded CDSS design. OBJECTIVE: This study aimed to explore how we can effectively translate the outputs of machine learning models based on HIV electronic health records into a real AI-powered CDSS for HIV care. Using the human-in-the-loop method, we engaged a set of stakeholders, including HIV physicians, nurse practitioners, infectious disease pharmacists, social workers, and case managers. Stakeholders interacted with an AI-powered CDSS prototype to identify barriers and challenges to adoption, as well as to inform a more holistic and context-aware AI-powered CDSS design. METHODS: We conducted a field study at Prisma Health in South Carolina that included pre- and postsurveys, interactive usability testing sessions, think-alouds, and in-depth interviews with 16 clinicians providing HIV care between March and September 2025. We analyzed survey responses using descriptive statistics, and then transcribed and analyzed think-aloud and interview data using an etic and emic approach. RESULTS: Clinicians identified multiple challenges and design considerations for AI-powered HIV CDSS, demonstrating that clinician-AI interaction is inherently sociotechnical and embedded across multiple socioecological levels. While clinicians relied on familiar clinical indicators as cognitive anchors for interpreting AI predictions, they emphasized that social determinants of health were central to their own risk assessment and clinical decision-making. Additionally, clinicians' trust in AI is conditional and develops over time, with explainability and actionability emerging as critical factors for translating predictions into meaningful clinical interventions. CONCLUSIONS: Findings highlight the need to move beyond technically accurate predictions toward AI-powered CDSS designs that align with clinicians' cognitive practices and socioecological realities of HIV care. By extending a sociocognitive framework through empirical grounding in HIV clinical practice, this study offers design insights for developing AI-powered CDSS that are trustworthy, context-aware, and capable of supporting actionable decision-making in HIV care settings and beyond.
2 Aug 2026
Read appraisal →Journal of psychiatric research
Systematic review of machine learning and deep learning models for EEG-based detection of depression.
OBJECTIVE: Depression is a leading cause of global disability, motivating the development of objective and scalable diagnostic approaches. Quantitative electroencephalography (QEEG) combined with machine learning (ML) and deep learning (DL) techniques has gained increasing attention for depression detection. This systematic review aimed to critically examine and descriptively compare the methodologies, performance metrics, and limitations of ML- and DL-based models applied to EEG data for depression detection. METHODS: A systematic review was conducted in accordance with PRISMA 2020 guidelines. Seven electronic databases (PubMed, Scopus, IEEE Xplore, ScienceDirect, Web of Science, SAGE Journals, and MDPI) were searched for peer-reviewed studies published between 2020 and 2024. Eligible studies included human participants, used EEG signals for depression detection, and applied ML or DL algorithms. Extracted information comprised algorithm type, sample size, EEG acquisition parameters, validation strategies, and reported performance metrics, which were synthesized descriptively across studies. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. RESULTS: A total of 42 studies met the inclusion criteria, including 23 ML-based and 19 DL-based investigations. Reported classification accuracy ranged from approximately 76% to 100%. DL studies showed a higher mean reported accuracy than ML studies (93.92% vs. 90.78%); however, this difference was not statistically significant in the exploratory non-parametric comparison. Near-perfect performance values were frequently observed in studies with small sample sizes and subject-dependent or exclusively internal validation strategies, raising concerns regarding overfitting and limited generalizability. Studies relying on publicly available datasets tended to report more stable performance. QUADAS-2 assessment revealed recurrent risk-of-bias concerns, particularly in the domains of patient selection and index test conduct. CONCLUSIONS: Both ML and DL approaches demonstrate potential for EEG-based depression detection, but reported performance differences between them should be interpreted cautiously. Although DL studies tended to report higher accuracy values, this pattern was not statistically significant in exploratory analyses and was strongly influenced by sample size, validation strategy, and methodological design. Future research should prioritize larger and more diverse samples, subject-independent or external validation strategies, and standardized reporting frameworks to enhance methodological rigor and clinical applicability.
2 Aug 2026
Read appraisal →Current opinion in pediatrics
Artificial intelligence in pediatric endocrinology: clinical applications, governance, and future directions.
PURPOSE OF REVIEW: Artificial intelligence has transitioned from theoretical promise to practical implementation across medicine including pediatric endocrinology. This review examines artificial intelligence applications across diabetes care, obesity management, thyroid disorders, growth and puberty, and bone age assessment followed by discussion of limitations and future directions. RECENT FINDINGS: Artificial intelligence-driven tools ranging from machine learning algorithms to multimodal large language models enable personalized risk prediction, diagnostic support, and treatment optimization. Human-in-the-Loop frameworks enhance safety by integrating computational efficiency with clinician oversight. In diabetes, artificial intelligence supports glycemic forecasting, automated insulin delivery, complication screening, and digital twin modeling for therapeutic discovery. In obesity, thyroid disease, growth disorders, and bone age assessment, artificial intelligence enhances diagnostic accuracy, risk stratification, and clinical workflow efficiency. However, widespread implementation raises concerns regarding algorithmic bias, data privacy, transparency, regulatory oversight, and health equity. Addressing these limitations through explainable artificial intelligence, federated learning, and continuous performance monitoring is essential. SUMMARY: Artificial intelligence should be viewed as an augmentative tool that enhances clinical judgment in pediatric endocrinology. Preparing the next generation of pediatric endocrinologists will require intentional integration of artificial intelligence literacy into training programs, equipping clinicians to critically evaluate algorithms, interpret outputs responsibly, and participate in the ethical development of future artificial intelligence-enabled tools.
2 Aug 2026
Read appraisal →Current opinion in pediatrics
Clinical implementation of artificial intelligence in adolescent mental healthcare
PURPOSE OF REVIEW: This review aims to summarize recent literature on artificial intelligence (AI) tools for adolescent mental health, including the types of tools available, their clinical applications, effectiveness, and safety, as well as relevant ethical considerations. RECENT FINDINGS: For clinicians, AI can facilitate clinical documentation, enhance therapy, and support the diagnosis process. Adolescents show interest in using AI for their mental healthcare and can benefit from AI-guided therapy apps and chatbots. Most studies that show effectiveness focus on depression treatment. Many tools are only in the prototyping stage, not tested on clinical samples, or lack safety measures, highlighting the need for further safety evaluation before specific app recommendations can be made. SUMMARY: AI is increasingly being implemented in pediatric health systems and adolescents' daily lives. Adolescent medicine practitioners should recognize the growing potential for certain AI applications to enhance access and support adolescents, review and utilize those applications that have empirical support of efficacy, and that provide guardrails for safe and ethical use.
1 Aug 2026
Read appraisal →JMIR mHealth and uHealth
Machine Learning Frameworks for Wearable-Based Stress Modeling in Naturalistic Settings: Scoping Review
BACKGROUND: Stress, as commonly recognized, is an integral part of modern life and can significantly affect both mental and physical health. While substantial advancements have been made in measuring physical fitness through wearable devices, the detection and assessment of mental stress remain in their early stages. OBJECTIVE: The objective of this paper is to review recent studies of wearable-based stress detection in naturalistic settings, with a specific focus on characterizing machine learning frameworks inspired by the model card approach. METHODS: This review was conducted using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. A total of 353 articles were identified through searches in databases such as PubMed, MEDLINE, ScienceDirect, IEEE, ACM Digital Library, Web of Science, and Embase. Studies were considered eligible if they collected data from healthy adults in naturalistic settings using wearable devices and used machine learning models for stress detection. RESULTS: A total of 34 articles met the eligibility criteria, including 11 conference papers, 22 journal articles, and 1 preprint published between 2017 and 2024. From these studies, we analyzed key machine learning modeling decisions such as problem formulation, ground truth determination, and machine learning algorithms. Additionally, we examined the major contributions of each study, focusing on the challenges they addressed and the solutions they proposed. Based on these findings, we proposed a model card framework for reporting machine learning-based, wearable-based stress detection. CONCLUSIONS: This scoping review highlights recent trends in machine learning models for stress detection and measurement using wearable signals. It underscores the need for improved standardization in reporting practices for datasets and key machine learning decisions, as well as the importance of addressing critical challenges associated with data collection in real-world settings. We hope this review will support and strengthen ongoing research efforts, promote knowledge sharing, and promote collaboration among researchers-ultimately advancing the field as a community.
1 Aug 2026
Read appraisal →Artificial intelligence in medicine
Green artificial intelligence in health applications
As the healthcare sector increasingly integrates Artificial Intelligence (AI) technologies to improve operational effectiveness, diagnosis, and therapy, the environmental footprint of these innovations has become a growing concern. High energy consumption, electronic waste, and carbon emissions associated with the deployment and training of AI models pose sustainability challenges that must be addressed. This paper investigates the concept and application of Green AI in healthcare, aiming to balance technological advancement with environmental responsibility. This systematic review explores key themes, including green computing practices, the adoption of energy-efficient AI models, and the use of renewable energy sources within healthcare settings. It identifies a range of healthcare applications employing Green AI, highlights emerging trends, and emphasizes the growing importance of environmental awareness in AI development. Furthermore, the study examines enabling tools and techniques, outlines barriers to adoption, and highlights how Green AI can help streamline processes, reduce resource waste, and promote environmentally friendly medical procedures like telemedicine. The review also discusses the significance of policy frameworks, international initiatives, and cross-sector collaboration in promoting environmentally responsible AI deployment. Finally, the paper presents practical implications and outlines future research directions to guide the sustainable evolution of AI in healthcare.
1 Aug 2026
Read appraisal →JMIR formative research
Teaching Health Informatics in a Skills-First Era: A Curriculum Mapping Approach Aligned With Workforce Demand
Academic programs in health informatics have been urged to align curricula with evolving workforce needs to better prepare graduates for practice. Such alignment supports workforce readiness, addresses talent shortages, and reduces onboarding burdens for employers. Although employers increasingly seek graduates who can demonstrate applied skills and competencies beyond textbook knowledge, academic programs continue to face challenges in translating workforce demand into curricular design. This Viewpoint is directed at health informatics program faculty and administrators and presents the author's perspective on a skills-first curriculum mapping approach aligned with current industry needs. The author argues that connecting program-level outcomes, course objectives, and learning activities to industry-recognized competencies strengthens workforce readiness while maintaining academic rigor. Drawing on the literature on competency-based education, workforce development, and health informatics training, this paper argues for intentional curriculum design that integrates transferable skills, stackable credentials, and stakeholder engagement. The key messages are as follows: (1) workforce demand should anchor curriculum design in health informatics programs, (2) curriculum mapping is a practical mechanism for operationalizing skills-first alignment, and (3) credential integration and continuous congruency evaluation support long-term program sustainability. This approach positions health informatics education to remain responsive, scalable, and aligned with the rapidly evolving professional landscape.
31 July 2026
Read appraisal →Scientific reports
Adaptive level modification via player skill classification and large language models
Maintaining player engagement in video games requires a careful balance between challenge and player competence. Static difficulty settings fail to account for individual skill variation, while existing dynamic difficulty adjustment systems are limited to tuning low-level game parameters rather than restructuring level content. This paper presents an adaptive level modification framework that personalizes gameplay by continuously inferring player skill and applying targeted structural modifications to level content in real-time. A hybrid behavioral dataset is constructed by combining agent-generated trajectories, produced by Proximal Policy Optimization (PPO) agents, a reinforcement learning approach, trained at three distinct skill levels, with manually collected human gameplay data labeled through clustering. A classifier trained on this dataset categorizes players into expert, normal, and beginner skill levels, achieving an overall accuracy of 97.82%. The classifier output drives a two-stage large language model (LLM) pipeline guided by prompt engineering, which expands a skill-conditioned prompt into a structured modification instruction applied to the current level chunk. A physics-constrained verifier based on a graph-based shortest path method ensures all modified levels remain traversable. Evaluated on Super Mario Bros. levels, the framework achieves a post-modification playability rate of 74.1% at the full-level granularity and 83.5% at the isolated-chunk granularity, closely matching the 80.0% baseline of the original levels.
30 July 2026
Read appraisal →PloS one
Evaluating the cultural alignment of multilingual LLMs in typical Japanese workplace scenarios
While current evaluations of LLM cultural alignment predominantly rely on static benchmarks in Western contexts, their ability to navigate generative, high-context socio-pragmatic demands in non-Western environments remains critically underexplored. This study investigates how multilingual LLMs adapt to the Japanese workplace-a stringent stress-test environment characterized by strong high-context communication norms and rigid honorific conventions-using Hofstede's six cultural dimensions as a heuristic framework. We evaluated five state-of-the-art LLMs (LLM-jp, Phi, Llama, Qwen, and GLM) through a large-scale crowdsourced human evaluation. Based on 1,718 valid evaluator sessions, native Japanese raters assessed model outputs to generate a holistic Japanese Workplace Cultural Alignment Score (JWCAS). To dissect the underlying communicative strategies, we paired this with a three-layer diagnostic sub-score analysis (Linguistic Form, Socio-Cultural Values, and Social Action). Our results reveal that leading multilingual models (Phi and GLM) achieved overall JWCAS scores comparable to, or significantly higher than, the native Japanese model (LLM-jp). Crucially, our sub-score analysis demonstrates that holistic evaluation metrics can obscure deep pragmatic deficits: while LLM-jp overfits to surface-level linguistic politeness (Layer 1), it shows critical weaknesses in socio-cultural values (Layer 2) and context-aware social strategies (Layer 3). In contrast, leading multilingual models demonstrate balanced competence across all layers. These findings suggest that true cultural competence requires moving beyond native linguistic mastery, highlighting the necessity of multi-dimensional diagnostic frameworks for cross-cultural AI alignment.
29 July 2026
Read appraisal →Neuropsychologia
Reading between the lines: Combining pause dynamics and semantic coherence for automated assessment of thought disorder.
Formal thought disorder (FTD), a hallmark of schizophrenia spectrum disorders, manifests as incoherent speech and poses challenges for clinical assessment. Traditional clinical rating scales, though validated, are resource-intensive and lack scalability. Automated speech recognition (ASR) allows for objective quantification of linguistic and temporal features of speech, offering scalable alternatives. Furthermore, ASR-derived utterance timestamps provide access to pause dynamics, which are thought to reflect the cognitive processes underlying speech production. Yet, their added value beyond semantic measures remains insufficiently explored. In this study, we evaluated a scalable multimodal framework that integrates pause features with semantic coherence metrics across three datasets: naturalistic self-recorded diaries (AVH, n = 140 participants), structured picture descriptions (TOPSY, n = 72 participants), and dream narratives (PsyCL, n = 43 participants). Pause-related features were evaluated alongside established coherence measures using support vector regression (SVR) to predict clinical FTD scores. Models using pause features alone robustly predict manually rated FTD severity consistently across datasets. Integrating pause features with semantic coherence metrics enhanced predictive performance compared to coherence-only models, with late fusion yielding the most robust and consistent gains in all three datasets. On average across datasets, Spearman correlation increased from ρ = 0.413 for semantic-only models to ρ = 0.455 with late fusion. The performance gains from semantic and pause features integration held consistently across all contexts, though the nature of the most informative pause patterns was dataset-dependent. These findings suggest that both pause dynamics and semantic coherence reflect complementary aspects of thought disorganization. Our integration framework provides a scalable approach for refining the assessment of disorganized speech to advance automated speech analysis in psychosis.
29 July 2026
Read appraisal →PloS one
PAP_NER: A large-scale vietnamese administrative named entity recognition corpus and hybrid deep learning architecture
Named Entity Recognition (NER) is fundamental for automating administrative document processing in digital government systems. However, Vietnamese NLP research faces a critical infrastructure gap: existing datasets focus on generic information extraction (news, medical) rather than domain-specific administrative text. We present PAP_NER, the first large-scale, gold-standard Vietnamese administrative NER corpus comprising 162,801 sentences with 205,807 entity annotations across five entity types critical for e-Government workflows: Agency (CQ), Legal Document (VBPL), Object (ĐT), Datetime (NG), and Quantity (SL). The dataset was constructed through a rigorous human-in-the-loop annotation pipeline, achieving an inter-annotator agreement of κ = 0.85. We demonstrate PAP_NER's value through comprehensive benchmarking of an established hybrid deep learning architecture, PhoBERT-CRF, which couples monolingual Transformer embeddings (PhoBERT) with Conditional Random Fields for structured prediction. PhoBERT-CRF achieves 97.95% Micro F1-score on the PAP_NER test set, significantly outperforming established baselines: BiLSTM+CRF (+2.01%), multilingual XLM-RoBERTa (+2.52%), and pure Transformer approaches (+0.44%). Ablation analysis reveals that the CRF layer provides statistically significant improvements for structurally complex entities (VBPL: + 0.96%, p < 0.05, McNemar's test). We release PAP_NER publicly (DOI: 10.5281/zenodo.18044019) under Creative Commons BY 4.0 license to support reproducibility and enable further research in Vietnamese administrative NLP. This work establishes a foundational dataset and methodology for addressing the Vietnamese government NER gap, with implications for low-resource language NLP research.
28 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
The past and future of neuropsychological assessment: Back to the origins to face digital technological changes.
BACKGROUND: The procedures used for the neuropsychological assessment of patients with dementia have been undergoing significant transformations relating to technological developments and the diffusion of artificial intelligence tools. OBJECTIVES: The integration of a theoretical position and a practical approach is needed in clinical contexts so that newly available tools can enhance the fundamental principles underlying neuropsychological assessment. METHODS: Following a historical review of the various approaches to neuropsychological assessment, an analysis of the risks associated with the introduction of digital techniques, when not supervised by expert clinicians, is presented. The need to integrate new technologies in a comprehensive assessment involving clinical observation, ad-hoc tests, and other sources of information such as neuroimaging and neurophysiological data is discussed. RESULTS: The integration of new technologies with a clinical, personalised approach to the patient represents a challenge for neuropsychology that deserves ad-hoc research and discussion.
26 July 2026
Read appraisal →Behavior research methods
Measuring complex constructs in large-scale text with computational social mixed methods
A growing convergence between social science and machine learning enables, in principle, large-scale analyses of complex social phenomena through text. Yet, approaches leveraging supervised text classification based on human-annotated data for statistical analysis often treat conceptual validity and technical performance as separate challenges, impairing measurement quality. We provide guidelines to bridge this gap in what we call computational social mixed methods pipelines across three stages: data annotation, model training, and statistical analysis. Building on best practices and our own methodological innovations, such as "Iterative Annotation" and "Training on Confident Examples", we address recurring pitfalls like unbalanced training data or stagnant model performance. We also discuss when large language models constitute a viable alternative to transformer-based classifiers. Using a case study on countering online hate, we illustrate how consequently integrating social science and machine learning expertise improves the validity and comparability of computational social science.
24 July 2026
Read appraisal →PloS one
"We'll have to see how it works": An interview study to understand collaborative practices in interdisciplinary artificial intelligence and healthcare research.
Developing artificial intelligence (AI) algorithms for healthcare is a collaborative effort, bringing data scientists, clinicians, patients and other stakeholders together. By understanding AI as 'sociotechnical' where the social and the technical nature of the work and the models are inseparable, we explore the AI development workflow and how stakeholders navigate the challenges and tensions of sharing and generating knowledge across disciplines. We conducted an inductive thematic analysis of 13 semi-structured interviews with participants in early stages of AI-in-healthcare research consortia in the UK. Our findings identify that participants needed to adapt both the tools used for sharing and the information communicated according to their audience, particularly when working with those with a clinical or patient perspective. We identify the novelty of participating in AI research, how AI knowledge is shared, and the inclusion of clinician and patient stakeholder perspectives as key areas within collaborative AI practices in healthcare. These findings highlight that bringing AI into the mix can introduce new obstacles to interdisciplinary work.
24 July 2026
Read appraisal →Journal of robotic surgery
Reliability and readability of AI chatbot responses to patient questions about robot-assisted radical cystectomy
Robot-assisted radical cystectomy (RARC) is a complex procedure that requires patients to understand surgical indications, urinary diversion, perioperative treatment, complications, recovery, and long-term functional outcomes. Although artificial intelligence (AI) chatbots are increasingly used to obtain medical information, their suitability for RARC patient education remains unclear. We conducted a cross-sectional comparative evaluation of four contemporary AI chatbots: ChatGPT-5, DeepSeek-V4, Claude Sonnet 4.6, and Gemini 3.5 Pro. A set of 20 core patient-education questions on RARC was developed by three senior urologic experts. Chatbot responses were assessed using DISCERN, the Ensuring Quality Information for Patients tool, the Global Quality Scale, and JAMA benchmark criteria. Readability was evaluated using the Automated Readability Index, Coleman-Liau Index, Flesch-Kincaid Grade Level, Flesch Reading Ease, Gunning Fog Index, and SMOG. Reliability scores differed significantly across models for DISCERN, EQIP, and GQS, while JAMA benchmark criteria were summarized descriptively as transparency signals. DeepSeek-V4 achieved the highest mean scores for DISCERN, EQIP, and GQS, while ChatGPT-5 and DeepSeek-V4 showed the strongest JAMA benchmark performance. Gemini 3.5 Pro generally had the lowest reliability and transparency scores. Readability also varied across models. DeepSeek-V4 produced the most readable responses overall, whereas Gemini 3.5 Pro generated the most complex text. However, all models exceeded the recommended sixth-grade reading level, and FRES scores remained below the recommended threshold. Contemporary AI chatbots generated responses with variable presentation quality, transparency, and readability for common RARC patient-education questions. Because factual accuracy was not directly assessed, these tools should not be interpreted as validated sources of clinical guidance and should not replace individualized counseling by urologists.
23 July 2026
Read appraisal →MedEdPORTAL : the journal of teaching and learning resources
Artificial Intelligence and Large Language Models: A Case-Based, Peer-Teaching Workshop for Preclinical Medical Students
INTRODUCTION: Artificial intelligence tools have rapidly become integrated in health care settings and are quickly affecting medical student education. However, there remains limited formal teaching on such tools in student curricula. This project sought to introduce preclerkship medical students to the basics of large language models and allow them to practice ways to best use these tools to supplement their learning. METHODS: The authors designed, implemented, and evaluated a 60-minute lecture and 100-minute workshop for second-year medical students. The workshop included interactive cases covering various aspects of artificial intelligence use, and some groups were led entirely by student leaders, allowing for peer teaching. RESULTS: One hundred sixty-eight students from Harvard Medical School and Harvard School of Dental Medicine were enrolled in this session. Anonymous pre- and postsession surveys (N = 124 and N = 62, respectively) were collected and compared via unpaired t test assuming unequal variance and showed statistically significant increase in the mean ratings of six 5-point Likert scale questions assessing artificial intelligence-related knowledge/self-efficacy (P < .01) and mixed changes to 5 questions relating to attitudes/behavioral intent. DISCUSSION: Our artificial intelligence and large language model session provides a framework for teaching medical students, early in their educational journey, the basics of these tools. Our session provides interactive exercises to illustrate how best to leverage such tools while also discussing their potential risks. Such education will be important to incorporate into medical student curricula as artificial intelligence technologies grow increasingly common.
23 July 2026
Read appraisal →Journal of medical Internet research
AI-Powered Simulation for Nursing Education: Mixed Methods Systematic Review
BACKGROUND: Traditional simulation-based nursing education is often constrained by high costs, resource intensity, and limited scalability. AI-powered simulations offer dynamic, scalable, and personalized alternatives. However, the empirical evidence regarding their pedagogical effectiveness and learner acceptance remains fragmented. OBJECTIVE: This study aimed to systematically evaluate and synthesize evidence on the effectiveness and learner perceptions of AI-powered simulations in nursing education. METHODS: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically searched 11 electronic databases (PubMed, CINAHL, Embase, Web of Science, Cochrane Library, Scopus, SinoMed, CNKI, Wanfang, VIP, and Google Scholar) for studies published between January 2014 and September 2025. Two independent reviewers performed study selection, data extraction, and quality appraisal using design-specific tools (risk of bias 2 tool [RoB 2; Cochrane Bias Methods Group] for randomized controlled trials [RCTs], Risk Of Bias in Nonrandomized Studies of Interventions [ROBINS-I; Cochrane Bias Methods Group] for nonrandomized studies, Mixed Methods Appraisal Tool [MMAT] for mixed methods, Joanna Briggs Institute [JBI] for qualitative, and Agency for Healthcare Research and Quality [AHRQ] for cross-sectional studies). Quantitative data were synthesized narratively, and qualitative findings were integrated using JBI meta-aggregation. A convergent segregated approach with joint display was used to generate meta-inferences. RESULTS: Nineteen studies involving 1253 participants (primarily prelicensure nursing students, with some interdisciplinary cohorts) were included. AI modalities comprised generative AI/large language models (n=7), AI-driven virtual patients/mannequins (n=5), AI-enhanced virtual/mixed reality (n=5), and chatbots (n=2). Three studies were RCTs, 4 were quasiexperimental with control groups, 3 were uncontrolled pre-post studies, 4 were mixed methods, 4 were qualitative, and one was a cross-sectional survey. Quantitative synthesis showed that evidence from RCTs and controlled quasiexperimental studies indicates significant improvements in cognitive knowledge and affective outcomes, including self-efficacy and communication confidence; however, effects on complex psychomotor skills were inconsistent, with one RCT finding AI-assisted simulation inferior to standardized patient simulation. Findings from uncontrolled designs are preliminary. Qualitative meta-aggregation revealed that learners valued safe, repeatable, nonjudgmental practice environments that reduced anxiety and bridged the theory-practice gap. Persistent challenges included technical frustrations, "robotic" interactions, lack of nonverbal cues, and system instability, collectively constituting an "authenticity gap." CONCLUSIONS: AI-powered simulations show promise for developing foundational clinical reasoning and communication skills in nursing education, though the evidence base is limited by the predominance of uncontrolled designs, reliance on self-reported measures, and absence of longitudinal data on skill retention or clinical transfer. Due to current technological limitations in replicating physical and emotional authenticity, AI should be implemented as a complementary tool alongside traditional simulation methods and clinical placements, rather than as a replacement. Future research should prioritize longitudinal outcomes, standardized competency measures, RCTs with active comparators, and implementation strategies addressing technical barriers.
22 July 2026
Read appraisal →Scientific reports
The role of AI in combating misinformation: leveraging text mining and social networking analysis
Misinformation on social media can be a severe threat to social trust, safety, and health of the population, especially in times of an epidemic like the Monkeypox outbreak. This study specifically focuses on a hybrid RoBERTa-GRU architecture designed to capture both contextual semantics and temporal dependencies in social media discourse. This research presents how the combination of text mining and social network analysis enables Artificial Intelligence (AI) to support misinformation detection. The proposal of a hybrid architecture that integrates RoBERTa and GRU-based embeddings in a contextual fashion and GRU-based modelling of sequential patterns helps identify and substantiate misinformation in social media posts. Based on a curated X (formerly Twitter) dataset consisting of Monkeypox posts (5787 posts), the model provided state-of-the-art results, with ROC-AUC 0.9979 and Cohen's kappa 0.9887; standalone baselines were also surpassed. Results reveal that the proposed transformer-RNN hybrid effectively captures both semantic depth and temporal relationships in misinformation detection tasks. In addition to performance, the paper addresses the limitations of dataset bias, multilingual constraint issues, and scalability as related to cross-linguistic applicability, multimodal study, and performance in real-time and resource-limited systems. The study adds value in this emerging body of knowledge on AI-driven social media analytics by offering practical guidance for mitigating health-related misinformation online.
22 July 2026
Read appraisal →JMIR cancer
Multiturn Large Language Model-Based Conversational Agents for Patients With Cancer and Caregivers: Scoping Review
BACKGROUND: Large language model (LLM)-based conversational agents are increasingly used in health care, yet their capacity to support genuine multiturn dialogue remains underexplored. In oncology, where patients and caregivers experience complex informational and emotional needs throughout the disease trajectory, conversational agents may support information provision, symptom consultation, and emotional assistance. However, research specifically examining multiturn conversational agents designed for patients with cancer and informal caregivers remains limited. OBJECTIVE: This scoping review aimed to map the research landscape of LLM-based multiturn conversational chatbots developed for patients with cancer and informal caregivers, focusing on system design, intervention purposes, evaluation approaches, safety considerations, and transparency of LLM-related components. METHODS: This scoping review followed the Joanna Briggs Institute methodology and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Six databases-PubMed, Embase, Scopus, Web of Science, CINAHL, and PsycINFO-were searched for studies published between January 2022 and January 2026, with supplementary searches conducted in IEEE Xplore Digital Library and ACM Digital Library in May 2026. Studies were included if they described LLM-based chatbots designed for patients with cancer or informal caregivers that supported multiturn conversational interaction. Two reviewers independently conducted the study selection and data extraction. RESULTS: Eight studies met the inclusion criteria. Most studies focused on prototype development, with limited research evaluating clinical outcomes. ChatGPT-based models were the most commonly used LLMs, and retrieval-augmented generation techniques were applied in several studies. Chatbots were primarily designed for emotional support or information provision. Evaluation approaches varied widely, including response quality, psychological outcomes, and user experience. However, no studies evaluated interaction-level characteristics such as conversational continuity or context retention, and only 2 studies reported any conversational memory mechanism. Reporting on safety risks, mitigation strategies, prompt design, model parameters, and adherence to LLM reporting guidelines was often limited or absent. CONCLUSIONS: This scoping review identified only 8 studies on LLM-based multiturn conversational chatbots for patients with cancer and informal caregivers. The field remains at an early stage, characterized by prototype-oriented development, heterogeneous design and evaluation approaches, and inconsistent safety and transparency reporting. Future development should prioritize genuine conversational capability, safety management, and transparent reporting.
22 July 2026
Read appraisal →PloS one
Textual overlap rather than domain alignment: A comparative study of fine-tuning strategies for specialised machine translation with large language models
General-purpose large language models (LLMs) may struggle in specialised machine translation, but the conditions under which fine-tuning improves translation performance remain unclear. This study compares full-parameter fine-tuning (FPFT) and parameter-efficient fine-tuning (PEFT) for Chinese-English political discourse translation using a purpose-built corpus and the Qwen3-14B model. Translation performance was assessed on three 50-item test sets using BLEU, ROUGE-L F1, METEOR, and BERTScore F1, together with BLEU pass-rate likelihood-ratio G2 tests, paired t-tests, and paired Cohen's dz for item-level score differences. The results reveal a clear contrast between unseen in-domain evaluation, maximum-overlap benchmarking, and semantically related but non-fine-tuned evaluation. On Test Set A and Test Set C, neither fine-tuning strategy produced a statistically significant BLEU pass-rate advantage over the base model, and paired tests across the continuous metrics did not show consistent fine-tuning gains. On Test Set B, which was sampled from the fine-tuning corpus, both fine-tuned models substantially outperformed the base model across all four metrics, with FPFT achieving the highest scores and PEFT providing a more computationally efficient alternative. These findings indicate that textual overlap between training and deployment data, rather than broad domain similarity alone, strongly conditions the observed benefit of fine-tuning. The study offers an empirically grounded framework for selecting fine-tuning strategies in specialised machine translation.
22 July 2026
Read appraisal →Science advances
Reduction of appearance artifacts in wearable on-skin electronics
Facial electrophysiological signals are crucial to human-machine interfaces and health care monitoring. Soft and skin-conformable electrodes enabled long-term and comfortable signal monitoring. However, the appearance of the electrodes affects the wearer's social interactions and self-identity, making daily usage difficult and leaving appearance artifacts. Here, we developed fully invisible and unperceivable on-skin electrodes free from appearance artifacts. Neither the wearer nor observers can detect the visual and tactile presence of the electrodes on the skin. The unperceivable property was confirmed with sensory experiments and physical characterizations of the film on skin. Furthermore, our invisible electrodes did not affect the psychological conditions of the wearers, which confirms the feasibility of artifact-free monitoring in daily lives. Last, we demonstrated the functionality of our electrode with successful monitoring of various facial electrophysiological signals, including electrooculogram (EOG), electromyogram (EMG), and electroencephalogram (EEG). Our fully invisible electrodes provide a promising direction in developing on-skin bioelectronics, seamlessly integrating health monitoring and human-computer interaction technologies into people's daily lives.
19 July 2026
Read appraisal →Journal of medical systems
Recent Advances in AI for Automated ICD Coding: A Systematic Literature Review
International Classification of Diseases (ICD) codes enable correct billing, insurance reimbursement, and healthcare analytics. However, manual coding is time-consuming, expensive, and error-prone, creating bottlenecks in clinical workflow and limiting scalability. Artificial intelligence (AI) has emerged as a promising solution for automated ICD code assignment from unstructured clinical text. This systematic review explores the current state of automated ICD coding research, examining models applied to diverse clinical documents including discharge summaries, electronic health records, nursing notes, and pathology reports. Following PRISMA guidelines, we searched six databases for studies published between 2019 and 2024, selecting 54 relevant studies from 4,280 initial citations. Our analysis reveals the use of diverse datasets, preprocessing techniques, and feature extraction methods, alongside a clear evolution from traditional machine learning to deep learning approaches, with substantial architectural diversity across convolutional, recurrent, transformer, and hybrid models. Performance varies considerably across dataset configurations, with models achieving higher accuracy on frequent code subsets compared to full label spaces. However, critical gaps persist: overreliance on single-language, single-institution datasets limits generalizability; difficulties in predicting rare codes remain unresolved; lack of model interpretability undermines clinical trust; and inconsistent evaluation protocols hinder meaningful comparison. To address these challenges, we propose a 5P evidence-grounded research agenda: Population Diversity, Performance Robustness, Prediction of Rare Codes, Provenance Transparency, and Practical Integration. These findings underscore AI's potential to transform ICD coding while highlighting the need for standardized benchmarks, rigorous external validation, multilingual datasets, and explainable architectures to enable equitable and effective deployment in real-world healthcare systems.
19 July 2026
Read appraisal →PloS one
A hybrid deep learning model for user story effort estimation
Accurate effort estimation of user stories is a key challenge in agile software development due to both subjectivity and the complexity of natural language requirements. This paper proposes a hybrid Deep Learning (DL) model for data driven effort estimation using large scale textual data and advanced semantic modeling. One of the significant contributions is the development of a dataset of 6,956 user stories which was collected from many heterogeneous sources and then meticulously cleaned and refined to 4,079 high quality instances, by using a systematic preprocessing and expert validation. Following a Design Science Research (DSR) methodology, a hybrid model integrating a pre-trained Bidirectional Encoder Representations from Transformers (BERT) encoder with a Long Short-Term Memory (LSTM) is developed to capture both contextual semantics and sequential dependencies in user story description. The DL model is evaluated against multiple Machine Learning (ML) baselines using a robust multi-metric framework. Experimental findings show the superior performance of the proposed model with a Mean Absolute Error (MAE) = 0.6481, Root Mean Square Error (RMSE) = 1.4559 and = 0.6581, which is a huge improvement over the conventional methods. To ensure practical relevance in discrete Scrum planning, the continuous model outputs were mapped to the standard Fibonacci sequence, achieving a classification accuracy of 72%. To guarantee the consistency of performance improvements, the statistical validation is done with the Wilcoxon Signed-Rank Test to show that the improvements are significant (p < 0.05). Moreover, the model is operationalized as web based decision support system to enable real time estimation in agile software development. The results reveal the efficacy of using large scale curated datasets alongside hybrid DL model to reduce estimation bias and boost predictive quality to deliver a scalable and strong solution for intelligent agile project management. Future work will focus on integrating eXplainable AI (XAI) techniques and validating the model across real world industrial datasets to further enhance transparency and generalizability.
18 July 2026
Read appraisal →Journal of behavioral addictions
Problematic internet use and borderline personality features: A systematic review informed by the alternative model of personality disorders.
BACKGROUND AND AIMS: Associations between problematic internet use (PIU) and borderline personality disorder (BPD) have been reported, but the literature is highly heterogeneous, with some studies assessing overall BPD severity and others examining individual traits (e.g., emotional lability, impulsivity, dissociation). This review synthesizes these approaches using the Alternative Model of Personality Disorders (AMPD), examining how BPD-relevant pathological personality traits (Criterion B) and impairments in self and interpersonal functioning (Criterion A) relate to PIU. METHODS: Following PRISMA 2020 guidelines, we searched PsycINFO, Web of Science, PubMed, Embase, and Scopus for studies examining associations between PIU (e.g., general use, social media, gaming, smartphone use) and personality traits or functioning in adults. Study quality was assessed using the NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. RESULTS: Thirty studies (N = 21,358) met inclusion criteria. Emotional lability and impulsivity showed the most consistent associations with PIU across outcome types. Few studies examined impairments in personality functioning, but available evidence suggests that identity disturbance may predict PIU over time and that interpersonal dysfunction is associated with greater use of online environments for reassurance and emotion regulation. Associations involving dissociation and detachment were relatively weak and less consistent. DISCUSSION AND CONCLUSIONS: Studies varied widely in design, measures, and theoretical approach, limiting direct comparison across studies. Despite this heterogeneity, findings indicate that emotional lability, impulsivity, and identity disturbance appear to represent transdiagnostic vulnerabilities that increase risk for PIU and shape patterns of online engagement.
18 July 2026
Read appraisal →JMIR nursing
Effectiveness of Artificial Intelligence-Based Nursing Interventions for Chronic Illness Care: Umbrella Review
BACKGROUND: Artificial intelligence (AI)-based nursing interventions are increasingly being used to manage chronic illnesses; however, their definitive impact on clinical outcomes remains inconclusive, necessitating a comprehensive evidence synthesis. OBJECTIVE: This umbrella review aimed to synthesize the evidence regarding the effectiveness of AI-based nursing interventions for chronic illness care and their subsequent impact on health care outcomes in clinical settings. METHODS: We conducted an umbrella review and prospectively registered the protocol. A systematic search of 5 electronic databases (PubMed, CINAHL, Cochrane Library, Scopus, and Web of Science) was performed to identify systematic reviews and meta-analyses published in English between 2021 and 2025. The methodological quality of the included studies was evaluated using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist. RESULTS: Eight high-quality systematic reviews were included, with machine learning identified as the predominant technology. Three primary outcome domains emerged: predictive, psychosocial, and hospital utilization. Due to measurement heterogeneity, the results were synthesized narratively. Our findings demonstrated that AI-based nursing interventions are effective in predicting adverse clinical events, unplanned hospital utilization, and health care costs. However, evidence regarding psychosocial outcomes remains insufficient. CONCLUSIONS: This review provides systematic evidence supporting the utility of AI in chronic illness management, particularly for improving predictive and utilization outcomes. These findings offer actionable insights for nursing leaders to integrate AI into clinical practice and education. Future research should prioritize rigorous empirical designs to further strengthen the evidence base for AI-driven nursing care.
17 July 2026
Read appraisal →International journal of medical informatics
A scoping review on conversational AI in mental health: A human-centered perspective.
BACKGROUND: Conversational AI offers scalable mental health support, with large language models (LLMs) enabling personalized interactions. Human-centered design is critical in this domain, yet a comprehensive synthesis from this perspective is lacking. This review maps conversational AI research in mental health across the patient journey and develops a human-centered taxonomy to guide future design. METHODS: Following PRISMA guidelines, we conducted a comprehensive search across fifteen multidisciplinary databases. We systematically analyzed the literature across six dimensions: research foci, mental disorder types, target populations, AI technologies, data sources, and evaluation metrics. A consensual taxonomy research method was employed to develop a human-centered design framework. RESULTS: Of 10,293 identified records, 677 studies met the inclusion criteria. Analysis reveals a marked increase in publications since 2020, predominantly from computer science (449 studies), followed by medicine (148) and social sciences (80). Research is skewed toward detection (23%) and intervention (66%) stages, with prevention (8%) and maintenance (3%) receiving less attention. Mood, anxiety, and stress-related disorders are the most investigated conditions. LLMs have emerged as the predominant AI technology, particularly within intervention and maintenance stages. Data sources continue to rely heavily on text-based inputs, with multimodal approaches still limited in adoption. Evaluation metrics vary significantly by discipline, reflecting limited cross-disciplinary integration. Through thematic synthesis, we developed a human-centered taxonomy comprising four primary dimensions: Emotional Sensitivity to Users, User-Centric Interaction Design, Human-AI Collaboration and Capability Enhancement, and Ethics and Accountability, with a total of thirteen sub-dimensions. CONCLUSIONS: This review provides a comprehensive, human-centered mapping of conversational AI research in mental health across the patient journey. Critical gaps remain in stage coverage, disorder diversity, population inclusivity, multimodal data integration, and interdisciplinary evaluation. The proposed taxonomy offers a structured framework to align AI development with human-centered principles, fostering empathetic, ethical, effective, and equitable mental health support.
16 July 2026
Read appraisal →Journal of affective disorders
Ecological momentary interventions for depression: A systematic review and meta-analysis
INTRODUCTION: Depression remains a prevalent condition with many individuals experiencing residual symptoms despite standard treatments. Ecological momentary interventions (EMIs) offer real-time, ecologically-valid microinterventions, yet their clinical utility and implementation challenges in depression remain underexplored. METHOD: A systematic search of six databases (inception to 25 July 2025) identified randomised controlled trials (RCTs) evaluating EMIs for depression. Pooled effect sizes assessed differences relative to comparator conditions. RESULTS: Sixteen RCTs involving 1258 participants were included. EMIs significantly reduced depressive symptoms at posttest (Hedges' g = 0.57; 95% CI = 0.33-0.80; p < 0.001) and at follow-up ranging from 10 to 32 weeks (Hedges' g = 0.43). High heterogeneity reflected variability in EMI formats. EMIs also showed a statistically significant improvement in quality of life (Hedges' g = 0.44; 95% CI = 0.17-0.71; p < 0.01), supporting their broader impact beyond symptom reduction. Engagement averaged 64.91% (SD = 17.18), ranging 37.5%- 88.0%, with variation across reporting methods. Acceptability and usability ratings were consistently high. Intervention dosage and risk of bias were significant moderators of treatment efficacy. Subgroup analyses revealed greater benefits from EMIs incorporating psychoeducation and active practice, and fully-automated formats. CONCLUSION: The review highlights EMI efficacy in reducing depressive symptoms and improving quality of life, with intervention dosage and methodological rigor emerging as key moderating factors. Reporting engagement remains a challenge, and substantial heterogeneity underscores the need for refined trial designs to isolate the active EMI components to best optimise treatment outcomes in depression. Future studies should prioritise strategies strengthen methodological rigor and optimising user engagement. PROSPERO No. CRD420251105663.
16 July 2026
Read appraisal →PloS one
A dual-stream deep learning architecture for business impact scoring and alert escalation
Modern network monitoring systems generate massive volumes of telemetry data, yet most existing anomaly detection models fail to prioritize alerts according to their operational urgency and business impact. This limitation results in delayed incident responses and inefficient alert management in Network Operations Centers. To address this gap, this study proposes a Dual-Stream Predictive Alert Escalation Framework that integrates temporal failure pattern learning with business impact-aware alert prioritization. The proposed architecture consists of two key components: a bidirectional temporal encoder for modeling multivariate Key Performance Indicator (KPI) time-series data, and an auxiliary severity encoder that captures contextual metadata related to operational risk and service criticality. The outputs of these two learning streams are combined through an attention-based fusion mechanism, and a Business Impact Scoring (BIS) layer generates impact-weighted escalation decisions for proactive incident management. Experimental evaluations using real-world KPI datasets and the AI4I_2020 predictive maintenance dataset demonstrate the superior performance of the proposed framework compared to baseline methods such as LSTM, GRU, CNN-LSTM, and BiLSTM-VAE. On the combined multivariate KPI dataset, the model achieved a precision of 0.94, a recall of 0.92, and an F1-score of 0.93, along with a PR-AUC of 0.95 and a ROC-AUC of 0.94. Under impact-aware evaluation, the framework attained the highest Impact-Weighted F1 (IW-F1) of 0.85 and BIS accuracy of 0.88, resulting in an estimated 31.6% reduction in operational costs through earlier and more accurate escalation of critical events. The suitability of the selected datasets is justified by their complementary roles: publicly available KPI time-series datasets represent real-world network telemetry behavior, while the AI4I_2020 dataset provides structured severity and operational context, enabling joint evaluation of failure prediction accuracy, escalation timeliness, and business impact modeling. By prioritizing alerts based on impact-aware severity rather than raw anomaly scores, the proposed framework directly supports operational cost reduction through earlier mitigation and improved decision-making in network operations. The proposed approach bridges the gap between anomaly detection and intelligent alert management by incorporating business relevance into predictive modeling. This dual-stream architecture offers a scalable and proactive solution for AIOps-driven network reliability and automated service resilience.
14 July 2026
Read appraisal →BMC medical research methodology
Digital focus groups in healthcare interventions: A reflexive methodological analysis of how socio-technical and relational factors co-produce qualitative data.
BACKGROUND: Digital focus groups are increasingly used in health research, yet their methodological implications within complex interventions remain insufficiently examined. When conducted with pre-existing cohorts, digital focus groups may function not only as evaluative tools but also as relational and socio-technical encounters that shape the generation of qualitative data. The goal of the present study was to examine how digital focus groups operate within this context. METHODS: This study presents a methodological re-analysis of focus group material originally collected for the evaluation of a hybrid psychosocial prevention intervention, examining how digital focus groups operate within this context following the perspective of Situational Analysis. Digital focus groups were conducted with participants who had completed the hybrid intervention together in fixed cohort groups, meaning participants were already acquainted with one another prior to the focus group sessions. For the analysis, the transcripts were examined using descriptive, inductive coding. Verbatim quotations were used as methodological markers to illustrate how relational, emotional and technological factors shaped the data. RESULTS: Pre-existing cohort cohesion strongly influenced how participants engaged in the digital setting, fostering emotional safety, peer-supported learning, motivational reinforcement and opportunities for self-regulation. Digital platforms structured visibility, turn-taking and synchronization, while differences in devices, connectivity and digital literacy affected participation and the stability of group interaction. The researcher role expanded to include facilitation, technical troubleshooting and emotional anchoring, making the research team part of the socio-technical infrastructure. At times, the focus groups elicited supportive or motivational effects that echoed elements of the intervention itself, blurring the line between evaluation and unintended interventional effects. CONCLUSIONS: Focus groups in intervention research operate as relational encounters rather than neutral data-collection tools. This applies particularly to Digital Focus Groups. Recognizing how pre-existing relationships, technological environments and researcher involvement shape interaction is essential for methodological transparency and rigorous interpretation. The findings highlight the importance of reflexivity, attention to digital differences, and awareness of unintended interventional dynamics when designing and interpreting digitally mediated qualitative methods in healthcare contexts. Furthermore, the findings indicate that the choice between digital and in-person FGs should not be guided by pragmatic considerations alone, but by the relational, technological and institutional conditions of the research situation. TRIAL REGISTRATION: DRKS-ID: DRKS00033080 (registration date: December 7, 2023).
13 July 2026
Read appraisal →JMIR mental health
Digital Mental Health Research Priorities, Revisited for the AI and Large Language Model Era
Digital mental health has become an established part of mental health care, but the rapid arrival of large language models and other artificial intelligence (AI) tools has refocused attention on the evidence needed to guide the field. This editorial updates the research priorities articulated by JMIR Mental Health in 2023, while reaffirming their emphasis on equity, replicability, privacy, efficacy, and engagement. While the importance of these priorities has not changed in recent years, the urgency with which they must now be applied has. As digital tools become more clinically consequential, research must move beyond demonstrating that a technology is feasible, usable, or novel. The field now needs studies that clarify how these tools work, for whom they are beneficial, under what conditions they may cause harm, and how they can be ethically integrated into care. We call for research that is transparent about the technologies being studied, grounded in meaningful clinical questions, attentive to safety, and designed to produce knowledge that remains useful as specific products and models change.
13 July 2026
Read appraisal →PloS one
Self-positioning in space science communication: A corpus-assisted discourse study
This study incorporates text mining into critical discourse analysis to examine how government science agencies in China and the United States position themselves in space science communication on social media. Two specialized corpora have been built by collecting posts from government science agencies on Weibo from China and X (formerly Twitter) from the U.S. With the help of the text mining tool KH Coder, this study gives a corpus-assisted discourse analysis of the particular ways of positioning at different levels of discourse: (1) topics/themes, (2) addressing terms, and (3) those words that co-occur with self-addressing terms in a sentence. The findings reveal significant differences in their preferential ways of positioning. Chinese government science agencies present themselves as state-affiliated yet approachable institutions, blending achievements and operational efficiency with patriotism and collective pride. Their use of diverse addressing terms and co-occurrence patterns portrays them as experienced, supportive guides, balancing national identity with interpersonal closeness. In contrast, U.S. government science agencies emphasize professionalism, focusing on research, space exploration, mission execution, and audience engagement, with minimal reference to state affiliation. Their addressing terms are formal and standardized, with pride centred on mission success and discovery, highlighting expertise and scientific leadership. Their preferential ways of positioning are further explained in their respective contexts in order to present a proper understanding of these differences.
12 July 2026
Read appraisal →Journal of medical Internet research
Evolution of Regional Information Infrastructures Integrating Health and Social Care in Scotland: Qualitative Study
BACKGROUND: Expectations of integrating health and social care providers have driven the development of digital solutions aimed at overcoming interoperability challenges and ensuring access to information needed for integrated care across fragmented services. However, challenges persist in aligning diverse coding practices, heterogeneous data-sharing mechanisms, and stakeholder needs. OBJECTIVE: We examine how expectations of interoperability and integrated care have shaped the growth of regional information infrastructures in Scotland, using the Key Information Summary (KIS), a summary record that shares key patient information from general practitioner records with out-of-hour services, ambulance services, hospitals, social workers, and caregivers across multiple care settings, as a case study. METHODS: This qualitative study examined the development, implementation, and adoption of KIS in Lothian, Scotland, across health and care settings, where it has been in use for 13 years. Multisited ethnography was used to understand how technology design, implementation, and adoption were shaped by social, organizational, cultural, and political factors. Data were collected through interviews with users, vendors, and implementers; observations of technology use and multidisciplinary team meetings; and documentary analysis of policies, user guides, and internal reports. A hybrid analytical approach was applied: the Technology, People, Organization, and Macroenvironment framework guided initial coding, while the sociology of expectations and information infrastructure theory were used inductively to trace evolving visions of integration, and the long-term development of regional information infrastructure. RESULTS: Data included 54 qualitative interviews, 20 hours of observation, and 59 documents collected between April 2024 and March 2025. Findings illustrate how information infrastructures for integrating health and care providers evolved through successive concerted efforts, conceptualized as waves. Three waves were identified, each characterized by attempts to interlink disparate information systems used by various health and care providers. The first wave focused on linking health care providers by developing networks and architectures required for sharing clinical information, which later supported the development and sharing of KIS. Subsequent waves sought to interlink information systems used by health care providers with those used by local authorities and social care providers. In the absence of shared data standards across these sectors, interoperability was achieved by extending the existing health care-centric infrastructure to different social care settings through workarounds such as providing proxy access to hospital systems and secure emailing networks. CONCLUSIONS: This work illustrates how regional information infrastructures for integrated care evolve through orchestrated waves of change. Some expectations for change required coordinated, system-level action, such as setting up standards, networks, and architecture, while others were realized through local adaptations. Integrating health and care providers through digitalization is a long-term process requiring sustained coordination, with progress often occurring through incremental, local extensions. Policies must support adaptive, long-term coordination, balancing system-level initiatives with local adaptations to achieve meaningful integration.
12 July 2026
Read appraisal →BMJ open
Pedagogical strategies for enhancing critical thinking in nursing students using large language models: a mixed-methods systematic review protocol.
INTRODUCTION: Large language models (LLMs) are rapidly normalising in nursing education. However, evidence regarding their impact on critical thinking-a cornerstone of safe clinical practice-remains inconsistent. Current syntheses have largely focused on student satisfaction and general utility, leaving the specific instructional mechanisms that cultivate this competency unexplored. This mixed-methods systematic review aims to identify effective pedagogical strategies for fostering critical thinking, providing evidence-based guidance to safeguard clinical competency in an artificial intelligence-integrated workforce. METHODS AND ANALYSIS: This review will follow a convergent segregated mixed-methods design in accordance with the Joanna Briggs Institute (JBI) methodology. A systematic search will be conducted across CINAHL, ERIC, MEDLINE, PsycINFO and Scopus supplemented by grey literature sources (ProQuest Dissertations, MedRxiv). The search will be limited to English-language studies published from January 2023 to June 2026. Eligible studies will include undergraduate nursing students exposed to structured LLM-based pedagogical interventions. Methodological quality will be appraised using design-specific JBI Critical Appraisal Checklists. Outcomes will be classified using a modified Kirkpatrick framework to distinguish objective competence from subjective confidence. Quantitative data will be synthesised narratively following Synthesis Without Meta-analysis guidelines, and qualitative data thematically. Streams will be integrated using a Joint Display Analysis to generate meta-inferences on effective instructional design. Finally, confidence in the cumulative evidence will be assessed using Grading of Recommendations Assessment, Development and Evaluation (GRADE) and GRADE-Confidence in the Evidence from Reviews of Qualitative research. ETHICS AND DISSEMINATION: As a systematic review of existing literature, this study does not require ethical approval. Findings will be disseminated through a peer-reviewed publication, conference presentations and a visual guide for educators.
11 July 2026
Read appraisal →Current treatment options in oncology
Telemedicine and Virtual Reality Technologies in Pain Management for Cancer Patients: A Structured Narrative Review
Cancer-related pain remains a complex and persistent clinical challenge due to its multidimensional nature and the limitations of pharmacological treatments alone. In our view, telemedicine and virtual reality (VR) technologies represent complementary and increasingly relevant approaches that address different yet interconnected dimensions of pain management in oncology care. Telemedicine should be considered a key component of contemporary cancer care, particularly for continuous symptom monitoring, early detection of pain exacerbation, and timely clinical intervention. Its ability to enhance communication between patients and healthcare providers and to support continuity of care makes it especially valuable in the long-term management of cancer-related symptoms. In contrast, VR interventions offer a targeted, non-pharmacological strategy for modulating pain perception through attentional distraction, emotional regulation, and immersive engagement. Based on current evidence, VR appears particularly useful in the management of procedure-related pain and in patients experiencing persistent pain that is insufficiently controlled with standard approaches. We propose that the integration of telemedicine and VR should be considered within a coordinated, patient-centered care model, where telemedicine facilitates ongoing assessment and identification of patient needs, and VR is applied as an adjunctive intervention tailored to individual symptom profiles. Such an approach has the potential to enhance both symptom control and psychological well-being while supporting treatment adherence. However, despite their promise, the implementation of these technologies requires careful consideration of clinical context, patient characteristics, and system-level factors, including digital literacy and technological infrastructure. Future research should prioritize the development of standardized protocols and the evaluation of long-term outcomes to support the sustainable integration of these digital health strategies into routine oncology practice.
11 July 2026
Read appraisal →PloS one
Beyond numbers: The governance role of data assets in real earnings management.
While data assets are increasingly recognized as strategic resources in the digital economy, their implications for financial reporting quality remain underexplored. Employing a textual analysis approach based on deep learning techniques to construct a measure of data assets, this study investigates their impact on real earnings management (REM) using a sample of Chinese A-share listed firms from 2010 to 2023. We document a negative association between data assets and REM. Our findings remain robust after addressing endogeneity concerns using instrumental variables based on regional digital infrastructure and propensity score matching. Mechanism analyses reveal that data assets mitigate REM through two distinct channels: curbing managerial short-termism and strengthening external monitoring via increased analyst coverage. Cross-sectional tests indicate that this governance effect is more pronounced among firms facing earnings pressure. Furthermore, data assets primarily constrain cost-side manipulation strategies, specifically abnormal production costs and discretionary expenditures. Finally, we show that the reduction in REM driven by data assets contributes to enhanced long-term firm value. Collectively, these findings uncover a novel governance effect of data assets, suggesting that digital infrastructure investments yield positive externalities for corporate financial behavior.
9 July 2026
Read appraisal →JMIR formative research
Exploring Informal Caregivers' Perception of the Olera Digital Caregiving Assistance Platform for Dementia Care: Mixed Methods Evaluation Study
BACKGROUND: Informal caregivers of people living with dementia often experience high rates of caregiver burnout while providing care. Although there are many websites and mobile apps available to help caregivers, many do not use digital tools. The Olera platform was developed to be an easily adoptable web-based support tool, connecting caregivers with long-term services and supports, financial assistance, and educational resources. The platform was developed based on the Build-Measure-Learn framework with input from caregiver needs assessments and usability studies. OBJECTIVE: This study aims to evaluate the quantitative and qualitative feedback of informal caregivers of people living with dementia on the second iteration of the Olera platform. The primary objective was to assess caregivers' acceptance of this caregiving platform. The secondary objective was to use qualitative methods to explore (1) the study cohort's challenges in daily caregiving to determine and compare them with prior literature, (2) their experience when using the Olera platform, and (3) their attitudes toward integrating artificial intelligence in caregiver services for future studies and platform development. METHODS: Caregivers were recruited through various sources and screened for eligibility through an initial survey. Participants used the platform for 4 weeks and completed a survey with an adapted Technology Acceptance Survey (TAS) and qualitative open-ended questions at the end of the testing period. TAS responses were summarized with descriptive statistics, while ANOVAs, t tests, and linear regressions were used to compare the differences in the overall TAS scores by caregiver characteristics. Qualitative feedback data on the platform's usefulness were analyzed via a thematic analysis framework approach. RESULTS: A total of 65 caregivers in the United States completed the study, with a mean age of 59.9 (SD 9.8) years. The majority were female (61/65, 95.3%), non-Hispanic or Latino White (45/65, 69.2%), and the adult child of their care recipient (42/65, 64.6%). Evaluation of the Olera platform showed a high acceptance rate, with each TAS item scoring above 5.0 and an overall TAS score of 5.83 (SD 0.85) out of 7. Higher platform use frequency was associated with higher TAS ratings in technology acceptance (F3,61=7.88, P<.001). Thematic analyses elicited the caregiving challenges, evaluation of the Olera platform, and feedback on artificial intelligence-assisted support. CONCLUSIONS: The Olera platform is an example of a beneficial web-based tool, though key features were requested to be included in the next iteration. Additionally, data supported prior findings regarding informal caregiver challenges and the insufficiency of conventional support mechanisms, indicating a need for more innovative digital solutions. Future research and development efforts using the Build-Measure-Learn approach are necessary to further iterate the platform's key features, enhance the tool, involve more informal caregivers in its improvements, and serve as a model for customizable, person-centered online care support.
6 July 2026
Read appraisal →Journal of medical Internet research
Are Traditional Registries Becoming Obsolete in the Modern Digital Health Ecosystem?
Registries have long been a cornerstone of medical research and public health, providing systematically collected data on diseases, treatments, and health outcomes. However, in the era of digital health, we argue that the traditional model of stand-alone registries needs reconsideration, given the context of increasingly digitized and interoperable health data ecosystems. Unless registries evolve to embrace embedded, standards-based data services, operating across interoperable infrastructure, they will become obsolete while digitalization is reshaping how data can be collected, shared, and used. In this viewpoint, we recount how the present health data ecosystem came to be and what role registries have come to play therein. Following that, we show how recent regulatory initiatives such as the Trusted Exchange Framework and Common Agreement in the United States or the European Health Data Space Regulation signal a shift toward cross-network health information exchange, promoting patient-centric data integration within electronic health record systems. We further illustrate how electronic health records are consequently set to evolve into information hubs, acting as the primary gateway for individuals through which they may access and control their personal health data spread throughout increasingly connected health data ecosystems. This, in turn, might stimulate the creation of digital twins and continuous learning health systems in practice. Following this line of thought, we discuss the opportunities and challenges of interconnected health data ecosystems. Ultimately, we propose that next-generation registries need to be designed as dynamic, service-oriented software stacks for research, leveraging the common data infrastructures that are currently being established around the world. Given the points raised in this viewpoint, we invite health care professionals and researchers alike to equally rethink the role that registries should play within the globally emerging interconnected health data ecosystems and contribute their findings. References included in this viewpoint were identified through searches of PubMed and Google Scholar with various search terms and combinations thereof pertinent to the topics touched on, for example, "patient registry," "clinical registry," "digital twin," "healthcare," "clinical research," "virtual twin," "TEFCA," or "EHDS." Only papers in English were reviewed. The final reference list was generated on the basis of originality and relevance to the broad scope of topics covered in this viewpoint, aiming to present a balanced overview of topic-related findings and arguments.
5 July 2026
Read appraisal →JMIR formative research
Knowledge, Attitudes, and Practices Related to AI in Learning and Research Among Medical Students in Vietnam: Cross-Sectional Study
BACKGROUND: In recent years, artificial intelligence (AI) has ushered in a promising era in medicine, particularly in medical education. However, studies assessing the knowledge, attitudes, and practices related to AI among medical students in Vietnam remain limited. OBJECTIVE: This study aimed to evaluate AI knowledge, attitudes, and practices among Vietnamese medical students in learning and research, and to identify factors associated with their AI practices. METHODS: A cross-sectional study was conducted among medical students at Thai Binh University of Medicine and Pharmacy from November to December 2025. Data were collected using an online structured questionnaire covering demographic characteristics and AI knowledge, attitudes, and practices. The main outcome of interest was AI practices in learning and research. Descriptive statistics and multivariable linear regression were used to examine associated factors. Regression coefficients (β), 95% CIs, and P values are reported. RESULTS: A total of 1002 medical students (mean age 21.00, IQR 19.00-23.00 years; n=596, 59.5% female) were included. The median percentage of maximum possible (POMP) score of AI knowledge was 66.67 (IQR 33.33-83.33), with a high level of familiarity with common tools (n=798, 79.6%). AI attitudes were generally positive (median POMP score 70.00, IQR 53.33-76.67). AI-related practices were lower (median POMP score 50.00, IQR 46.88-71.88), with AI being used primarily for information retrieval and literature research support. In the multivariable analysis, knowledge POMP score (β=0.12, 95% CI 0.08-0.16) and attitudes POMP score (β=0.42, 95% CI 0.34-0.51) were significantly associated with AI practices POMP score (P<.001). Age, gender, major, grade point average classification, and having participated in an AI seminar or training were not associated with AI practices. CONCLUSIONS: Medical students showed favorable knowledge and positive attitudes, but their AI practices remained limited. Integrating AI into medical curricula, including fundamentals, applications, and ethical aspects, is essential to prepare future physicians for AI-driven health care.
5 July 2026
Read appraisal →Computers, informatics, nursing : CIN
Bridging the Gap Between Potential and Practice: An Integrative Review of Generative Artificial Intelligence in Nursing
BACKGROUND: Generative artificial intelligence (GAI) has emerged as a transformative tool in health care, particularly with the integration of large language models (LLMs) into electronic health record (EHR) systems. These tools have the potential to enhance clinical decision-making, increase efficiencies, and decrease time in the EHR. However, despite expanding availability, adoption in nursing practice remains limited. This integrative review aimed to synthesize the literature and explore barriers related to the integration of GAI tools into nursing practice. METHODS: We conducted an integrative search of peer-reviewed literature published between November 2022 and July 2025 to examine the current evidence while assessing for methodological quality. The retrieved articles were screened for title, abstract, and full text eligibility. We synthesized themes related to GAI adoption by nurses, focusing on ethical, educational, and workflow-related factors that influence use. RESULTS: A total of 10 studies met criteria including 7 qualitative descriptive design, 2 case reports, and 1 experimental clinical trial. Four key barriers emerged: educational gaps, ethical concerns, data transparency issues, and workflow misalignment contributing to nurses' hesitancy to engage with GAI tools. Recommendations include involvement of nurses in the design, implementation, and evaluation of GAI tools and mandatory AI curricula in nursing education. CONCLUSIONS: Overcoming barriers requires nurse involvement in GAI design efforts, targeted education, and governance models that foster trust and usability. Nurse-centered integration of GAI tools has the potential to advance workforce efficiencies while preserving patient safety.
4 July 2026
Read appraisal →Journal of biomedical informatics
A comprehensive survey of AI agents in healthcare
OBJECTIVE: This survey aims to systematically map the rapidly evolving landscape of AI agents in healthcare. It addresses the critical need to adapt general-purpose agentic frameworks characterized by autonomy, planning, and tool use to the high-stakes, safety-critical constraints of medical decision-making and patient care. METHODS: We conducted a comprehensive review of over 200 recent studies, synthesizing literature from major academic databases. We developed a holistic taxonomy that traces the full lifecycle of healthcare agents, analyzing perception modalities, core technical architectures, and evaluation protocols specific to autonomous systems. RESULTS: The review presents a quantitative landscape analysis showing exponential growth in the field. We structure the domain into three pillars: (1) Perception of multi-modal clinical data (e.g., EHR, imaging, genomics); (2) Agent Capabilities, including tool use, reasoning, memory, and multi-agent collaboration; and (3) an Application Ecosystem organized by stakeholder roles (clinicians, patients, researchers, and administrators). Additionally, we categorize evaluation frameworks, and discuss the deployment readiness of current systems across technical, evidentiary, and governance dimensions. Finally, we identify challenges for advancing healthcare agents from controlled evaluation toward real-world clinical integration. A continuously updated repository of related papers is available at https://github.com/AgenticHealthAI/Awesome-AI-Agents-for-Healthcare. CONCLUSION: AI agents offer significant potential to enhance healthcare through autonomous reasoning and workflow integration. However, current research remains largely concentrated in benchmark and controlled evaluation settings, and the translation into clinical practice will require advances in reliability, privacy protection, governance, and operational integration.
4 July 2026
Read appraisal →Neuroscience and biobehavioral reviews
Anxiety detection using neural and physiological signals and artificial intelligence: A comprehensive review.
Anxiety disorders are a significant challenge to global health, yet current diagnostic methods rely mainly on subjective and episodic assessment. The application of artificial intelligence (AI) in combination with neural and physiological signals is a promising pathway toward objective and continuous monitoring. This review provides a comprehensive evaluation of machine learning (ML) and deep learning (DL) techniques applied to electroencephalography (EEG), electrocardiography (ECG), Photoplethysmography (PPG), and electrodermal activity (EDA) for anxiety detection. A systematic review of the literature covering the period 2015-2025 indicates that the field has evolved from traditional ML systems based on hand-crafted features to modern end-to-end deep learning schemes. Our review indicates that while classical models remain effective, hybrid models such as CNN-LSTM and more sophisticated architectures, like Transformers, consistently deliver state-of-the-art results, particularly in multimodal data integration scenarios. Despite the impressive accuracies reported, a critical examination identifies several key challenges including reliance on narrow, laboratory-trained datasets, a lack of standardized validation procedures, and limited transparency in complex models, collectively impeding clinical translation. Progress in this field requires the creation of large-scale, clinically-validated datasets; the development of fault-tolerant, generalizable, and interpretable schemes; and a transition from basic state classification towards longitudinal, personalized monitoring in support of just-in-time intervention. As a comprehensive and up-to-date narrative synthesis of AI-driven anxiety detection using neural and physiological signals, this review consolidates a fragmented literature, articulates the translational gaps limiting real-world deployment, and establishes a clear research agenda for the development of clinically impactful digital mental health technologies.
3 July 2026
Read appraisal →JMIR mental health
From Alliance to Nexus: Rethinking Digital Therapeutic Relationships
In traditional human psychotherapy, the therapeutic alliance (TA) is regarded as a fundamental factor that describes the client-therapist relationship, mainly due to strong evidence demonstrating its impact on treatment outcomes regardless of theoretical orientation. More recently, advances in artificial intelligence (AI) and other technologies have led to the emergence of the concept of digital TA, used to characterize the relationship between clients and AI-based therapeutic systems. This approach replicates human dynamics but overlooks key differences between human therapists and digital agents. Prematurely translating the concept of TA into the digital context fails to address issues such as the sycophantic tendencies of current systems and the inherent limitations of algorithmic interaction. We propose the digital therapeutic nexus, a framework that recognizes these differences and provides a set of structured criteria for categorizing digital interactions into 3 progressive levels. This Viewpoint argues that only at the highest level can parallels be drawn to the human TA and stratifies the main risks associated with each nexus level. Transitioning from the concept of alliance to that of a nexus offers a more precise conceptual basis for describing and evaluating digital therapeutic relationships, with implications for research, design, and the ethical development of AI-based mental health interventions.
3 July 2026
Read appraisal →The Journal of international medical research
Examining the state of telehealth for mental health and substance use care after the coronavirus disease 2019 pandemic: An integrative review.
ObjectiveThe objective of this integrative review was to synthesize literature and provide implications for clinical practice on telehealth use among patients with serious mental illness and substance use disorders following the coronavirus disease 2019 pandemic.MethodsAn integrative review guided by Socio-Technical Systems Theory was applied. The PubMed, Cumulative Index to Nursing and Allied Health Literature, PsycINFO, Medline, Academic Search Complete, and Gale Health and Wellness databases were searched for publications from 1 January 2019 to 1 December 2024. Articles selected according to the established inclusion and exclusion criteria were evaluated using a rapid critical appraisal checklist developed by Fineout-Overholt and Melnyk.ResultsAmong the 172 articles reviewed, 16 peer-reviewed and 2 government publications were included. Four themes were identified: (a) treatment adherence; (b) satisfaction reported by patients and providers; (c) telehealth policy developments; and (d) access to services. Telehealth supported continuity of care, improved satisfaction, and improved access. Technological and financial barriers restricted equitable access. Policy changes enabled broader adoption; however, regulatory approaches varied across jurisdictions.ConclusionTelehealth remains an integral method for delivering mental health and substance use care following the coronavirus disease 2019 pandemic. Greater focus is needed on long-term effectiveness and limitations of telehealth.
3 July 2026
Read appraisal →Journal of medical Internet research
Transformation Versus Innovation in Digital Health Care and the Future of Clinical AI
Digital innovation is frequently presented as the key to transforming health care. In this News and Perspectives article, JMIR Correspondent and academic physician Boon-How Chew reports on the distinction between and the direction of transformation and innovation, reflecting on the future of clinical AI and what lasting change in health care ultimately requires.
3 July 2026
Read appraisal →Psychiatric services (Washington, D.C.)
Psychiatric Risk Associated With Large Language Model Chatbots: An Emerging Concern
Large language model chatbots such as ChatGPT have been adopted by millions of users, yet their psychiatric implications are poorly understood. Recent media reports describe 26 apparent instances of psychosis and two instances of depression and suicide in the context of heavy chatbot use, often marked by delusions seemingly reinforced by the model. Because chatbots simulate conversation but are statistical systems optimized for agreement, they may amplify distorted beliefs. Research is needed to determine whether these reports represent a genuine pattern of risk and to identify vulnerable populations. Clinicians should screen for chatbot use and discuss these systems with patients.
2 July 2026
Read appraisal →Journal of Alzheimer's disease : JAD
Early diagnosis of Alzheimer's disease through handwriting analysis and deep learning: A review.
Alzheimer's disease (AD) is one of the most prevalent neurodegenerative disorders worldwide, requiring early identification for timely intervention and to slow disease progression. However, existing diagnostic approaches, while effective at later stages, remain limited in detecting early-stage AD. Handwriting analysis has recently emerged as a non-invasive, cost-effective, and ecologically valid digital behavioral biomarker that reflects neurocognitive impairment. This review examines the role of handwriting as a neurocognitive marker for AD, focusing on integrating deep learning methodologies to enhance early diagnostic accuracy. It also elucidates the neurocognitive mechanisms linking handwriting behavior and AD, addressing current methodological and translational challenges. We performed a PRISMA-informed structured literature search and narrative synthesis of handwriting- and drawing-based studies for detecting AD/mild cognitive impairment (MCI), including offline handwriting images and online pen-stroke kinematics captured by digital devices. Task paradigms, data dimensions, preprocessing pipelines, modeling strategies (traditional machine learning and deep learning), evaluation practices, and translational considerations were summarized, and studies were organized by detection purpose and analytic approach. Our findings show that handwriting-based models generally discriminate AD/MCI from healthy controls with accuracy exceeding 80%, while deep learning models (e.g., convolutional neural network and multimodal Transformer fusion) approach 90% in structured tasks like clock drawing and figure copying. Online kinematic markers (e.g., reduced velocity, prolonged in-air time, increased pausing, and pressure instability) recur across studies, and multimodal integration with speech, gait, or facial signals can further improve sensitivity and ecological validity, although most studies are small and single-center.
2 July 2026
Read appraisal →The Orthopedic clinics of North America
Wearable Devices and Artificial Intelligence to Enhance Perioperative Care in Total Knee Arthroplasty
Rapidly evolving wearable devices have the potential to transform health care delivery and enhance outcomes. Applications of wearable smart devices range from activity and motion tracking to real-time monitoring of vital signs and biochemical markers. Recent evidence provides support for the use of wearable devices in chronic disease management, rehabilitation, and perioperative care of patients with total knee arthroplasty. Data derived from these devices may enhance treatment personalization and early identification of complications. Challenges remain regarding accuracy, standardization, privacy, and patient adherence. Future advances must overcome these limitations.
2 July 2026
Read appraisal →Current opinion in psychiatry
Opportunities and risks of large language models in digital interventions for substance use disorders
PURPOSE OF REVIEW: Large language models (LLMs) are increasingly integrated into digital mental health tools, yet their role in substance use disorder (SUD) interventions remains poorly understood. This review synthesizes emerging evidence on the opportunities and risks of applying LLMs across the digital SUD care continuum. RECENT FINDINGS: Studies report promising applications in early detection, personalized support, continuous monitoring, and relapse prevention. LLMs demonstrate capacity to extract substance-use signals from natural language, generate supportive and motivational responses, and interpret narrative data for patient-reported outcomes. However, risks are substantial. LLMs can produce inaccurate or hallucinated content, may reinforce stigma or demographic bias, and can generate misleading or potentially unsafe advice. Privacy concerns are amplified in SUD contexts, where sensitive data are often managed outside regulated healthcare systems. Existing regulatory frameworks such as the EU AI Act or U.S. device regulations, do not yet provide clear governance for anonymous, AI-supported SUD interventions. SUMMARY: LLMs have potential to expand scalable, low-threshold support for SUDs, but their safe deployment requires validation, bias mitigation, transparent data governance, and robust human oversight. Evidence remains preliminary, and clinical integration should proceed cautiously.
2 July 2026
Read appraisal →Journal of biomedical informatics
Clinical document metadata extraction: A scoping review.
OBJECTIVES: Clinical document metadata, such as document type, structure, author role, medical specialty, and encounter setting, is essential for accurate interpretation of information captured in clinical documents. However, vast documentation heterogeneity and drift over time challenge harmonization of document metadata. Automated extraction methods have emerged to coalesce metadata from disparate practices into target schema. This scoping review aims to catalog research on clinical document metadata extraction, identify methodological trends and applications, and highlight gaps warranting further investigation. METHODS: We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines to identify articles from Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science and external sources that perform clinical document metadata extraction, either primarily as a methodology study, secondarily as a feature for a downstream application, or for analysis. We initially identified and screened 342 articles published between 2011 and 2025, then comprehensively reviewed 77 we deemed relevant to our study. RESULTS: Among the 77 articles included in our full text review, 49 were methodological, 22 used document metadata as features in a downstream application, and 6 analyzed document metadata composition. We observe myriad purposes for methodological study and application types. Available labelled public data remains sparse except for structural section datasets. Methods for extracting document metadata have progressed from largely rule-based and traditional machine learning with ample feature engineering to transformer-based architectures with minimal feature engineering. DISCUSSION AND CONCLUSION: Clinical document metadata extraction research has accelerated over recent years. The emergence of large language models has enabled broader exploration of generalizability across tasks and datasets, allowing the possibility of advanced clinical text processing systems. We anticipate that research will continue to expand into richer document metadata representations and integrate further into clinical applications and workflows.
2 July 2026
Read appraisal →IEEE transactions on pattern analysis and machine intelligence
50 Years of Automated Face Recognition
Over the past five decades, automated face recognition (FR) has progressed from handcrafted geometric and statistical approaches to advanced deep learning architectures that now approach, and in many cases exceed, human performance. This paper traces the historical and technological evolution of FR, encompassing early algorithmic paradigms through to contemporary neural systems trained on extensive real and synthetically generated datasets. We examine pivotal innovations that have driven this progression, including advances in dataset construction, loss function formulation, network architecture design, and feature fusion strategies. Furthermore, we analyze the relationship between data scale, diversity, and model generalization, highlighting how dataset expansion correlates with benchmark performance gains. Recent systems have achieved near-perfect large-scale identification accuracy, with the leading algorithm in the latest NIST FRTE 1:N benchmark reporting a False Negative Identification Rate (FNIR) of 0.15 percent at False Positive Identification Rate (FPIR) of 0.001 on a gallery of over 10 million identities. Larger galleries increase false positive rates and deployments at greater scales will see higher error rates. We delineate key open problems and emerging directions, including scalable training, multi-modal fusion, synthetic data, and interpretable recognition frameworks.
1 July 2026
Read appraisal →Journal of bodywork and movement therapies
Wearable technologies in neurorehabilitation and cardiac rehabilitation: A narrative review
BACKGROUND/OBJECTIVE: Wearable devices are sensor-based technologies capable of continuous physiological and movement monitoring, improving health tracking and clinical care. They can play a crucial role in clinical practice including cardiac and neurological rehabilitation, aiding in conditions as stroke, Parkinson and heart failure. These tools enhance treatment adherence and outcomes through remote, continuous, and personalized monitoring. By overcoming barriers of traditional rehabilitation, wearable devices offer cost-effective, accessible, and patient-centered solutions for chronic care. The purpose of this study is to evaluate the state of the art of the use of wearable devices in cardiology and neurology clinical rehabilitation practice. METHODS: A structured literature search was performed in PubMed, Embase, Web of Science, Scopus and Cochrane Library databases, for original studies published between 2014 and 2024 involving adult patients and clinical data. Only original studies with patient intervention or observation were included. RESULTS: Of the 25 studies included, 19 focused on neurology and 6 on cardiology, all exploring wearable devices in adult rehabilitation from 2014 to 2024. Neurological studies targeted conditions like stroke and Parkinson's disease, using robotic aids and virtual reality, while cardiac studies focused on activity monitoring and recovery support. Devices ranged from IMUs and sensor-equipped gloves to wrist-worn ECG monitors, with outcomes assessing motor function, heart rate, and rehabilitation adherence. Patient acceptance was generally positive, particularly in tele-rehabilitation contexts, although usability and long-term adherence were inconsistently reported. CONCLUSIONS: Wearable devices effectively improve neurological and cardiac rehabilitation by supporting personalized, data-driven therapies and remote monitoring, helping to monitor activity and therapeutic adherence. Overall, they promote better outcomes, patient autonomy, and access to care, although there is less evidence. Future studies should further investigate the utility of these devices in different rehabilitation settings.
1 July 2026
Read appraisal →Current psychiatry reports
Is Technology Remaking Therapy: The Screen and the Self: Telepsychiatry, AI Therapy, and the Defense Against Intimacy
PURPOSE OF REVIEW: This paper examines several methods of technology that have challenged traditional expectations of the meaning of psychotherapy, from the widespread adoption of telepsychiatry to the subsequent emergence of AI-driven therapeutic agents (Therabots). RECENT FINDINGS: Widespread usage of new technology that impacts the therapeutic process has outpaced an analysis of how that technology might affect the meaning and effectiveness of that process. Lawsuits assume such technology causes harm, while limited data and the literature has been more mixed. From Frankenstein to CRISPR, new technology always has its cheerleaders and its detractors. The more the technology seems to impact a topic especially connected to our humanity, the deeper the convictions will be on both sides. Certainly, when it comes to psychotherapy, the introduction of new technologies such as telepsychiatry to Therabots has provoked discussion. We argue that while new technologies offer practical advantages, they risk functioning as structural defenses against the vulnerability and authentic intimacy essential to transformative psychotherapy. Through analysis of the therapeutic alliance, relational dynamics, and the psychology of vulnerability, this paper contends that the structural form of telepsychiatry does not alter the inherent nature of the therapeutic experience, whereas AI-mediated therapy may collude with maladaptive defenses, fundamentally altering the nature of the therapeutic encounter.
1 July 2026
Read appraisal →Langmuir : the ACS journal of surfaces and colloids
Wearable Sleep Detection System Based on Piezoelectric Signals and Convolutional Neural Network Analysis
Long-term and comfortable sleep monitoring is a core requirement for daily health management. However, existing sleep monitoring technologies fail to simultaneously achieve accurate sleep stage classification, wearing comfort, and direct monitoring of characteristic physiological signals. Here, we report a wearable sleep monitoring system (SMS) based on piezoelectric sensing and convolutional neural network (CNN) analysis. The system assesses sleep structure and quality by capturing characteristic eye movement signals and provides actionable recommendations for long-term sleep health management. The sensor employs a highly stereoregular polyacrylonitrile (PAN) composite fiber film modified with PDA@ZnO as the sensing unit, providing high sensitivity, excellent wearing comfort, and long-term durability. It conforms closely to the upper eyelid, enabling effective acquisition of direct physiological signals associated with sleep. Furthermore, we developed a CNN model for five-state sleep classification and validated it using original-recording-level 5-fold validation and a 1000-sample processed held-out test set, achieving an accuracy of 95.90% with a Cohen's Kappa coefficient of 0.949. The system efficiently presents sleep quality information through a simple and user-friendly human-computer interaction interface. The proposed system introduces a novel approach to convenient health monitoring and provides a feasible technical solution for accurate and long-term sleep health assessment in daily life.
1 July 2026
Read appraisal →Medical education online
Exploring medical Artificial Intelligence Readiness among Korean medical students: a cross-sectional study
INTRODUCTION: Artificial intelligence (AI) is rapidly transforming healthcare systems and clinical practice, increasing the need to prepare future physicians to work effectively with AI technologies in clinical contexts. Despite growing interest in integrating AI into medical education, empirical evidence regarding medical students' readiness to use AI remains limited. This study aimed to assess medical AI readiness among Korean medical students and explore factors associated with their readiness. METHODS: A cross-sectional survey was conducted among medical students enrolled in a six-year medical program at a university in Korea. A total of 204 students participated in the study. Medical AI readiness was measured using the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS), which assesses four domains: cognition, ability, vision, and ethics. Descriptive statistics, independent t-tests, and Pearson's correlation analyses were performed using SPSS version 27. RESULTS: The overall mean score of medical AI readiness was 4.19 on a 7-point Likert scale, indicating a moderate level of medical AI readiness. Among the subscales, ethics showed the highest mean score (4.69), followed by vision (4.44), ability (4.18), and cognition (3.92). The frequency of AI use was significantly associated with medical AI readiness, whereas the daily duration of AI use was not. No significant gender differences were observed. When students were divided into the low (pre-medical years 1-2) and high (medical years 1-4) groups, the high group showed significantly higher scores only in the ability subscale. CONCLUSIONS: The findings suggest that medical students are not yet sufficiently prepared to utilize AI technologies in clinical practice, particularly in terms of knowledge and practical competencies related to AI. These results highlight the need for structured and longitudinal AI education in medical curricula to better prepare future physicians for AI-integrated healthcare environments.
1 July 2026
Read appraisal →Journal of medical Internet research
A Futures Framework for Clinical AI Governance: Anticipating Emerging Risks, Shifting Roles, and Regulatory Challenges
This viewpoint develops the futures framework for clinical artificial intelligence governance (FF-CAIG), a conceptual and anticipatory framework for organizing emerging governance challenges in clinical artificial intelligence (AI). Although life cycle-oriented oversight is increasingly reflected in clinical AI regulation and institutional governance, existing approaches remain more developed for near-term validation, current-state assurance, and retrospective risk detection than for longer-horizon sociotechnical change. This gap is increasingly relevant, as AI systems become more complex, adaptive, and autonomous, and as they become more deeply embedded in care relationships and accountability structures. FF-CAIG is grounded in 3 futures methodologies: the 3 horizons model, scenario planning, and causal layered analysis. It is operationalized through an emerging clinical AI risk taxonomy that links these methods to governance domains. Its practical outputs include horizon classification, risk-domain mapping, scenario stress-testing findings, accountability-chain mapping, and horizon-scaled minimum governance actions for deployment or continued use. Applied across near-term, transitional, and longer-term horizons, the framework proposes cross-horizon priorities, including stronger predeployment equity evaluation, clearer life cycle accountability, clinician AI oversight competencies, and safeguards for increasingly autonomous or AI-mediated care systems. We illustrate FF-CAIG through 3 representative clinical AI deployment patterns and discuss its limitations, including differential compliance burdens, risks of overdocumentation, variable feasibility across jurisdictions, and the need for empirical validation. FF-CAIG is intended not as a prescriptive policy instrument or validated assessment tool, but as a structured analytic approach for regulators, health system leaders, developers, and researchers seeking prospective and systems-oriented approaches to clinical AI governance.
30 June 2026
Read appraisal →Journal of medical Internet research
Physical Activity Interventions Using Digital Health Interventions for Cancer-Related Fatigue in People With a History of Cancer: Scoping Review
BACKGROUND: Although exercise has been proven effective in alleviating cancer-related fatigue (CRF), traditional face-to-face programs may not be accessible due to physical, temporal, or geographical barriers. Digital health interventions (DHIs) offer scalable alternatives for promoting physical activity; however, evidence synthesizing DHI-based physical activity interventions specifically targeting CRF and their intervention characteristics remains limited. OBJECTIVE: This scoping review aimed to map the types of digital health-based physical activity interventions for managing CRF, to summarize the key characteristics of DHI modalities and CRF outcomes, and to identify knowledge gaps for future research. METHODS: A systematic literature search was conducted across 4 databases (PubMed, EMBASE, Cochrane, and PsycINFO) up to December 2025. Inclusion criteria comprised experimental studies involving adults with a history of cancer, digital exercise interventions, a control group, and fatigue outcomes. Screening and data extraction followed the Joanna Briggs Institute Manual for Evidence Synthesis and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review) standards. The protocol was registered in PROSPERO (International Prospective Register of Systematic Reviews; CRD42022304285). Interventions were classified using the Evidence Standards Framework (ESF). RESULTS: Thirty-three studies comprising 3443 participants were included, representing 32 interventions. Most were randomized controlled trials (n=30, 91%). Interventions were delivered primarily via mobile apps (11/32, 34%) and wearable devices (8/32, 25%), followed by web-based platforms, videoconferencing, exergaming, and augmented reality. Eighteen interventions (reported in 19 studies) demonstrated statistically significant between-group CRF reductions, predominantly at immediate postintervention assessments. Evidence for sustained benefit beyond 12 months was limited, and only one study evaluated ultra-long-term outcomes, which did not demonstrate maintained improvement. Populations with breast cancer accounted for the largest proportion of participants. Fatigue measurement tools varied substantially, potentially contributing to heterogeneity in effect estimates. Most interventions were classified as ESF tier C, indicating a predominant focus on clinical outcome improvement rather than individual-level self-management and system-level implementation. Wearable device-based interventions showed the highest proportion of significant CRF improvement, whereas mobile app-based interventions reported both significant and nonsignificant results. DHIs lasting more than 12 weeks appeared to be associated with more effective CRF outcomes compared to shorter programs. Direct comparisons between in-person and digital delivery were scarce. CONCLUSIONS: Digital health-based physical activity interventions can reduce CRF in people with a history of cancer, with wearable device and longer-duration interventions showing the most favorable outcomes. However, the current evidence is concentrated in populations with breast cancer, and system-level integration remains unexplored. Future research should prioritize diverse populations with cancer, longer follow-up periods, standardized fatigue measurement, and systematic examination of specific intervention components that contribute to CRF reduction. Ultimately, advancing from individual efficacy trials toward scalable, workflow-integrated digital solutions will be key to sustainable CRF management across diverse oncology settings.
29 June 2026
Read appraisal →Journal of medical Internet research
Digital Interventions Targeting Parents to Improve Early Childhood Movement, Nutrition, and Sleep Behaviors: Systematic Review
BACKGROUND: Early childhood (0-5 years) is key for shaping health behaviors, yet optimal behaviors are rarely achieved. Digital health promotion interventions offer scalable support for families; however, most research has focused on childhood more broadly, leaving limited evidence for the early childhood period. OBJECTIVE: The primary aim of this systematic review was to examine whether autonomously delivered digital interventions targeting parents are effective at increasing physical activity, reducing sedentary behavior, improving nutrition (breastfeeding, feeding practices), and/or optimizing sleep among children aged 0-5 years. The secondary aim was to review the reporting of co-design practices, user engagement, and process evaluation, and to assess how engagement influences intervention effectiveness. METHODS: Seven databases were searched for randomized controlled trials (RCTs) evaluating autonomously delivered digital interventions targeting one or more of the following behaviors: physical activity, sedentary behavior, nutrition, or sleep among children (published to January 2026). Study quality was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for RCTs. Findings were narratively synthesized by target age-group and behavior, and the direction of effect was summarized in structured tables. RESULTS: Of the 14,352 identified records, 38 interventions (33 RCTs, 4 pilot RCTs, and 1 feasibility RCT) were included. Most studies focused on pregnancy to infancy (n=24; 0-1 y), followed by preschoolers (n=8; 3-5 y) and toddlers (n=6; 1-2 y). Intervention duration ranged from 2 weeks to 1000 days, and various digital formats were used (apps n=11, SMS text messaging n=10, web- or internet-based platforms n=6, WeChat [Tencent] n=3, tablet-based program n=2, a combination of app and SMS text messaging n=1, website and emails n=1, emails and SMS text messaging n=1, automated voice calls n=1, Facebook Messenger Chatbot [Meta] n=1, and online videos n=1). Interventions spanning pregnancy to infancy reported mixed findings for breastfeeding and feeding practices. Studies targeting toddlers showed improvements in sleep, mixed findings for diet and screen time, and no differences in physical activity. Most studies targeting preschoolers reported significant improvements for feeding practices and diet, but no differences in physical activity, sedentary behavior and sleep, and mixed findings for screen time. Most studies reported co-design or engagement (n=24), but few examined the impact of engagement on intervention effectiveness (n=6), and those that did reported mixed findings. Interpretation was limited by heterogeneous designs, inconsistent outcome measures, and mixed risk-of-bias ratings across studies. CONCLUSIONS: This review advances the field by synthesizing evidence on scalable digital interventions that support parents in promoting healthy lifestyle behaviors across the first 2000 days, together with key design and implementation factors that have rarely been reported in previous reviews. Unlike prior work, it focuses exclusively on autonomously delivered digital interventions in early childhood. Findings show heterogeneous designs and mixed effectiveness, and highlight 3 priority evidence gaps: limited studies in toddlers and preschoolers, incomplete reporting of engagement, and limited understanding of how engagement influences outcomes. These findings define priorities for future research to strengthen the evidence for scalable digital interventions in early childhood.
28 June 2026
Read appraisal →Journal of health organization and management
Cloud-based healthcare architecture for securing and monitoring healthcare data
PURPOSE: A new method known as Lionized Remora optimization based Recurrent Neural Network (LRObRNN) is recommended to enhance the safety of medical information stored on cloud servers to tackle these issues. DESIGN/METHODOLOGY/APPROACH: To safeguard patient data, healthcare organizations must thoughtfully choose reliable and compliant cloud service providers while implementing robust security measures. Storing patient information in cloud systems raises issues with illegal access and data breaches. FINDINGS: The LRObRNN generates a secret key using Lionized Remora optimization and employs cryptography to encrypt sensitive healthcare data. Continuous monitoring ensures the security of data transmission by identifying irregularities. ORIGINALITY/VALUE: Leveraging Recurrent Neural Networks the system analyzes sequential data, enabling the detection of patterns and potential security breaches during data transmission. The performance evaluation includes metrics such as encryption and decryption time, confidentiality rate, processing time, resource usage and efficiency, which are compared with other existing models.
27 June 2026
Read appraisal →PloS one
Transformer-based advertisement image layout generation and object fidelity optimization
The layout of the image and the positional distribution of the objects directly affect the audience's visual focus and the effect of the message conveyed. By setting up a specific layout structure, the viewer's attention can be more focused on the product launched or the moral of the advertisement. Existing work related to position-controllable text-to-image generation has made great progress in generating results on simple images. However, when generating images of complex scenes, the quality is often poor. This can result in the model failing to accurately convey the message of the advertisement when used to generate advertisements. To address these limitations, we propose LAYOBJ-GAN, a novel two-stage framework for layout-controllable advertisement image synthesis. Unlike prior work, our method explicitly models background layouts jointly with object layouts during the text-to-layout generation stage, enabling comprehensive spatial planning of complex scenes. Technically, we introduce a Transformer-based sequence-to-sequence layout generator that learns long-range dependencies between textual descriptions and both background and object regions, which has not been explored in previous advertisement-oriented text-to-image frameworks. In the layout-to-image stage, we further propose a fine-grained text-layout interaction normalization module (TL-Norm) that enables object knowledge transfer from a pre-trained category-to-image model, allowing object appearance to be adaptively modulated by textual context and layout constraints. Extensive experiments on MS-COCO and a high-definition advertisement dataset (AsHQ-10K) demonstrate that LAYOBJ-GAN significantly outperforms seven state-of-the-art methods in image quality, layout controllability, and semantic object accuracy. These results confirm the effectiveness of explicitly modeling background layouts and transferring object-level generative knowledge for complex advertisement image synthesis.
27 June 2026
Read appraisal →JMIR mental health
Coproduction Without Youth? Closing the Participation Gap in Digital Mental Health Research
Young people are among the most intensive users of digital and generative artificial intelligence (GenAI)-enabled mental health tools, yet they remain underrepresented in the research and design processes that shape these technologies. Although participatory approaches such as co-design and patient and public involvement are widely endorsed as best practices, youth involvement in digital youth mental health (DYMH) research is often inconsistent, superficial, or limited to late-stage consultation. This participation gap risks producing interventions that are misaligned with young people's lived experiences, priorities, and vulnerabilities, particularly in the context of rapidly evolving and scalable GenAI systems. This Viewpoint aims to reexamine the underlying drivers of the participation gap in DYMH research; clarify how participation is conceptualized and implemented across disciplines; and propose concrete, actionable recommendations to support more meaningful and consistent youth involvement across the research life cycle. We draw on interdisciplinary literature from digital mental health, human-computer interaction, child-computer interaction, and health research policy. Our Viewpoint integrates conceptual frameworks (eg, Lundy's model of participation), existing reviews of co-design practices, and emerging evidence on GenAI in mental health. We adopt a life cycle-oriented perspective to examine how youth participation is distributed across stages of research and development, including problem formulation, design, implementation, and evaluation. We identify 3 interrelated drivers of the participation gap. First, conceptual and linguistic fragmentation obscures what participation entails in practice, with terms such as co-design, participatory design, user-centered design, and patient and public involvement used inconsistently across disciplines. Second, youth involvement is uneven across the research life cycle, with participation often concentrated in early ideation or usability testing but largely absent from upstream decision-making and downstream evaluation. Third, institutional barriers-including ethics review processes, consent requirements, funding constraints, and adult-centric research norms-systematically limit meaningful youth partnership. These challenges are amplified in the context of GenAI, where opaque "black box" systems, simulated therapeutic interactions, and rapid deployment cycles introduce distinct risks if youth perspectives are not integrated. We propose a set of minimum expectations to address these gaps, including explicit specification of participatory models, life cycle mapping of youth involvement, reporting of youth influence on decisions, dedicated funding for participation, proportional ethics frameworks, and mechanisms for youth-informed governance of GenAI systems. Closing the participation gap in DYMH research is both an ethical imperative and a practical necessity. Moving beyond aspirational commitments requires embedding youth participation as a standard, resourced, and accountable component of research, design, and governance. In the context of rapidly evolving digital and GenAI technologies, failure to do so risks producing interventions that are scalable but not safe, credible, or responsive to the needs of young people.
27 June 2026
Read appraisal →JMIR research protocols
Exploring Non-Embodied AI-Based Digital Companions for Older Adults in Aging and Care Contexts: Protocol for a Scoping Review
BACKGROUND: Conversational artificial intelligence (AI) technologies are increasingly positioned as a response to social isolation, loneliness, and unmet psychosocial needs across health and care contexts. Non-embodied AI-based digital companions have attracted growing attention for their potential to support companionship, social interaction, communication, and psychosocial well-being among older adults, including people living with dementia. However, the evidence base remains underexplored. Terminology is inconsistently applied, systems are variably defined, and studies are distributed across disciplinary silos, limiting critical investigation of how these technologies are conceptualized, designed, and evaluated. OBJECTIVE: This study aims to map and critically synthesize the existing literature on non-embodied AI-based digital companions for older adults in aging, health, and care contexts. The review seeks to describe digital companions, examine methodological approaches, and identify research gaps. METHODS: This protocol follows the Joanna Briggs Institute methodology for scoping reviews and will be reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines). A comprehensive search will be conducted across multiple electronic databases, including MEDLINE, APA PsycINFO, CINAHL, Scopus, Web of Science, IEEE Xplore, and ACM Digital Library, as well as selected gray literature sources. The search strategy combined terms related to digital companions, conversational AI, aging, and care contexts, including concepts related to companionship, social interaction, communication, loneliness, and psychosocial support., Eligible studies will include empirical studies involving older adults that examine non-embodied AI-based digital companions designed primarily to support companionship, social interaction, communication, or related psychosocial support in aging, health, and care contexts. Two reviewers (YC and MG) will independently conduct study selection and data charting. Data will be synthesized using descriptive statistics and narrative analysis. RESULTS: This protocol outlines a systematic approach to identifying, selecting, and synthesizing the existing evidence on non-embodied AI-based digital companions. A preliminary PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-style search flow for the revised database searches identified 2289 records from MEDLINE, CINAHL, and APA PsycINFO. After duplicate removal and eligibility-based removals before screening, 1978 records remained available for title and abstract screening. The full scoping review will summarize study characteristics, populations, contexts, digital companion features, and methodological trends using descriptive tables and narrative synthesis. As of June 2026, the revised database searches had been completed for MEDLINE, CINAHL, and APA PsycINFO, and title and abstract screening was underway. The full scoping review results are expected to be submitted for publication after screening, data charting, and synthesis are completed. CONCLUSIONS: This scoping review is expected to clarify conceptual boundaries, map the scope of current research, and identify knowledge gaps related to non-embodied AI-based digital companions in health, aging, and care contexts. The findings will inform future research, design, and implementation of non-embodied AI-based digital companions.
26 June 2026
Read appraisal →JMIR medical education
Exploring the Role of Early Career Medical Professionals From a Digital-Oriented University in Germany in Promoting Digital Health in Professional Settings: Qualitative Interview Study
BACKGROUND: To address care delivery gaps, the health care system must embrace innovative digital solutions. Additionally, the rising integration of digitalization as a topic into medical education is providing students with broader opportunities to engage with digitalization overall. As digital health becomes an increasingly integral component of medical education and health care practice, digitally affine early career medical professionals constitute a vital resource for advancing digitalization within the health care sector. OBJECTIVE: This study examines how early career medical professionals from Witten/Herdecke University in Germany, with various courses focusing on digitalization, acquire digital knowledge, apply it across diverse practical contexts-ranging from start-ups and corporate environments to traditional clinical settings-and how they contribute to the advancement of digitalization within the entire health care sector. METHODS: Using a qualitative approach, 19 interviews were conducted with early career medical professionals who graduated in the last 15 years at Witten/Herdecke University. Subsequently, the interviews were transcribed and analyzed using a deductive-inductive approach. RESULTS: The findings reveal that medical graduates often acquire digital skills through intensive self-study and learning on the job, integrating them in various ways into their professional lives. Moreover, while graduates recognize their high potential to make their own contribution to the advancement of digitalization, they also face significant barriers such as knowledge gaps, limited resources, and complex regulations, which hinder their ability to contribute to digitalization in a variety of professional settings. Medical graduates report that they face a pressing need for enhanced knowledge access, improved institutional frameworks, and supportive policy measures to maximize their potential in advancing digitalization initiatives. CONCLUSIONS: Recent medical graduates represent an underused resource for health care digitalization. Unlocking this potential requires coordinated action across medical education, health care institutions, and policymaking to create appropriate conditions for graduates to actively drive digitalization.
26 June 2026
Read appraisal →JMIR nursing
Nurses' Experiences Using AI in Clinical Practice: Systematic Review
BACKGROUND: Artificial intelligence (AI) tools are increasingly used in clinical settings, yet most syntheses focus on nurses' attitudes or readiness rather than experiences after direct use in practice. OBJECTIVE: This study aimed to synthesize registered nurses' experiences of using AI in clinical practice and to identify perceived benefits, barriers, and implementation implications. METHODS: We conducted a systematic literature review of empirical studies reporting nurses' experiences of AI use in clinical settings. Searches were performed in CINAHL, Embase, MEDLINE, PsycINFO, and PubMed (last search: September 13, 2023). Moreover, 2 reviewers (AJSS and QZ) independently screened titles, abstracts, and full texts and appraised included studies using the Mixed Methods Appraisal Tool. We used thematic synthesis with a primarily deductive framework based on the Technology Acceptance Model 2, with the addition of facilitating conditions from the unified theory of acceptance and use of technology. RESULTS: In total, 20 studies met the inclusion criteria. Perceived usefulness and facilitating conditions were most frequently reported; nurses described AI as supporting decision-making, workflow efficiency, and confidence when implementation included adequate training, interoperability, and technical infrastructure. Ease of use was closely tied to interface design and training. Job relevance and output quality were rated more positively when nurses described AI as aligning with nursing tasks and as producing interpretable, reliable outputs. Common barriers included usability issues, limited integration into workflows and electronic systems, privacy and trust concerns, and inconsistent or poorly contextualized outputs. Across studies, nurses often described adoption as conditional on organizational readiness and meaningful involvement of nurses in design and implementation. CONCLUSIONS: Nurses' accounts suggest that AI may augment clinical work, but the perceived benefits reported in the included literature were contingent on workflow alignment, usable interfaces, training, and supportive infrastructure. These findings suggest potential value in involving nurses in the co-design and iterative refinement of AI tools, grounded in the consistent evidence that facilitating conditions, such as training, interoperability, and organizational readiness, are the primary determinants of whether AI is experienced positively. Prospective evaluation using nursing-relevant outcomes, such as usability, workflow integration, and trust, is needed to move beyond post hoc experiential accounts and establish what implementation conditions reliably produce benefit.
26 June 2026
Read appraisal →BMJ open
Understanding self-management and digital health needs in musicians with playing-related musculoskeletal disorders: an explanatory sequential mixed-methods study
OBJECTIVES: To explore the characteristics of musicians with current or previous troubling musculoskeletal symptoms associated with musician self-limitation of activity (often referred to as playing-related musculoskeletal disorders (PRMSDs)), barriers and facilitators to PRMSD self-management, health needs and perspectives on digital health interventions (DHIs). The purpose of the study was to understand the unique needs of this population to effectively inform future DHIs to support this community. DESIGN: A mixed-methods study comprising an online survey and four online focus groups (2-3 participants per group). Survey data were analysed statistically, and focus group discussions were analysed thematically. SETTING: Online survey and focus groups conducted via Zoom. PARTICIPANTS: UK-based musicians with current or previous PRMSDs. RESULTS: A total of 100 musicians participated in the survey study, and 10 musicians participated in the focus group study. Over a third (38%, n=38) of participants had experienced musculoskeletal symptoms that interfered with their ability to play at their usual level 0-2 times in the last 5 years, 24% (n=24) reported 3-5 episodes, and 38% (n=38) had experienced them more than five times. The majority of participants (67%, n=67) continued playing music while experiencing these symptoms. Several barriers to self-management were identified, including limited music-specific health education during training, financial and time constraints and insufficient accessible resources. Key facilitators and health needs included healthcare professionals who understood musicians' professional demands and performance pressures, personalised instrument-specific health support, peer support from fellow musicians and accessible digital resources including self-assessment tools, automated reminders, health tracking, motivational features, peer support forums and multimedia resources organised by condition or symptom stage. Addressing these needs could enable musicians to better manage their symptoms, maintain playing activity and make informed decisions about when and how to seek help. CONCLUSION: This study reveals unmet health needs in musicians and offers recommendations for designing DHIs that promote musicians' health and well-being. Key design recommendations include personalisation by instrument type, health tracking, peer support and consolidated multimedia resources. These findings can inform both intervention design and policy development to better support this population.
26 June 2026
Read appraisal →Journal of medical Internet research
You Can't Launch This: Trust as Infrastructure in Digital Behavioral Health
Digital behavioral health interventions frequently fail to scale, even when evidence-based and technically and operationally sound. In this News and Perspectives article, researcher, digital behavioral health platform founder, and JMIR Correspondent Trevor van Mierlo concludes a four-part series examining why this occurs, reporting on the foundational role of trust.
25 June 2026
Read appraisal →Journal of medical Internet research
Digital Health Intervention to Promote Lifelong Specialized Care in Adults With Congenital Heart Disease: Theory-Driven Community Co-Designed Study
BACKGROUND: Adults with congenital heart disease frequently experience gaps in lifelong adult congenital heart disease (ACHD) specialty care, leading to preventable complications, hospitalizations, and premature mortality. However, effective, scalable, accessible, and sustainable strategies to reduce these gaps are lacking. Digital health offers potential solutions but requires a rigorous scientific approach in its design to address the needs of the target population. OBJECTIVE: This study aims to describe the development of a theory-based, community co-designed digital health intervention to improve lifelong ACHD care. METHODS: We integrated theory-based behavioral frameworks, semistructured qualitative interviews with patients and clinicians, and a community-engaged approach to develop a digital health intervention for ACHD. The primary behavioral target was completing an ACHD specialist appointment. We conducted Capability, Opportunity, Motivation, and Behavior (COM-B)-guided semistructured interviews with patients with ACHD and clinicians to identify barriers to specialized care amenable to a digital intervention, and patient-centered goals for the digital tool. The community partners helped develop key intervention objectives, create a theory-driven framework, and specify how each intervention component targets specific COM-B barriers. RESULTS: We interviewed 54 participants (n=37 patients with ACHD, and n=17 clinicians) and engaged 21 community partners representing 4 advocacy organizations. Design objectives emphasized addressing patient loneliness, ensuring accessibility and credibility, and enabling scalability while centering patient perspectives. Participants identified 4 priorities: providing credible resources, uplifting patient voices, customizing to patient needs, and centering positivity and joy. The digital tool, named by community partners as Empower My Congenital Heart (EMCH), was designed within the web- and mobile-based, Apple- and Android-compatible, Eureka Digital Research platform (University of California, San Francisco). Key intervention components included educational modules, peer support, appointment planning nudges, and a digital medical passport. The EMCH's theory-driven framework specifies how each intervention component targets specific COM-B barriers to specialized ACHD care. CONCLUSIONS: The theory-driven, community co-designed EMCH digital tool provides a scalable approach to promote lifelong ACHD specialist care. Ongoing process evaluation and a planned randomized controlled trial will assess acceptability, engagement, and effectiveness in reducing care gaps. If proven effective, EMCH has the potential to prevent complications, emergency hospitalizations, and mortality affecting the majority of adults with congenital heart disease.
25 June 2026
Read appraisal →PloS one
A lightweight hybrid deep learning approach for fashion mnist classification with explainable attention visualization
The classification of fashion images is an essential task in the e-commerce sector, where accurate categorization improves user experience and refines product discovery. Convolutional Neural Networks (CNNs) and Transformers have demonstrated strong performance in image classification tasks due to their ability to learn complex visual features. However, deep variants of these architectures, such as VGG-19, ResNet-50, Vision Transformer (ViT), and Swin Transformer, contain tens of millions of parameters, requiring high memory and powerful GPUs for training, which makes them less suitable for low-resource and edge device environments. To address these limitations, this research proposes a lightweight hybrid architecture, TinyCNN with Linear Self-Attention (LSA), optimized for resource-constrained settings. The proposed model contains fewer than half a million parameters and is trainable on a CPU, achieving a classification accuracy of 91.47% on the Fashion-MNIST dataset. In addition, multiple Explainable Artificial Intelligence (XAI) techniques are implemented, including Self-Attention visualization, Multi-Head Attention, Attention Flow, Attention Rollout, Fixed query position attention maps, Integrated Gradients, LIME, and SHAP, to provide visual interpretability of the model's predictions and enhance transparency in its decision-making process.
25 June 2026
Read appraisal →Journal of Korean medical science
Evaluating Large Language Models for Post-Publication Promotion: A Blinded Comparative Study of Social Media Posts in Public Health
BACKGROUND: Social media platforms such as X (formerly Twitter) are increasingly used by journals, authors, and institutions to promote newly published research. Well-designed posts can enhance visibility, accelerate knowledge translation, and increase altmetric attention. However, creating accurate and policy-compliant content is time-intensive. Large language models (LLMs) offer a potential solution, yet systematic evaluations of their performance in post-publication promotion remain limited. METHODS: We conducted a blinded, crossed, offline evaluation of four LLMs: GPT-5 (OpenAI), Gemini 2.5 Pro (Google DeepMind), Grok-3 (xAI), and Perplexity Pro (Perplexity AI), tasked with generating X-style posts (≤ 260 characters) for 36 open access articles from The Lancet Public Health, The Lancet Planetary Health, and Annual Review of Public Health. Posts were generated using a standardized system and user prompt. A single blinded rater scored outputs using a five-domain rubric (factual accuracy, clarity, policy compliance, call-to-action quality, structure/metadata; maximum score 10). Secondary measures included character count, hashtag use, and readability (Flesch-Kincaid Grade Level). General linear models with Bonferroni-adjusted post hoc tests and non-parametric analyses were applied. RESULTS: All four models achieved perfect factual accuracy and no policy violations. Mean total quality scores differed significantly by model, P < 0.001. GPT-5 (9.60) and Perplexity Pro (9.60) performed best, followed by Gemini 2.5 Pro (9.47), while Grok-3 scored lower (8.80). Domain analyses showed Grok-3 underperformed in call-to-action quality (1.40 vs. ≥1.97 in other models, P < 0.001) and produced significantly shorter posts (median 194 characters, P < 0.001). Perplexity Pro scored highest for policy compliance, while GPT-5 and Gemini 2.5 Pro achieved superior structural scores. Readability varied: GPT-5 8.9 (7.3-9.2) and Perplexity Pro 7.3 (6.5-8.8) generated more complex outputs, whereas Gemini 2.5 Pro 5.1 (4.8-6.5) and Grok-3 4.5 (3.6-6.3) produced more accessible posts. CONCLUSION: LLMs can reliably generate accurate and policy-compliant social media posts for research promotion, with differences in style and readability that may inform audience targeting. GPT-5, Gemini 2.5 Pro, and Perplexity Pro produced high-quality outputs, while Grok-3 underperformed across several domains. These findings highlight the potential of LLMs as scalable first-draft tools for post-publication promotion, capable of improving the reach and accessibility of scientific research. Careful model selection, tailored to audience and communication goals, together with human oversight, remains essential.
25 June 2026
Read appraisal →BMJ health & care informatics
Clarifying the relationship between biomedical and health informatics and digital health: expert perspectives.
The rise of digital health (DH) has sparked debates about its relationship with the long-established field of biomedical and health informatics (BMHI), with some questioning the need for new academic programmes in DH since much of the likely content is already covered in BMHI training programmes. At the International Medical Informatics Association panel, 'Shaping the Future of Biomedical and Health Informatics in the Digital Health Era' (MEDINFO 2025), only one-third of participants clearly distinguished the two fields, highlighting conceptual ambiguities and the influence of DH narratives. This article compiles the perspectives expressed by the panellists, all experienced BMHI scholars, presenting their distinct viewpoints on how the field can maintain its scientific identity while engaging with emerging DH initiatives.
24 June 2026
Read appraisal →Journal of medical Internet research
Trusting Generative AI for Health Advice: Preregistered Survey Experiment
BACKGROUND: Generative artificial intelligence (AI) systems are increasingly used for health information seeking, yet it remains unclear how the public evaluates AI-generated health advice relative to guidance from credentialed clinicians in digital environments. Understanding the conditions under which AI is perceived as credible is critical as these systems become integrated into digital health ecosystems. OBJECTIVE: This study examined how source type (a human nurse in an online portal, a health care-specialized generative artificial intelligence system framed as an "AI nurse," or ChatGPT [OpenAI], a general-purpose chatbot), message characteristics, contextual risk, values framing, and individual differences in medical skepticism and experience with AI shape credibility evaluations of the provided advice and its purported source. METHODS: In a preregistered online experiment, a national sample of US adult participants (N=1502) was randomly assigned to 1 of 3 source conditions and evaluated health advice across 3 scenarios: low risk (dietary advice for cholesterol), high risk (chest pain triage), and a morally sensitive scenario (egg freezing). Advice type (intuitive vs counterintuitive) was manipulated in the risk scenarios, and ideological framing (neutral, conservative-leaning, and liberal-leaning) was manipulated in the morally sensitive scenario. Primary outcomes included participants' perceived credibility of the advice and beliefs about whether the patient should follow it. Source-level perceptions of competence and benevolence were also assessed. Medical skepticism and prior AI experience were examined as moderators. RESULTS: Advice attributed to a human nurse was rated as more credible than advice attributed to either AI source. Message intuitiveness showed effects comparable to, and sometimes larger than, the effects of source: intuitive advice was perceived as more credible than counterintuitive advice, with this difference amplified in high-risk contexts. In the morally sensitive scenario, ideological framing influenced perceived bias but did not interact significantly with source. Medical skepticism moderated source evaluations: higher skepticism was associated with greater perceived competence of the AI nurse and lower perceived competence of the human nurse. CONCLUSIONS: Generative AI is evaluated within existing credibility frameworks rather than dismissed outright as inferior to human expertise. While licensed clinicians retain a credibility advantage, AI-generated advice is generally perceived as competent and legitimate. Importantly, individuals skeptical of traditional medical authority may evaluate AI-based guidance more favorably, suggesting that AI systems may redistribute-rather than uniformly erode-trust in health advice. As AI tools become embedded in patient-facing health platforms, message design and audience characteristics may shape acceptance more strongly than source labeling alone.
24 June 2026
Read appraisal →JMIR nursing
Digital Leadership Scale for Clinical Nurses: Development and Validation of an Instrument
BACKGROUND: The rapid advancement of digital technologies, combined with the evolving complexity of health care environments, has introduced a new paradigm in nursing practice. Clinical nurses are now required not only to deliver safe and effective patient care but also to demonstrate competencies in digital literacy and innovation. Among these emerging competencies, digital leadership has become a critical attribute-enabling nurses to lead digital transformation, ensure patient safety, enhance care quality, and support system-level change within health care organizations. Despite its increasing relevance, there is a notable absence of validated measurement tools tailored to assess digital leadership in clinical practice. OBJECTIVE: This study aimed to develop and psychometrically validate a Digital Leadership Scale for Clinical Nurses (DLS-CN) to systematically evaluate the digital leadership capabilities of nurses working in clinical settings. METHODS: The scale development process followed a rigorous multistep procedure. Initial items were derived from previous qualitative research involving a literature review and in-depth interviews, complemented by an additional literature review conducted in this study. The content validity of 38 preliminary items was evaluated by 9 experts over 2 rounds. A pilot test was conducted with 30 nurses, followed by cognitive interviews with 5 nurses to refine item clarity and relevance. The final set of items was administered to 446 clinical nurses across various health care institutions. Data were randomly split for exploratory factor analysis and confirmatory factor analysis. Additional analyses were conducted to evaluate item discrimination, convergent validity, and internal consistency using IBM SPSS 25.0 and AMOS 23.0. RESULTS: The finalized DLS-CN consists of 29 items grouped under four domains: (1) ability to use digital technology, (2) digital safety management, (3) digital collaboration mindset, and (4) organizational influence. These 4 factors explained 56.9% of the total variance. The scale showed strong internal consistency (Cronbach α=0.95). Convergent validity was demonstrated through strong positive correlations with the Nursing Informatics Competency Scale (Pearson correlation coefficient r=0.82; P<.001) and the Self-Leadership Scale (Pearson correlation coefficient r=0.83; P<.001). CONCLUSIONS: The DLS-CN is a valid and reliable instrument for measuring digital leadership among clinical nurses. It offers a practical tool for educators, administrators, and researchers to assess and enhance digital leadership capabilities-ultimately supporting the digital transformation of health care systems.
24 June 2026
Read appraisal →Journal of medical Internet research
Potential of Digital Tools for Chronic Pain Management
Chronic pain is notoriously complex to manage and treat. In this News and Perspectives article, JMIR Correspondent Vanessa Nirode reports on several digital innovations that may help to bridge existing care gaps.
24 June 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
AI agents are sensitive to nudges.
Large language models (LLMs) are increasingly deployed as autonomous agents that make choices and use tools on behalf of users. Yet, we have limited evidence about how their decisions are shaped by their environment. We adapt a human decision-making task to test leading LLMs under four forms of choice architecture: defaults, suggestions, information highlighting, and "optimal" nudges derived from a resource-rational model of human choice. We treat human behavior as a baseline for predictable sensitivity to such interventions. Across models and prompting strategies, LLMs often depart substantially from this baseline. They sometimes pay excessive costs to acquire information, sometimes ignore available information, and, most crucially, are far more responsive to nudges than humans, such that weak cues that slightly shift human behavior have larger effects on model choices, toward both better and worse payoff outcomes. Chain-of-thought prompting and in-context human data do not reliably stabilize behavior. Recent reasoning-optimized LLMs can, in some configurations, restore more human-level sensitivity to nudges, but do so inconsistently and at substantial computational cost. These results point to an important and largely neglected safety concern: LLM agents can be behaviorally brittle under subtle changes in choice architecture, even in the absence of adversarial settings.
23 June 2026
Read appraisal →Journal of psycholinguistic research
Statistical Analysis of Human and AI Machine Translation Accuracy, Style, and Context in Academic English
AI-driven Machine Translation (MT) technology has raised doubts about whether it can replace Human Translation (HT), particularly in the academic English field. This research contrasts AI-based MT and HT of academic English on accuracy, stylistic quality, errors, contextual appropriateness, and overall suitability. A quantitative comparative design was employed for the translation of eight Chinese texts with various genres. Using purposive sampling, 4 certified human translators were selected to produce the HT, and also used AI-based MT systems such as DeepL, ChatGPT, and Google Translate to translate the text. 69 experts evaluated the translated versions, and this data was examined using SPSS software. HT produced better performance than MT through mean accuracy, stylistic quality, contextual appropriateness, and overall suitability (Human: 4.76, AI: 3.73), revealed statistically significant differences (p < 0.001), and effect sizes (Cohen's d = 1.53 to 2.81). Compared to HT, the MT showed higher grammatical and semantic errors (Cohen's d = 2.81). The fluency, tone, and register of stylistic quality were evaluated at higher levels for HT (p < 0.001). Among AI-MT tools, DeepL shows higher performance in translation (M = 4.16, SD = 0.37). The genre-wide comparison of MT and professionally certified HT, and the comparison among multiple AI tools, towards various translation dimensions, provides insights into the differences in translating quality. The research indicates a continued reliance on human professionals for important scholarly translations while encouraging continued development in AI translation software for better quality.
22 June 2026
Read appraisal →JMIR cancer
Digital Intervention for Electronic Patient-Reported Outcomes in Advanced Cancer: Mixed Methods Study
BACKGROUND: Digital health interventions are increasingly being integrated into oncological care to support patients in managing treatment-related symptoms and psychological distress. In a randomized controlled pilot trial, we investigated the feasibility and preliminary efficacy of a digital health app (SOFIA) among patients with cancer, including those in palliative care. SOFIA consists of an electronic patient-reported outcome (ePRO) assessment and coaching component. We showed good feasibility and high acceptability of SOFIA in routine clinical care. OBJECTIVE: This pilot study aimed to evaluate user experiences and behavior with SOFIA. We applied a mixed methods design, combining a qualitative exploration of patients' experiences and a quantitative analysis of app usage patterns. The integrated findings are intended to inform the refinement and further development of digital health interventions to better address the needs, preferences, and engagement behaviors of this patient population. METHODS: Patients randomly assigned to the intervention group participated in semistructured interviews at 2 time points during the 12-week intervention period: midway through the intervention (at week 6) (T1) and postintervention (at week 12) (T2). Qualitative data were analyzed using content analysis. User data were descriptively analyzed with Python and the Pandas library (version 2.2.3). RESULTS: Our qualitative analysis of 29 patients revealed benefits regarding the ePRO assessment (empowerment, support, user-friendliness, and facilitation of patient-physician communication), as well as some criticism (inflexible design and insufficient use by physicians). Benefits of the coaching tool included the availability of helpful information and design aspects such as clarity and user-friendliness. Quantitative data from 32 active users of the app showed that 16 (50%) of patients read articles, 28 (87.5%) started journeys, and 15 (46.9%) completed exercises at any point during the study. CONCLUSIONS: The results of this mixed methods study may provide important indications for digital interventions, including ePRO assessment, for patients with cancer. From the patients' perspective, key features include intuitive design, relevant symptom items, reliable reminder functions, the translation of ePROs into clinically actionable information, clear symptom visualizations, seamless integration into electronic health records, and effective physician engagement. Both our qualitative and quantitative data show the importance of therapy-specific content. These results might increase acceptance and usage, and thereby also the clinical benefit, of future digital interventions for patients with cancer.
20 June 2026
Read appraisal →European radiology experimental
Digital twin technologies in prostate cancer as a frontier for precision medicine
Prostate cancer (PCa) is the most frequently diagnosed malignancy among men and presents major clinical and socioeconomic challenges worldwide. Despite advances in early detection, imaging, and therapy, managing PCa remains complex due to disease heterogeneity, risks of overdiagnosis, and overtreatment. Digital twin (DT) technologies might represent an emerging conceptual framework aimed at supporting dynamic, patient-specific virtual modeling for personalized clinical decision-making. By integrating multimodal clinical, imaging, molecular, and physiological data, DTs can simulate disease progression, predict treatment responses, and support proactive, adaptive care. This perspective explores the conceptual framework for DT ecosystems in PCa, highlighting potential clinical impacts, infrastructural requirements, and barriers to implementation. Harnessing DTs could impact PCa management into a truly predictive, personalized, and participatory approach, improving outcomes and optimizing healthcare resource utilization globally. RELEVANCE STATEMENT: DT technologies may enable personalized, predictive PCa management by integrating multimodal patient data to guide diagnosis, treatment selection, and monitoring, with the potential to improve outcomes, reduce overtreatment, and optimize healthcare resource utilization KEY POINTS: DTs create virtual patient models to personalize PCa care. They integrate imaging, clinical, and molecular data into one system. This approach may reduce overdiagnosis and unnecessary treatments.
20 June 2026
Read appraisal →Current psychiatry reports
The Digital Mirror: Clinical Potentials and Relational Risks of Generative AI in Mental Health Interventions
PURPOSE OF REVIEW: This review explores the rapidly evolving integration of Generative Artificial Intelligence (GenAI) in mental health care. It aims to evaluate current applications in assessment, treatment planning, and psychotherapeutic interventions, while critically examining the clinical risks, ethical dilemmas, and the future potential of GenAI as an adjunctive tool rather than a replacement for human-delivered therapy. RECENT FINDINGS: Recent studies indicate that AI models can effectively assist in diagnostic reasoning, biomarker identification via EEG, and the prediction of symptom trajectories from session transcripts. Randomized controlled trials (RCTs) suggest that GenAI chatbots significantly reduce anxiety and depressive symptoms in the short term, particularly in settings with limited access to clinicians. However, human-led therapy remains superior in fostering deep emotional engagement and clinical impact. Significant risks identified include the potential for GenAI to foster dependency, reinforce maladaptive schemas or delusional ideation through "sycophantic" mirroring, and raise complex ethical-legal challenges regarding the reporting of criminal disclosures. AI represents a transformative adjunctive layer in mental health, offering scalable support for assessment, training, and between-session monitoring. While technological advances in personalization, multimodality, and immersive virtual reality enhance its clinical utility, GenAI lacks the authentic relational depth and"calibrated mismatches" essential for autonomy and transformative change. Future integration must prioritize a human-centered, blended approach, where GenAI is strictly supervised by clinicians within a robust ethical and regulatory framework to preserve the essential heart of the therapeutic connection. Research priorities, interim clinical safeguards, and recommendations for navigating the gap between current evidence and real-world adoption need to be defined and implemented.
19 June 2026
Read appraisal →Current psychiatry reports
Digital Tools to Support Mental Health in Later Life: Scoping Review of Systematic Reviews
PURPOSE OF REVIEW: This scoping review synthesises existing evidence from systematic reviews on the effectiveness and implementation of digital mental health interventions among community-dwelling older adults. RECENT FINDINGS: Twenty-one systematic reviews were included. Results showed that a range of digital tools demonstrate potential to improve common mental health and psychosocial symptoms among older adults, with most evidence concentrating on digital tools to improve depressive symptoms. However, reviews' findings were frequently mixed and accompanied with cautions that primary evidence under-reported key elements such as theoretical underpinnings, intervention design process, participant demographics, intervention acceptability and usability, participant retention, adverse events, and long-term outcomes. More rigorous research and reporting are needed to understand the mechanisms underpinning effective digital mental health interventions for older adults and how they might mitigate the age-related digital divide in mental health services.
19 June 2026
Read appraisal →The Journal of clinical endocrinology and metabolism
Diabetes technology: an update.
Diabetes is one of the most prevalent chronic diseases worldwide, with rates that continue to increase. More than 30 years ago, the Diabetes Control and Complications Trial demonstrated that intensive glucose management decreased long-term vascular complications. Unfortunately, many people with diabetes still struggle to meet glycemic goals. In this mini-review, we highlight advances in diabetes technology that are associated with improvements in glycemic management. Continuous glucose monitoring (CGM) and automated insulin delivery systems are associated with significant improvements in glycated hemoglobin (HbA1c), time in range (70-180 mg/dL), and decreases in severe hypoglycemia and diabetic ketoacidosis in people with both type 1 and type 2 diabetes. Recent data show that these technologies improve outcomes early in the course of type 1 diabetes. CGM is also being explored as a tool to monitor progression through early-stage type 1 diabetes (2 antibodies positive) to identify individuals who may benefit from disease-modifying therapies as well as to prevent the onset of diabetic ketoacidosis at onset of stage 3 (insulin-requiring) type 1 diabetes. Adjunctive pharmacologic therapies and artificial intelligence may further expand and improve therapies, offering potential synergistic benefits. However, there continue to be significant disparities in access to diabetes technologies and access to insulin worldwide. This mini-review summarizes recently published data, highlights emerging applications, and underscores the need to pair technological innovation with strategies that promote equitable access and support for diabetes care to improve outcomes for all people with diabetes.
19 June 2026
Read appraisal →International journal of medical informatics
Automated Flow and local LLM-Driven clinical Context Engineering: Precision colorectal cancer recurrence registry
OBJECTIVE: This study develops and validates a deployable, privacy-preserving automated tool to address the labor-intensive nature of colorectal cancer (CRC) recurrence registration. Our tool utilizes a reproducible, two-stage 'Clinical Context Engineering' workflow to mimic expert clinical reasoning and overcome the limitations of handling longitudinal clinical data ambiguity. METHODS: We retrospectively studied 3053 CRC patients (2010-2018). A local Large Language Model (LLM) (Qwen3:14B) analyzed ∼ 19,900 pathology and ∼ 43,900 imaging reports using iterative, patch-based analysis ("raw LLM"). Clinical validation rules were applied to generate "rule-based LLM" output, enhancing explainability and trustworthiness. Both automated methods and the manual Taiwan Cancer Registry (TCR) database were compared against a 20% manual reference standard (N = 602). Full prompts and validation code are provided for complete reproducibility. RESULTS: Under a strict 60-day temporal tolerance, the Rule-Based LLM achieved 90.7% accuracy, comparable to standard TCR processes (92.0%), and 77.2% sensitivity. The application of clinical validation rules significantly improved specificity from 87.7% (Raw LLM) to 93.9% (Rule-Based LLM). In time window analysis, the Rule-Based LLM identified 87.1% of recurrences within 60 days of the reference date. CONCLUSION: Our locally deployed, privacy-preserving, and explainable Clinical Context Engineering framework offers a viable, non-inferior alternative to standard TCR processes, reducing workload while maintaining data quality and fostering trust in AI-assisted cancer registry automation.
16 June 2026
Read appraisal →International journal of medical informatics
An AI-assisted framework for the ethical use of machine learning in healthcare
This study develops and evaluates ETHICS, a concise, clinician-facing ethical protocol for the routine use of machine learning (ML) in healthcare. Using ChatGPT for first-stage drafting, we generated six actionable principles - Equity and Fairness, Transparency and Patient-Centered Care, Human Oversight and Clinical Integrity, Information Privacy and Data Governance, Continuous Improvement and Sustainability, and Support and Education for Professionals - structured as a mnemonic to support uptake in time-constrained clinical settings. Outputs were treated as provisional and refined through mandatory human source verification, iterative readability optimization, multidisciplinary expert review, and scenario-based stress testing. Readability analysis showed substantial improvement from high complexity to clinician-accessible language. Expert ratings indicated strong endorsement with excellent inter-rater reliability. Implementation readiness was confirmed across five clinical scenarios, all passing predefined adequacy thresholds. The findings suggest generative AI can accelerate ethical protocol drafting, but validity and practicality depend on structured human oversight and real-world testing.
16 June 2026
Read appraisal →Sexual health
Digital versus clinic-based sexual health services: patterns of use and equity implications from a UK-based evaluation
BACKGROUND: Digital sexual health services have expanded rapidly in England; yet, concerns remain regarding equitable access among different population groups. We compared demographic characteristics and outcomes between online and clinic-based sexual health service users in Leicester, Leicestershire, and Rutland. METHODS: A retrospective analysis of data (April 2019-June 2023) compared online contacts, via SH:24, a UK online sexual health service providing postal sexually transmitted infection (STI) testing and remote contraception, with in-clinic attendances. Demographic variables included age, gender, ethnicity and Index of Multiple Deprivation (IMD 2019) decile, where Deciles 1-2 represent the most deprived areas. Ethnicity was recoded into Office for National Statistics categories. Reasons for attendance, contraception methods, and STI test outcomes were analysed. Risk ratios with 95% confidence intervals compared proportions between modalities. RESULTS: Online users were younger than clinic attendees (mean age, 25.1 vs 31.4 years). A slightly higher proportion of women accessed online services although gender was unrecorded in 59.1% of online attendances. White and mixed ethnicity groups were more likely to use online services than Black, Asian and 'Other' ethnic groups. Individuals living in the most deprived areas were less likely to access online services. STI testing was more frequent online (56.8% vs 43.3%), while positivity rates for infections were lower. CONCLUSIONS: Complementary to clinic-based sexual health services, online pathways provide accessible STI testing and contraception. Differences in uptake by age, ethnicity and deprivation highlight the importance of monitoring equitable access as digital provision expands. Maintaining both online and face-to-face provision is likely to remain important for equitable sexual health care.
16 June 2026
Read appraisal →International journal of medical informatics
From algorithmic innovation to clinical deployment: A systematic review of methodological gaps limiting federated learning in healthcare
OBJECTIVE: Federated learning (FL) is a distributed machine learning paradigm designed to enable model training across decentralized data sources without requiring data centralization. This review critically examines FL as a methodological approach in biomedical informatics, summarizing its conceptual foundations, methodological variants, successes, and limitations, and identifying directions for future research. METHODS: We conducted a systematic methodological review following PRISMA 2020 guidelines. Searches were performed in PubMed, Web of Science, Scopus, IEEE Xplore, and Google Scholar for studies published between January 2017 and March 2025. Articles were included if they proposed, extended, or critically analyzed FL methods for biomedical or life science data. Methods were categorized by federation topology, optimization strategy, data heterogeneity handling, privacy mechanisms, evaluation design, and translational readiness. RESULTS: From 8,412 records, 97 articles met inclusion criteria. Rapid methodological innovation exists in optimization and privacy mechanisms, while support for design-time evaluation and governance remains limited. CONCLUSION: Federated learning represents a significant methodological advance in biomedical informatics but current implementations address only a subset of translational challenges. Future work must integrate study design, evaluation, interpretability, and governance into FL methods.
16 June 2026
Read appraisal →Croatian medical journal
Artificial intelligence in pathology: a framework for preserving brain capital in the diagnostic apex
AIM: To present a five-criterion calibration framework for evaluating artificial intelligence (AI) tools in pathology centered on preserving "brain capital" - the finite cognitive resources available to clinicians - and stabilizing the "diagnostic apex," the point where histological evidence meets clinical judgment. METHODS: Using cognitive load theory (CLT) and implementation science, we developed the Pathology AI Diagnostic Balance - a conceptual model mapping the physician-specimen interaction within working memory and IT infrastructure constraints - and examined it in the context of peer-reviewed case studies of implemented pathology AI tools. RESULTS: The framework identifies five criteria: Contextual Literacy (integration of clinical history), Responsibility (preservation of signing authority), Evidence Gap Analysis (closure of diagnostic uncertainty), Verification (feasibility of quality control), and Opportunity Cost (return on cognitive investment). Successful tools, such as Ki-67 quantification and breast cancer screening, reduce cognitive burden by automating routine tasks or enabling rapid verification. Conversely, tools requiring an exhaustive re-review or producing opaque recommendations deplete cognitive bandwidth, causing decision fatigue and diagnostic errors. CONCLUSION: Pathologists are positioned to exercise evaluative authority over tools that increase extraneous cognitive load. Clinical expertise represents pathologists' most distinctive contribution to AI development: defining which failure modes matter clinically, what context an AI must integrate, and whether a tool serves patient care. By prioritizing cognitive sustainability over purely technical metrics, the profession can ensure AI functions as a genuine enhancement of diagnostic capability rather than an additional burden on diagnostic workflows.
15 June 2026
Read appraisal →Journal of affective disorders
MDD-thinker: A reasoning-enhanced large language model for diagnosis of major depressive disorder.
BACKGROUND: Major depressive disorder (MDD) is a leading cause of global disability and poses a substantial public health burden. However, current diagnostic approaches largely rely on subjective assessments and lack the ability to integrate heterogeneous clinical and sociodemographic information. Recent advances in large language models (LLMs) offer new opportunities to support MDD diagnosis through reasoning over complex data, yet their clinical applicability is constrained by challenges related to interpretability, hallucinations, and reliance on synthetic data. METHODS: We propose MDD-Thinker, an LLM-based diagnostic system that integrates supervised fine-tuning (SFT) and reinforcement learning (RL) to enhance reasoning and interpretability under the evaluated conditions. Using the UK Biobank dataset, we constructed 40,000 structured reasoning samples and incorporated an additional 10,000 records from publicly available mental health datasets. MDD-Thinker was trained on these heterogeneous textual data and evaluated against conventional machine learning models, deep learning methods, and representative LLM baselines in terms of diagnostic performance and interpretability. RESULTS: MDD-Thinker achieved high performance in MDD diagnosis, with an accuracy of 0.8268 and an F1-score of 0.8081, showing better performance than conventional machine learning models, deep learning approaches, and representative LLM baselines on the evaluated dataset. Beyond predictive accuracy, it consistently produced structured reasoning paths that were clinically coherent, enabling transparent interpretation of diagnostic decisions in the evaluated experiments. The integration of SFT and RL contributed to notable improvements in both diagnostic reliability and reasoning quality. CONCLUSION: MDD-Thinker demonstrates the potential of reasoning-enhanced LLMs for large-scale MDD diagnosis under the evaluated settings. By jointly optimizing accuracy, interpretability, and efficiency, the proposed system provides a scalable and explainable approach for intelligent psychiatric assessment within the scope of the study, highlighting the potential of reasoning-oriented LLMs in mental health care.
15 June 2026
Read appraisal →International journal of medical informatics
Machine learning approaches to identifying neurodevelopmental disorders using social media data: a systematic review
BACKGROUND: Neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), often emerge in early childhood and can significantly impact social, academic, and emotional functioning. Early identification is critical to improving long-term outcomes, yet traditional diagnostic processes are time-consuming and resource-intensive. As social media becomes an integral part of daily life, user-generated content offers a novel source of behavioral and linguistic data that may support early detection. Advances in artificial intelligence (AI) and machine learning (ML) now make it possible to analyze these large-scale data streams for clinical insights. METHODS: A comprehensive search was performed across five databases-PubMed, Scopus, Web of Science, IEEE Xplore, and ACM Digital Library-from inception up to July 2025. Studies were included if they used AI or ML methods to analyze social media data for detecting NDDs. Data extraction focused on platform type, targeted disorder, dataset size, ML technique, and diagnostic performance. RESULTS: Nineteen studies met the inclusion criteria. Most focused on ASD and ADHD, using platforms such as Reddit, Twitter, YouTube, and Facebook. ML models achieved moderate to high classification performance, with F1-scores ranging from approximately 0.48 to 0.89 depending on the data type and disorder. Video-based models showed particular promise in identifying nonverbal behavioral markers. However, research on other NDDs remains limited, and methodological heterogeneity, small sample sizes, and ethical challenges persist. CONCLUSION: AI-driven analysis of social media data holds significant promise for scalable, non-invasive screening of neurodevelopmental disorders. While current work largely focuses on ASD and ADHD, future research should extend to underrepresented NDDs and address concerns related to data validity, bias, and privacy. With continued advancement, these tools may serve as valuable complements to traditional diagnostic methods.
15 June 2026
Read appraisal →Journal of affective disorders
Implementing artificial intelligence (AI) to facilitate health outcomes in mood disorders: Application versus aspiration.
The burden of depressive and bipolar disorders at the individual and societal level are extraordinary and increasing. For decades, evidence-based treatments for these conditions have been established but outcomes amongst individuals with lived experience remains suboptimal. Notwithstanding calls to close gaps between current practice and best practice, there is an absence of evidence that overall health outcomes are significantly improving. Artificial Intelligence (AI) is the cornerstone of the digital revolution. Currently, AI sources (e.g., Open Source) are widely accessed by healthcare providers and persons with lived experience for search queries and decision support. The aspiration for AI-informed medical practice is to improve health outcomes by assisting in timely diagnostic detection, illness monitoring, informing treatment selection, integrating multimodality care, decreasing barriers to access and facilitating scalability to psychosocial interventions. Against this background, rigorous evidence is still needed to empirically demonstrate transformative improvement in each of the aforementioned areas. In addition, multiple ethical, technical, scientific and economic questions are not adequately answered including aspects of confidentiality and patient engagement. This short commentary endeavors to succinctly summarize the evidentiary base as it relates to the capabilities that AI offers currently, in the near and more intermediate term. The overarching aim is to provide readers with an up-to-date understanding of what aspects of AI are currently applicable versus those that are aspirational.
15 June 2026
Read appraisal →International journal of medical informatics
Human in the loop artificial intelligence in healthcare: applications, outcomes, and implementation challenges
BACKGROUND: The integration of artificial intelligence in healthcare has transformed clinical practice and research methodologies. However, concerns regarding algorithmic accountability, interpretability, and safety have necessitated human oversight in AI systems. Human in the loop artificial intelligence represents a collaborative paradigm where human expertise and machine intelligence converge to enhance decision making while maintaining ethical standards and clinical safety. AIM: This review synthesizes current evidence on human in the loop AI in healthcare delivery and research, examining implementation frameworks, clinical outcomes, comparative advantages over fully automated and clinician-only approaches, and challenges. METHOD: A comprehensive narrative review was conducted using PubMed, Scopus, Web of Science, and IEEE Xplore databases covering studies from 2018 to 2025. Data were thematically synthesized to identify patterns, frameworks, and outcomes. This narrative approach enables comprehensive conceptual synthesis across diverse HITL-AI applications and contexts. RESULTS: Human in the loop AI demonstrates significant applications across diagnostic imaging, clinical decision support, patient monitoring, drug discovery, and research data analysis. Evidence indicates improved diagnostic accuracy, reduced medical errors, enhanced patient safety, and increased clinician trust compared to both automated AI and traditional approaches. Implementation requires EHR interoperability, clear liability frameworks, adaptive training protocols, and quantum-safe cryptographic security. Challenges include workflow integration, regulatory gaps for adaptive systems, and sustainability concerns. CONCLUSION: This review advances the field by synthesizing cross-domain implementation patterns, mapping collaboration models to risk-stratified contexts, identifying regulatory gaps for adaptive systems, and proposing future directions including post-quantum cryptographic integration, AI-driven adaptive architectures, and multi-center scalability frameworks for optimizing human-machine collaboration in healthcare.
15 June 2026
Read appraisal →Pulmonology
Hypnodensity sleep metrics from fingertip photoplethysmography are better associated with daytime sleepiness and fatigue than traditional metrics from polysomnography
BACKGROUND: Traditional sleep metrics from polysomnography are often limited by the hypnogram, in which each 30-s epoch is classified into one of the five sleep stages. This oversimplifies sleep stage continuity and fragmented sleep, typical in sleep-disordered breathing (SDB). Deep learning offers more dynamic sleep modelling with a higher temporal resolution by providing probabilities for each sleep stage, known as hypnodensities. RESEARCH QUESTION AND METHODS: We investigated whether high-frequency (1/s) hypnodensities captured from nocturnal fingertip photoplethysmography (PPG) recordings better correlate with patient-reported outcomes in SDB than traditional metrics. RESULTS: Among 2,280 patients (65% male, mean age 62 years, mean apnoea-hypopnoea index (AHI) 23.5/h), we found that several PPG-derived hypnodensity metrics correlate with self-reported daytime fatigue and sleepiness. The level of correlation was better or similar compared to traditional hypnogram-based sleep metrics derived from manually scored polysomnography. Specifically, PPG-based hypnodensities for N1 and N3 sleep showed a stronger association (p < 0.005) with fatigue and sleepiness than manually scored N1 and N3 sleep. Multivariable logistic regression (adjusted for age, sex, body mass index, AHI, and total sleep time below 90% blood oxygen saturation) revealed significant associations (p < 0.01) between multiple PPG-based hypnodensity metrics and daytime sleepiness. CONCLUSION: These findings suggest that PPG-based hypnodensities improve the characterisation of sleep quality and better predict patient-reported outcomes in SDB patients. Future uses may include tracking sleep quality with fingertip pulse oximeters outside of sleep centres, potentially over multiple nights due to their simplicity.
14 June 2026
Read appraisal →Journal of global health
mHealth for chronic disease management: effects on adherence in Ghanaian patients with diabetes and hypertension
BACKGROUND: Medication non-adherence remains a major barrier to effective chronic disease management, particularly in low- and middle-income countries. In Ghana, limited access to continuous care and patient engagement tools contributes to poor adherence among individuals living with type 2 diabetes and hypertension. Mobile health interventions, including smartphone applications, may offer scalable solutions to support long-term medication use and self-management. Here, we assess the effect of a smartphone-based intervention on medication adherence among adults with these conditions. METHODS: We conducted a quasi-experimental study among 874 adults with type 2 diabetes or hypertension in Ghana. Participants were assigned to either a smartphone-based intervention, which included medication reminders, educational content, and self-monitoring tools, or standard care involving routine outpatient management and follow-up without access to the smartphone-based intervention. We measured medication adherence at baseline and six months using the Medication Adherence Rating Scale and assessed it through generalised estimating equations and difference-in-differences analyses, adjusting for baseline adherence, age, sex, and treatment setting. RESULTS: Use of the smartphone application was associated with a greater improvement in medication adherence over six months in the overall study population (adjusted beta coefficient (aβ) = 0.062; 95% confidence interval (CI) = 0.010-0.123). Similar improvements were observed among participants with type 2 diabetes (aβ = 0.067; 95% CI = 0.012-0.135) and hypertension (aβ = 0.068; 95% CI = 0.005-0.134). However, there was no statistically significant difference in adherence between the intervention and control groups at six months. CONCLUSIONS: Smartphone-based interventions were associated with modest improvements in medication adherence over time, although between-group differences at follow-up were not statistically significant. As access to mobile technology expands across Africa, such interventions may provide scalable strategies to support chronic disease management.
14 June 2026
Read appraisal →Scandinavian journal of primary health care
The diagnostic accuracy of AI-assisted diabetic retinopathy screening in primary care: a prospective validation study
OBJECTIVES: This study investigated the diagnostic accuracy of AI-assisted diabetic retinopathy screening in primary care, using ophthalmologist-led screening as the reference standard. METHODS: Patients with type 2 diabetes attending routine appointments at 10 primary care clinics underwent AI-assisted screening, followed by re-screening at an ophthalmology clinic. The quality of fundus images captured in primary care was independently assessed, and diagnostic accuracy was evaluated by comparing AI-assisted results with ophthalmologist results, including sensitivity, specificity, PPV, NPV, and AUC. Two analyses were conducted: one including all images and one excluding those of poor quality. RESULTS: Among 183 patients (336 images), 18.6% of images were classified as poor quality. When all images were included, the AI-assisted screening achieved a sensitivity of 73.7%, specificity of 90.2%, PPV of 31.1%, NPV of 98.3%, and AUC of 0.82. Excluding poor-quality images improved sensitivity to 80.0%, NPV to 98.7%, and AUC to 0.84. Additional ocular findings unrelated to diabetic retinopathy were observed in 96 patients, including confirmed or non-specific signs of glaucoma, cataract, age-macular degeneration, benign nevus and reduced visual acuity. CONCLUSION: AI-assisted screening in primary care shows potential for clinical application, but further validation in larger populations and improvements in image quality are needed before clinical implementation.
14 June 2026
Read appraisal →BMJ paediatrics open
Smartphone-based support for early childhood development in a rural low-resource setting: an individually randomised pilot feasibility trial from Guatemala
OBJECTIVE: To test the feasibility of a smartphone app, BebeApp, that provides evidence-based early childhood development (ECD) guidance in rural Guatemala and pilot procedures for an adequately powered randomised controlled trial. DESIGN: We conducted an individually randomised pilot feasibility trial to compare BebeApp to printed ECD guidance. SETTING: This trial was conducted in Tecpán, Chimaltenango, a semi-rural community that is 95% Kaqchikel Indigenous Maya. PATIENTS: First-time primary caregiver-infant (0-28 days) dyads. INTERVENTIONS: BebeApp provides age-dependent, evidence-based caregiver guidance for breastfeeding and complementary feeding, sleep and developmental support. MAIN OUTCOME MEASURES: A mixed-methods evaluation of BebeApp's implementation and acceptability using the Reach, Effectiveness, Adoption, Implementation, Maintenance framework. App usability, usefulness and satisfaction were assessed via questionnaires, semi-structured interviews and app interaction data. RESULTS: 41 infant-caregiver dyads were enrolled with 40 completing the study. Engagement was high with caregivers opening BebeApp a median (IQR) 11 (6-21) times per month. Usability was found to be acceptable but no difference was found between pre-measurements and post-measurements. In interviews, caregivers expressed some initial difficulty using BebeApp but were able to gain confidence with a training session. App usefulness and satisfaction responses were positive. Caregivers often noted that BebeApp was their only source of ECD information outside of their family. CONCLUSIONS: We found that caregivers in rural Guatemala responded positively to a smartphone ECD app and engaged with it throughout the trial. Given the urgent need for ECD programmes in low- and middle-income settings, smartphone apps may be a method to deliver services directly to caregivers.
14 June 2026
Read appraisal →JMIR cancer
Identification of Early Signs of Mental Health Disorders in Older Survivors of Cancer Using Patient-Generated Health Data: Observational Study
BACKGROUND: Older survivors of cancer face heightened risk of depression and anxiety related to cancer experiences, fear of recurrence, and aging-related difficulties. Conventional mental health monitoring approaches, such as clinical assessments and even electronic patient-reported outcomes, are limited by recall bias, patient burden, and infrequent data collection. Emerging patient-generated health data from wearables and smart home devices offer passive, low-burden, continuous monitoring, but their ability to capture mental health risks in older survivors of cancer remains unclear. OBJECTIVE: This study aims to explore whether patient-generated health data collected in the wild, either passively or actively, can classify older survivors of cancer as having or not having signs of anxiety and depression based on Patient Health Questionnaire-4 (PHQ-4) scores and to assess the potential added value of passive monitoring modalities, such as smart plugs. METHODS: This study recruited 41 older survivors of cancer (mean age 72.3, SD 6.81 years) from the LifeChamps project. Over a 12-week period, participants were monitored using an activity tracker to measure physical activity, sleep, and physiological metrics; a smart scale to capture weight and body composition; and a smart plug to track television use as a proxy for sedentary television viewing. Mental health status was self-reported via the PHQ-4 questionnaire in a mobile app. Machine learning models were trained to classify mental health risk based on features derived from each sensor modality, both independently and in combination. RESULTS: Tree-based gradient boosting models showed good performance in classifying PHQ-4-defined mental health risk. The best-performing configuration, combining smart plug and smart scale features, achieved a mean F1-score of 0.77 (SD 0.15) and a mean area under the receiver operating characteristic curve (AUC) of 0.85 (SD 0.10) across 3 repeated train-test splits. Standalone smart plug models, based solely on passive television use patterns, achieved a mean F1-score of 0.66 (SD 0.04) and a mean AUC of 0.71 (SD 0.06), outperforming models that relied only on activity tracker data (mean F1 0.59, SD 0.2). Multimodal combinations tended to improve average performance but did not consistently yield large gains over the strongest single-modality configurations, likely reflecting adherence-related data loss for wearables and scales. Crucially, passive monitoring of television use patterns emerged as a promising behavioral proxy measure of mental health states. CONCLUSIONS: This study pioneers the use of passively collected data (eg, smart plugs) for mental health monitoring in older survivors of cancer, demonstrating their potential. Smart plugs capture behavioral patterns without user burden, with reasonable standalone performance (mean AUC 0.71, SD 0.06), positioning them as a promising low-burden modality. Future work should validate findings in larger independent cohorts and in prospective clinical workflows. Such technologies could transform monitoring for vulnerable populations, enabling scalable, inclusive care while reducing health care burdens.
14 June 2026
Read appraisal →Journal of medical systems
MEL-IA: An Interoperable AI System for Multimodal Skin Lesion Classification in Hospital Settings
UNLABELLED: Skin cancer represents a growing healthcare challenge, motivating the development of AI sytems that can support lesion assessment while integrating efficiently into clinical workflows. This study presents MEL-IA (MobilE skin Lesion dIAgnosis), an interoperable AI system designed for automated skin lesion classification and full integration with hospital information systems. METHODS: The system combines dermatoscopic images and structured clinical metadata using an EfficientNet-B4 - based multimodal model trained on the ISIC 2019 BCN_20000 and MSK subsets. Internal robustness was assessed through stratified 5-fold cross-validation, and external generalization was evaluated on the independent HAM10000 dataset. Interoperability and operational performance were validated through deployment in a real hospital environment using HL7, DICOM, PACS, and HIS/RIS systems. RESULTS: We evaluated internal robustness using stratified five‑fold cross‑validation, with global accuracy of 0.86, macro F1-score of 0.85, and AUC values above 0.97. The external evaluation showed that the model generalizes well under different acquisition conditions, achieving an accuracy of 0.84 and balanced accuracy of 0.65, with sensitivities of 0.60 for melanoma 0.72 for basal cell carcinoma. The multimodal model outperformed the image model baseline across all classes. Technical deployment confirmed full interoperability, > 99% successful study integration, and near real time processing. CONCLUSIONS: MEL-IA provides multimodal skin lesion classification capabilities and integrates smoothly into hospital infrastructures based on standards. The results demonstrate technical feasibility, interoperability, and operational viability for deployment within dermatology oriented clinical workflows. Further clinical validation studies are warranted to assess the system's impact on diagnostic decision-making and patient outcomes.
14 June 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Feasibility and acceptability of ExerciseGuideUK for those living with and beyond lung cancer: a mixed methods study
PURPOSE: Digital health interventions offer promise for addressing unmet needs in individuals living with and beyond lung cancer (LWBLC). ExerciseGuideUK, a personalised, web-based exercise platform, was developed for those LWBLC. This study aimed to evaluate ExerciseGuideUK. METHODS: Participants were recruited from Hull University Teaching Hospital and used ExerciseGuideUK over eight weeks. Feasibility and acceptability were assessed using quantitative measures, including the System Usability Scale, structured questionnaires, and qualitative interviews (guided by the Theoretical Framework of Acceptability). A novel mixed-methods integration approach called the Pillar Integration process (PIP) was used to integrate quantitative and qualitative findings on feasibility, engagement, barriers, and adaptation potential. RESULTS: Eighteen participants (mean age 65 ± 14.42y) enrolled. Recruitment and retention rates were 30.5% and 77%, respectively. Notably, 25.4% of those screened were excluded due to digital access issues, including some participants who owned smartphones, suggesting limited digital literacy. Among those who participated, acceptability, usability, and engagement were high. Valued features included goal-setting tools, breathlessness management strategies, and personalised exercise prescriptions. Barriers included treatment side effects, work responsibilities, and low confidence with technology. CONCLUSION: ExerciseGuideUK showed moderate feasibility and high acceptability among users. This is the first known application of PIP in the context of lung cancer, exercise, and digital health. The PIP facilitated a comprehensive understanding of feasibility and acceptability, guiding informed adaptations by users. The PIP warrants further testing and refinement in relation to optimising digital intervention design that promotes equitable, patient-centred care. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05121259.
14 June 2026
Read appraisal →Journal of robotic surgery
The need for a dedicated surgical consent framework for robotic telesurgery: a global call to action
Robotic telesurgery has progressed from experimental demonstrations to real-world clinical implementation across multiple continents. Landmark interconnected procedures, including intercontinental robotic prostatectomies, humanitarian implementations in underserved regions, and expansion of teleproctoring/tele-mentoring networks, showcase a rapidly maturing field. Despite this proliferation, no standardized, procedure-specific surgical consent for telesurgery exists in the medical literature. Current consent processes for robotic surgery, telemedicine, and surgical research fail to address critical elements unique to telesurgery, including latency thresholds, technical redundancies, network architecture, cybersecurity risks, remote surgeon responsibilities, and contingency planning. Our article aims to define the essential elements that should compose a dedicated telesurgery-specific informed consent and to highlight why current consent frameworks are insufficient for this rapidly expanding global practice. This expert-opinion paper synthesizes high-impact literature on consent ethics, digital surgery, telehealth governance, and robotic surgery; evaluates current consents used in pioneering telesurgery programs across North America, South America, Africa, Europe, and Asia; and incorporates multidisciplinary insights from surgery, anesthesiology, biomedical engineering, cybersecurity, telecommunications, ethics, and global health. We identify major gaps in contemporary consent frameworks, including (1) inadequate disclosure of connectivity-related risks; (2) insufficient explanation of data transmission, cybersecurity, and encryption; (3) absence of latency thresholds and safety-based interruption criteria; (4) lack of transparency regarding remote surgeon responsibility and handover protocols; and (5) limited patient education regarding contingency conversion and chain-of-care accountability. We thus propose a 9-component International Telesurgery Consent Framework, providing standardized language and structure for global implementation. The rapid expansion of telesurgery demands an equally rapid evolution in patient-centered informed consent. A dedicated telesurgery consent should become the ethical, medicolegal, and operational standard for any remote robotic clinical procedure. This paper serves as the basis for an international consensus effort involving global leaders in robotic surgery, telehealth, AI-enabled surgery, and digital health regulation.
14 June 2026
Read appraisal →Scientific data
Appendix300: Surgical video and patient metadata of 330 laparoscopic appendectomy cases from five institutions
The limited availability of diverse and representative training data poses a critical barrier to the development of clinically relevant computational tools for intraoperative surgical decision support. Surgical procedures are not routinely recorded, and data annotation requires domain expertise, resulting in a scarcity of open-access surgical video datasets with high-quality annotations. Existing datasets are typically limited to single institutions and specific procedures, such as cholecystectomy, and rarely comprise patient-level metadata like demographic characteristics, disease history, or laboratory parameters. The Appendix300 dataset comprises 330 laparoscopic surgery recordings, including 325 full-length laparoscopic appendectomies and 5 control recordings from non-appendectomy procedures in pediatric and adult patients treated at five German centers. The dataset includes patient-level clinical metadata (demographics, medical history, clinical symptoms, preoperative laboratory parameters, and histopathologic findings, as well as standardized expert annotations of the laparoscopic grade of appendicitis). This dataset enables novel validation tasks for computer vision in laparoscopic surgery and facilitates simulation of decentralized learning approaches, overall enhancing the breadth and translational relevance of AI-based surgical video analysis.
14 June 2026
Read appraisal →JMIR formative research
An Interactive Digital Dashboard for Patient Monitoring and Management in a Continuity of Care Centre: Development and Preliminary Usability Evaluation Study
BACKGROUND: Clinical dashboards are becoming important tools for managing and monitoring hospitalized patients across different wards. Moreover, careful attention to design, usability, and user interaction is essential for developing effective support tools for clinicians. OBJECTIVE: This study aimed to describe the development, implementation, and preliminary evaluation of an interactive dashboard for patient monitoring and management in the Continuity of Care Centre (CCC). METHODS: We developed a dashboard according to clinicians' requests and the daily workflow of case managers. First, a CCC Data Mart was created to collect all patients' information automatically extracted from the hospital's data warehouse. However, case managers had the possibility to enter additional patient information in the dashboard using a dedicated form. Moreover, CCC physicians, nurses, and administrative staff were surveyed using 2 validated questionnaires, the System Usability Scale and the Questionnaire for User Interaction Satisfaction. The Situation Awareness Index was proposed to evaluate user awareness and task efficiency. RESULTS: The first version of the CCC dashboard presented 4 panels with different types of information, both on the individual patient and on metrics related to the overall patient population. The first panel focused on patients' data, such as demographic factors, admission, transfer, discharge wards (and their dates), etc. The importance of this panel was the possibility of viewing information collected from different sources within a single interface. The other 3 panels displayed different key performance indicators for the overall patient population and presented data both in the form of tables in the second panel and graphs in the third and fourth panels. After 3 months of daily use, a total of 15 participants, 10 nurses, 2 administrative staff members, and 3 physicians, were recruited for the dashboard evaluation. The average System Usability Scale score of the dashboard was 61.5 (SD 15.7) points, which indicates "OK to good" usability, and the median score obtained with the Questionnaire for User Interaction Satisfaction was 5.77 (IQR 4-7), with the highest results in usability (mean 6.33, SD 0) and learning (mean 6.01, SD 0.39). The overall Situation Awareness Index score was 4 points, with the highest score in "familiarity of dashboard" (mean 4.73, SD 1.66 points) and "arousal support" (mean 4.6, SD 1.8 points). CONCLUSIONS: We developed an interactive dashboard for patient monitoring and management, with positive evaluations from users across different questionnaires.
14 June 2026
Read appraisal →JMIR mental health
Prevalence and Predictors of Self-Reported Adverse Experiences in Digital Meditation Training: 2 Randomized Controlled Trials
BACKGROUND: Digital meditation-based interventions (MBIs) reach vast global audiences with millions of active users, yet concerns persist about the frequency and nature of adverse experiences (ie, AExs) occurring during meditation training. Some researchers have argued that AExs are substantially underdetected and reflect iatrogenic harm caused by meditation (ie, adverse effects [AEfs]). Others contend that these experiences largely reflect common stressors that would be experienced without meditation. These competing perspectives underscore the need for further research, particularly in the context of digital MBIs, the most widely used form of meditation training. OBJECTIVE: This study examined the prevalence, predictors, and subjective evaluations of AExs during a digital MBI and tested whether reported experiences may be caused by meditation practice via comparisons between meditation-exposed and nonexposed participants. METHODS: Data were drawn from 2 trials of the Healthy Minds Program. Exploratory study 1 (n=315) consisted of a sample of distressed US undergraduate students to estimate the prevalence of AExs and identify baseline predictors. Preregistered confirmatory study 2 (n=594) sampled distressed US adults from all 50 states to replicate findings from study 1 and to examine participants' subjective evaluations of AExs. Study 2 additionally compared AEx rates between participants who did and did not complete guided meditations to assess whether AExs could be caused by meditation exposure. Study 3 (n=87) used qualitative methods to analyze study 1 participants' responses to an open-ended question regarding their strategies for coping with AExs. RESULTS: In studies 1 and 2, 27.9% (88/315) and 10.1% (40/396) of participants, respectively, reported at least one AEx during the study period, with 6.7% (21/315) and 3% (12/396) reporting functional impairment, largely aligning with previous research. Critically, in study 2, rates of AExs did not significantly differ between participants who did and did not complete guided meditations, suggesting that these experiences were not caused by meditation practice. Higher baseline depression, anxiety, loneliness, experiential avoidance, and perceived barriers to meditation predicted more frequent AExs. In studies 1 and 2, 89.8% (79/88) and 90% (36/40) of participants who reported AExs, respectively, indicated that they were glad to have learned to meditate. Qualitative analyses showed that participants used diverse coping strategies, often using skills learned through the Healthy Minds Program. CONCLUSIONS: AExs were relatively common but occurred at comparable rates among participants who did and did not meditate, challenging claims that such experiences were caused by meditation practice in distressed individuals. Although a small subset of participants reported some degree of functional impairment, most evaluated their AExs as tolerable and described their overall MBI experience as positive. Together, these findings highlight the importance of distinguishing AExs that likely reflect epiphenomena of preexisting distress or symptoms from iatrogenic harm attributable to MBIs.
14 June 2026
Read appraisal →Journal of medical Internet research
Telehealth Usability, Engagement Patterns, and Technical Infrastructure in Managing Noncommunicable Diseases Among Health Care Professionals in Brazil, Ghana, Honduras, and the United Kingdom: Multinational Cross-Sectional Study
BACKGROUND: Noncommunicable diseases (NCDs) account for over 70% of global deaths, with hypertension and diabetes serving as major contributors. The COVID-19 pandemic disrupted traditional health care services for NCDs and highlighted telehealth as a crucial alternative. Telehealth-encompassing synchronous and asynchronous electronic communication to deliver clinical services remotely-can overcome geographical barriers and enhance patient engagement. However, telehealth usability among health care professionals (HCPs) remains under-studied across low-, middle-, and high-income countries. OBJECTIVE: This study aimed to examine which telehealth engagement patterns, technical infrastructure factors, and user profiles were most strongly associated with usability among HCPs and to descriptively compare these across 4 diverse countries: Brazil (high- to middle-income country), Ghana (low- to middle-income country), Honduras (low- to middle-income country), and the United Kingdom (high-income country). METHODS: A multinational cross-sectional survey was conducted with 290 HCPs across 4 countries. Participants completed the System Usability Scale and provided data on telehealth engagement (eg, frequency, duration, and number of systems used), technical infrastructure (connection stability and support satisfaction), and their user profile (demographics, job role, and training received). Descriptive statistics summarized these patterns and usability scores. Multiple linear regression with bootstrap-based sensitivity analyses identified factors associated with telehealth usability. Given the nonprobability design, no formal inferential comparisons were made between countries. Instead, observed patterns were reported descriptively. RESULTS: Higher telehealth usability scores were associated with greater connection stability (b=5.06, 95% CI 3.06-7.05), higher satisfaction with online support information (b=5.02, 95% CI 3.27-6.75), more frequent use (b=3.05, 95% CI 1.36-4.73), longer duration of use (b=1.59, 95% CI 0.49-2.68), and being a physician by profession (b=3.82, 95% CI 0.23-7.40). Average usability scores were highest among users in Ghana (mean 79.75, SD 14.19) and the United Kingdom (mean 79.00, SD 14.71), followed by Brazil (mean 72.01, SD 14.62) and Honduras (mean 63.09, SD 15.57). According to System Usability Scale guidelines, scores corresponded to "good" usability for users in Ghana, the United Kingdom, and Brazil and were below the "good" threshold for users in Honduras. While most users in Ghana (97/111, 87.4%), Honduras (31/38, 81.6%), and Brazil (57/80, 70.4%) reported using only 1 telehealth system, two-thirds of UK users (40/60, 66.7%) reported using 2 or more systems. User profiles also varied; prepandemic use was highest in Ghana (84/111, 75.7%) and lowest in Honduras (7/38, 18.4%). Other engagement patterns across countries were reported. CONCLUSIONS: Telehealth usability is driven by technical infrastructure reliability, a robust online support infrastructure, and an "experience effect" from frequent and long-term engagement. Descriptive differences in engagement patterns and infrastructure highlight the need for tailored strategies to address setting-specific challenges. These are essential to optimize telehealth integration and improve health care outcomes for patients with NCDs worldwide.
14 June 2026
Read appraisal →Scientific reports
Performance of GPT-based large language models in hepatocellular carcinoma stratification: liver function assessment, BCLC staging, and treatment recommendations.
Large language models (LLMs) like GPT have been proposed to support complex clinical decision-making. This study evaluated the performance of GPT-based LLM in analyzing clinical, radiological, and laboratory data from patients with hepatocellular carcinoma (HCC) to assess liver function, assign BCLC stage, and recommend treatment. Data from 106 HCC patients (82% male, median age 65 [22-86]) were compiled into anonymized integrated reports. Four GPT-versions (4, o1, o3, 5.4) were prompted-using both short and long instructions-to calculate MELD, ALBI, and Child-Pugh scores, assign BCLC stage, and generate treatment recommendations based on current guidelines. Outputs were compared to expert consensus and tumor board decisions. Errors were categorized by type and source. Time and cost analyses compared GPT to clinical staff. All GPT versions achieved high accuracy (> 85%) in liver function assessment, with MELD calculation being the most error-prone. BCLC staging accuracy ranged from 46.2% (version 4) to 84.0% (o3), with misclassification of radiological reports as the main error source. Reasoning-optimized models (o1, o3) performed best for treatment recommendations, achieving an overall accuracy (correct suggestions and acceptable alternatives) of up to 90.6%. In 9-14% of cases, GPT suggestions were retrospectively more guideline-concordant than tumor board decisions. GPT processing was significantly faster and reduced costs by approximately 300- to 1300-fold compared to clinical staff. GPT-based LLMs show potential as decision-support tools for liver function assessment, BCLC staging, and treatment guidance in HCC. Particularly with reasoning-optimized models and detailed prompting, LLMs may serve as valuable adjuncts in multidisciplinary HCC workflows. However, a non-negligible error rate requires expert oversight and further model refinement.
14 June 2026
Read appraisal →BMC geriatrics
The association between smart elderly care and subjective well-being among older adults in China: the mediating roles of health status and social activities
BACKGROUND: Under the initiative of China's "Internet+" strategy, smart elderly care reflects the government's capacity to bridge the digital divide and provide inclusive care services for older adults. Though studies have identified positive associations between smart elderly care and health outcomes, empirical research examining its impact on older adults' subjective well-being remains limited. METHODS: This study utilizes data from the 2021 Chinese Longitudinal Healthy Longevity Survey (CLHLS, N = 8,950), which includes multifaceted information on individuals aged 65 and above, covering their personal characteristics, health status, family information, and community elderly care services. A logit model is employed to investigate the association between smart elderly care and SWB. The mediating effects are tested using the bootstrap resampling method. To address endogeneity concerns, instrumental variable (IV) approaches including IV-Probit and 2SLS are applied as robustness checks. RESULTS: The results reveal a positive correlation between smart elderly care and older adults' subjective well-being (β = 0.101, p < 0.01). This association is partially mediated by both health status (proportion mediated: 38.47%) and social activities (proportion mediated: 2.65%). Heterogeneity analyses show that this positive association holds consistently across both rural and urban older adults, and is more pronounced among those with lower digital literacy. CONCLUSIONS: This study challenges the prevailing assumption that the "digital divide inevitably isolates the older adults from participating in a digitized society." By integrating digital technologies into older adults care services, we demonstrate that smart elderly care is positively associated with bridging technological barriers and addressing the unmet needs of aging populations. These findings further propose a synergistic mechanism-policy innovation, technological adaptation, and service optimization, to build an inclusive older adults care ecosystem, offering evidence-based insights for policymakers in aging societies to balance equity and efficiency in digital transformation.
14 June 2026
Read appraisal →JMIR mHealth and uHealth
WeChat-Delivered Mobile Medical Nutrition Therapy Intervention in Gestational Diabetes Mellitus: Randomized Controlled Trial
BACKGROUND: Medical nutrition therapy (MNT) serves as the foundational intervention in the clinical management of gestational diabetes mellitus (GDM) management. However, inadequate supportive care often hinders patients' ability to sustain dietary modifications and self-management behaviors, particularly for complex regimens. Flexible online interventions are thus gaining interest as adjuncts to clinical care, with the potential to improve the outcomes of GDM self-management. OBJECTIVE: This study sought to evaluate the benefits of WeChat-delivered medical nutrition therapy (WeMNT), a WeChat-delivered MNT intervention, for patients with GDM. METHODS: A parallel-group, single-blind randomized controlled trial (with outcome assessor blinding) was implemented at a university hospital's obstetric clinic. Eligible participants were those with a 24- to 28-week singleton pregnancy, a GDM diagnosis confirmed by the 75 g oral glucose tolerance test, no requirement for insulin therapy, and the ability to use a smartphone and WeChat, as well as to communicate in Chinese. Participants were randomized 1:1 to the intervention (n=47, 50%) or control group (n=47, 50%) using a random number table. The WeMNT intervention was designed per the behavior change wheel framework: (1) analyze capability-, opportunity-, and motivation-based barriers related to MNT adherence; (2) formulate targeted intervention functions; and (3) select evidence-based behavior change techniques. Three WeChat mini-programs served as the core delivery platform to implement the multifaceted intervention functions. The primary outcomes measured in this study included fasting blood glucose, 2-hour postprandial glucose (2hPG), hemoglobin A1c (HbA1c), and gestational weight gain (GWG). The secondary outcomes assessed included obstetric complications and neonatal parameters. Intention-to-treat analysis using adjusted generalized linear mixed models was conducted to evaluate the effects of the intervention. RESULTS: From March 2023 to October 2023, a total of 94 participants were enrolled in this study. Among the participants, 88 (93.62%) individuals (mean age 32.18, SD 5.04 years) successfully completed the study, 44 (46.81%) in the intervention group and 44 (46.81%) in the control group. Approximately 68.18% (30/44) of the participants in the intervention group demonstrated sustained adherence, as expected, adhered to the diet plan ≥60% of the days. Compared with the control group, the intervention group showed a significantly greater decrease in GWG over time (group×time interaction: β=-1.96, 95% CI -3.58 to -0.34; P=.02), with no significant effects on fasting blood glucose. Conversely, significant decreases in 2hPG (β=-0.12, 95% CI -0.19 to -0.04; P<.001) and HbA1c (β=-0.49, 95% CI -0.74 to -0.23; P<.001) were noted in the intervention group. No unexpected maternal adverse events occurred, and no significant effects on other health outcomes were detected. CONCLUSIONS: As a WeChat-delivered, behavior change wheel-informed intervention, WeMNT successfully reduced 2hPG, HbA1c, and GWG in GDM management. Its integrated self-management model yields robust evidence, validating this patient-centered tool as a viable option for clinical adoption.
14 June 2026
Read appraisal →Journal of medical Internet research
Experiences of Home-Dwelling Older Adults and Their Family Caregivers With Digital Health Services: Qualitative Study
BACKGROUND: As the population ages, demand for health services among older adults and caregivers increases. Digital health services help meet this demand. However, acceptance and experiences differ between older adults and caregivers. OBJECTIVE: This study aims to explore the specific experiences of home-dwelling older adults and their caregivers with digital health services, identify their willingness to use these services and the factors influencing their adoption, and identify their service needs to inform the subsequent design and optimization of digital health products and services. METHODS: From December 2023 to February 2024, a descriptive qualitative study was conducted. Researchers used purposive and maximum variation sampling to recruit 18 home-dwelling older adults and 17 family caregivers, including 10 matched dyads. Participants came from 7 community health service centers and a tertiary hospital in Hefei, China. Open-ended, semistructured, face-to-face interviews were conducted separately. Data were analyzed using conventional content analysis. RESULTS: Four main themes emerged. The first were the application characteristics, including usage situations and preferred functions. The second theme described specific service experiences, both positive and negative. The third was the influencing factors. Promoting factors included health needs, user experience, and subjective norms. Obstructive factors differed between groups and included low digital literacy, weak economic foundation, service security concerns, and lack of time. The fourth theme covered suggestions and expectations for improvement. Older adults appreciated the advantages but often had suboptimal experiences due to complex designs and threats to personal dignity. Conversely, caregivers valued efficiency but reported low use, hindered by time constraints and concerns about privacy and safety. CONCLUSIONS: This study reveals a critical gap between the potential and actual use of digital health services. Older adults face barriers with usability and digital literacy. Caregivers struggle to integrate these services and have trust issues. Future optimization requires action at several levels. Government support is needed through policies, authoritative platforms, and subsidies. Service providers should offer age-friendly, personalized design, and continuous support. Social support systems are also essential, such as digital reciprocity from family and peer help. These strategies are critical for enhancing user experience, bridging the digital divide, and enabling both older adults and their caregivers to fully benefit from digital health technologies.
14 June 2026
Read appraisal →BMJ open
Barriers and facilitators to digital health tool adoption for hypertension management: systematic review of qualitative studies
OBJECTIVES: Digital health interventions (DHIs) show considerable promise in supporting hypertension self-management by promoting preventative care and self-monitoring. While their efficacy is increasingly evident, the long-term uptake, acceptance and sustained engagement with these tools are frequently challenged by issues such as usability, trust and varying user experiences. This review aims to synthesise qualitative evidence to identify barriers and facilitators and the key factors that impact the adoption, acceptance and engagement with DHIs for hypertension self-management. DESIGN: Systematic review of qualitative literature using thematic analysis following Cochrane's qualitative and implementation methods guidance. DATA SOURCES: PubMed, PsycInfo, Web of Science and the Cochrane Library were searched in February 2025. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: The searches included relevant qualitative and mixed-methods studies on the use of digital devices for hypertension management, which described the barriers and facilitators associated with these tools. We included studies published from 2015 to 2025 to capture relevant evidence. Only studies published in English with a qualitative approach were included. RESULTS: From an initial 10 943 identified publications, 15 met our inclusion criteria, primarily originating from Europe and the USA, exploring diverse racial and ethnic group experiences. Our thematic synthesis revealed 7 analytical and 22 descriptive themes detailing barriers and facilitators encountered by patients with hypertension, healthcare providers (HCPs) and caregivers. These themes covered technology utilisation, design components, linguistic and cultural relevance, healthcare factors, trust and credibility and interpersonal interactions. CONCLUSION: Our analysis underscores that factors such as the usability, design and relevance of social support profoundly influence the uptake and acceptance of DHIs in hypertension self-management among patients, caregivers and HCPs. PROSPERO REGISTRATION NUMBER: CRD42023480389.
14 June 2026
Read appraisal →Family practice
Current state of electronic problems lists in primary care: a rapid scoping review
BACKGROUND: Electronic problem lists (PLs) are central to the problem-oriented medical record and increasingly underpin clinical decision support (CDS), interoperability, and emerging artificial intelligence applications in primary care. However, persistent concerns regarding PL accuracy, completeness, and governance limit their clinical value. OBJECTIVE: To update the foundational framework proposed by Hodge and Narus by synthesizing contemporary evidence on electronic PLs in primary care and primary care-relevant settings. METHODS: We conducted a rapid scoping review following Joanna Briggs Institute guidance and PRISMA-ScR reporting standards. MEDLINE (Ovid) was searched for peer-reviewed studies published between March 2016 and May 2026. Eligible studies examined digital PLs or extractable PL-related practices relevant to primary care. Findings were synthesized using a hybrid inductive-deductive approach and mapped to the seven Hodge and Narus themes. RESULTS: A total of 103 studies were included. Across settings, PLs were widely recognized as foundational for longitudinal care, safety, and coordination. However, incomplete, outdated, or inconsistently maintained PLs remained common. Persistent challenges included unclear ownership, workflow misalignment, and variability in what constitutes a "problem." Emerging technologies-including CDS, natural language processing, and machine learning-were increasingly used to support PL generation, curation, reconciliation, and organization, with greatest benefit when embedded within clinician-led workflows. CONCLUSION: Despite technological advances, the clinical value of electronic PLs in primary care continues to depend on governance, workflow integration, and shared accountability. Updating the Hodge and Narus framework highlights the need for sociotechnical approaches to PL improvement as PLs become integral to CDS and artificial intelligence-enabled care. Problem lists (PL) are a key part of electronic health records. They provide clinicians with a quick overview of a patient's important health issues—such as long-term conditions, past diagnoses, and current concerns—and help support safe and informed care. When PLs are accurate and up to date, they can improve communication and decision-making. However, in everyday clinical practice, many PLs are incomplete, outdated, or difficult to maintain. As electronic health records are increasingly used to support clinical decision support tools and new digital technologies, the quality of PLs has become more important than ever. Poor-quality PLs can lead to missed information, duplicated work, or unsafe care decisions. To better understand how PLs are working in modern healthcare, we conducted a rapid scoping review of research published between 2016 and 2026. We examined studies that looked at how PLs are used, how accurate they are, what challenges clinicians face in keeping them updated, and what solutions have been proposed. In total, 103 studies were included. Across these studies, several common problems were identified. Important health conditions were often missing or out of date, and clinicians were frequently unsure who was responsible for updating the PL. Time pressures and busy clinical workflows made regular maintenance difficult. Some studies explored new digital tools, such as automated text analysis, to help identify or update problems, but these approaches are still developing and require careful evaluation. Improving PLs will require clearer standards, shared responsibility among healthcare teams, and EHRs that make updating information easier. Thoughtful use of new technologies may also help reduce clinician workload while improving patient safety. This review highlights where PLs are working well and where improvements are still needed, offering guidance for future research, technology design, and improvements in primary care practice.
14 June 2026
Read appraisal →Journal of medical Internet research
Patient Cognitive Bias in Large Language Model-Supported Health Consultations: Simulation-Based Comparative Study
BACKGROUND: Large language models (LLMs) are increasingly used by patients for health information and preliminary medical advice. In patient-facing consultations, users may present explicitly stated diagnostic preferences or symptom narratives emphasizing a preferred explanation. Such cognitively biased input constrains the diagnostic context available to the model and may systematically steer its reasoning during interactive LLM-supported health consultations. OBJECTIVE: This study aimed to quantify the impact of patient cognitive bias on LLM diagnostic performance in multiturn consultations, assess the effectiveness of prompt-based mitigation strategies and decoding temperature adjustment, and evaluate a dual-system framework for improving robustness under biased interaction. METHODS: We developed a simulated patient agent to generate both unbiased and cognitively biased consultations using 1273 medical question answering dataset United States Medical Licensing Examination cases. Six widely used LLMs of varying capacities were evaluated through 3-round, multiturn dialogues, after which each model produced a final diagnostic judgment based on the complete consultation record. Diagnostic accuracy was the primary outcome. Secondary outcomes included bias-induced accuracy decline (absolute reduction in accuracy under biased vs standard consultations) and bias-influenced error proportion (proportion of incorrect responses aligned with the patient's preferred but incorrect diagnosis). Three prompt-based mitigation strategies and 4 decoding temperature settings were tested. In addition, a dual-system framework was evaluated, in which a conversational foundation LLM conducted patient interaction and history taking (System 1), while a reasoning-oriented LLM (o1-mini) generated the final diagnostic judgment (System 2). In the foundation-only condition, the same LLM performed both interaction and diagnosis. RESULTS: Across all 6 evaluated models, cognitively biased consultations led to marked diagnostic accuracy declines of approximately 7 to 39 percentage points compared with standard multiturn consultations, whereas static single-response tests and standard consultations showed comparable accuracy. Larger deteriorations were observed in lower-capacity models, with some approaching random-guess performance under bias. Errors were frequently aligned with patient bias, with bias-influenced error proportion exceeding one-third across models, indicating systematic conformity rather than random error. Prompt-based mitigation strategies and decoding temperature reduction yielded limited and inconsistent improvements and did not reliably prevent bias-induced performance loss. By contrast, the dual-system framework substantially improved diagnostic accuracy under biased conditions, producing gains of approximately 10 to 39 percentage points across most models and recovering a large proportion of the performance lost due to bias, particularly in lower-capacity systems. CONCLUSIONS: Patient-driven cognitive bias represents an underrecognized behavioral risk in LLM-supported health consultations. Common mitigation approaches, such as prompt engineering or decoding parameter adjustment, provide limited resilience. Explicitly separating conversational interaction from deliberative diagnostic reasoning through a dual-system framework enables more robust diagnostic performance under biased input while potentially preserving patient-facing dialogue fluency by retaining the foundation LLM as the conversational component, offering a scalable design strategy for safer medical AI systems.
14 June 2026
Read appraisal →JMIR research protocols
Effectiveness of Guideline-Based Clinical Decision Support Systems: Protocol for a Systematic Review
BACKGROUND: Clinical guidelines (CGs) standardize care through evidence-based recommendations, while clinical decision support systems (CDSS) can assist in applying these guidelines to individual patients. The scientific basis for the decisions offered by decision support systems is often not explicitly stated or not clearly specified in the literature on CDSS. Therefore, a systematic examination of the literature is needed to map the current state of CDSS, with a particular focus on the integration of CGs. OBJECTIVE: This study aims to systematically collect, describe, and synthesize evidence of randomized controlled studies of interventions using CDSS with a well-defined integration of evidence-based CGs and evaluating direct medical outcomes. METHODS: This systematic review adheres to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklists. The eligibility criteria for this review are defined using the patient, intervention, control, outcome, and study design framework, including studies involving patients with any medical condition or disease. Study interventions need to include guideline-based CDSS, encompassing all types of interventions used for treatment. Each intervention must provide a sufficiently accessible technical description, including the types of data and algorithms used for decision-support procedures. The guidelines used within these CDSS interventions must be derived from a clearly defined, evidence-based guideline development process published by a discernible guideline-producing body. Studies must use a randomized controlled study design. Only studies evaluating the effectiveness of the interventions on direct medical outcomes are included. Web of Science, including MEDLINE, and Scopus will be searched with search expressions aligned with the eligibility criteria. RESULTS: On August 11, 2022, the initial search was conducted on Web of Science and Scopus. From a total of 6203 records, 1347 were removed prior to screening as duplicates, 2506 records were excluded during the first screening step, and 2291 were excluded during the second step. Next, 41 papers were excluded based on full-text review, and 18 papers were finally included in the review following this initial search. This review explores whether CDSS based on CGs can improve clinical outcomes, although their effectiveness may vary depending on various factors. Potential limitations, such as high study heterogeneity, have already been identified. An update of the review has been started in April 2025. CONCLUSIONS: To our knowledge, this is the first rigorous systematic review on the effectiveness of guideline-based decision support systems in which the technical integration and algorithmic embedding of CGs have been described or can be inferred from secondary literature. With this review, we aim to address this gap by providing a detailed analysis of existing research and identifying best practices, challenges, and areas for future investigation.
14 June 2026
Read appraisal →Journal of medical Internet research
Uptake of Clinical Decision Support Systems Among Health Care Professionals in Six European Countries and the United States: Cross-Sectional Survey Within the I-CARE4OLD Project
BACKGROUND: The use of Clinical Decision Support Systems (CDSS), such as clinical decision rules, algorithms, or machine learning-based applications, has gained attention in recent years. However, their adoption and effectiveness may vary across different health care systems and settings. For a CDSS to be adopted, it must effectively address the practical issues encountered by professionals; however, little research has been done to identify these needs and requirements. OBJECTIVE: This study aims to describe and compare the current use of various decision-support and prediction tools in long-term care for older people across health professionals from 6 European countries and the United States. METHODS: This study analyzed survey data from a CDSS pilot study in a purposive sample of health professionals working with older adults with complex chronic conditions from six European countries and the United States. The survey included participants' general background information, their current use of decision support tools, and their attitudes on the potential benefits of CDSS. About 20 participants were sampled per country. Closed responses were analyzed using correlation coefficients and regression models, while open-ended responses were clustered in a qualitative manner, categorizing each response. RESULTS: A total of 151 professionals (mean age 45.5, SD 11.6 years, 71.5%, 108/151 female) participated in the pilot study. Most participants were physicians (85/151, 56.3%) or nurses (57/151, 37.7%). About 51% (78/151) of the participants reported using CDSS, while 22.4% (34/151) used predictive CDSS, showing important variation across samples from the seven countries. The regression model for comfort with technology showed a positive association for openness to new technologies (β=0.622; P<.001), although an inverse significant association was found for age (β=-0.022; P<.001). No significant associations were found for the actual use of CDSS. Participants reported using CDSS mainly for diagnostic purposes or for guideline implementation, not aimed at prognostic information. In contrast, examples of prognostic tools were most frequently mentioned by respondents as being valuable improvements to clinical practice. CONCLUSIONS: While some countries' samples reported well-integrated digital health infrastructures and higher CDSS adoption rates, others still face challenges in implementing these. However, we found multiple examples of emerging tools, and at the same time, an important demand for predictive CDSS. Our findings highlight the need for improvement of current CDSS implementation and both development and implementation of particularly predictive CDSS.
14 June 2026
Read appraisal →Journal of medical Internet research
Telehealth Scale and Artificial Intelligence Adoption Tiers Across Clinical and Operational Domains in US Hospitals: Cross-Sectional Study
BACKGROUND: Telehealth expansion and artificial intelligence (AI) adoption are often described as parallel dimensions of health system digital transformation. However, whether telehealth scale is associated with hospital AI adoption and whether this relationship varies across hospital settings remain unclear. OBJECTIVE: This study examined the association of telehealth scale with clinical and operational AI adoption tiers in US hospitals and assessed whether these patterns differed by telehealth reporting behavior and geography. METHODS: This cross-sectional study included 6173 US acute care hospitals using linked 2024 American Hospital Association Annual Survey and Information Technology Supplement data and 2023 Healthcare Cost Report Information System data. Telehealth scale was parameterized using log-transformed telehealth volume, a telehealth nonreporting indicator, and a reported-zero telehealth indicator. Clinical and operational AI adoption tiers were derived from hospital-reported AI capability items and classified into 3 tiers. Both outcomes were modeled using multioutput gradient-boosted tree classifiers, and model behavior was interpreted using Shapley additive explanations, partial dependence plots, and stratified analyses by the Core-Based Statistical Area category. RESULTS: Telehealth volume was the strongest predictor of both clinical and operational AI adoption tiers and had a larger contribution to the clinical AI model. Telehealth nonreporting was common, occurring in 57% (3521/6173) of hospitals, and was concentrated among hospitals in the lowest clinical AI adoption tier, accounting for 91.4% (3145/3441) of hospitals with no reported clinical AI adoption. Higher telehealth volume was associated with a steep increase in predicted clinical AI adoption tiers at lower telehealth volumes, followed by a plateau at higher volumes. At similar telehealth volumes, rural hospitals showed weaker telehealth-attributed contributions to predicted clinical AI adoption tiers than metropolitan hospitals. Supplementary analyses suggested that telehealth reporting status and telehealth intensity reflected related but distinct structural processes. CONCLUSIONS: Telehealth scale was strongly associated with hospital AI adoption tiers, especially clinical AI adoption tiers. These findings suggest that telehealth capacity may serve as a practical hospital-level marker of broader digital readiness for AI adoption, but the cross-sectional design does not establish whether telehealth expansion precedes or causes AI adoption. Hospitals with telehealth nonreporting and rural hospitals may face additional structural barriers that limit the translation of digital capacity into AI maturity. Policies to reduce inequities in hospital AI adoption may therefore need to pair telehealth expansion with implementation support, interoperability capacity, and organizational resources.
14 June 2026
Read appraisal →JMIR public health and surveillance
Digital Health Center App for Community and National Malaria Surveillance in Cambodia: Implementation Case Study
BACKGROUND: By 2015, the emergence and dissemination of multidrug-resistant Plasmodium falciparum in the Greater Mekong Subregion threatened regional and global malaria control efforts. In response, Greater Mekong Subregion countries committed to malaria elimination by 2030, with strengthened surveillance as a strategic pillar. In 2017, Cambodia introduced an elimination-oriented digital Malaria Information System (MIS). Its health center app enables real-time, geo-located, case-based malaria reporting across primary health centers, and is fully integrated with the MIS. OBJECTIVE: This study aimed to evaluate the real-world national implementation of Cambodia's Android-based health center app, considering coverage, fidelity, timeliness, and data use, and their effects on malaria surveillance performance, case management, programmatic response, and public health outcomes. METHODS: System performance and public health use were assessed using system-generated metadata, national surveillance data, and user surveys. Operational indicators included technical performance, data completeness, and reporting timeliness, alongside surveillance outcomes such as case notification, classification, reactive case detection, and foci investigation. Nationwide user experience was measured via a survey of 761 health centers across 21 provinces, with in-depth structured surveys at 9 health centers in 3 provinces. Descriptive analyses evaluated system functionality, contribution to malaria surveillance and response, and usability among frontline health workers. RESULTS: The health center app demonstrated strong technical performance, with rapid loading and resilient data transmission under low-bandwidth conditions, supporting reliable reporting in resource-constrained settings. Integrated real-time dashboards provided analytics for case management, surveillance monitoring, risk stratification, and targeted public health interventions. Data completeness remained high (99%, 89/90 fields in 2024), demonstrating consistent routine use even as case incidence declined. Between January 1, 2025, and July 31, 2025, 69 malaria cases were reported nationally (23 locally acquired, 7 domestically imported, and 39 internationally imported). Of these, 95.7% (66/69) were notified and classified within 1 day. Reactive case detection was completed within 3 days for all 21 eligible cases, and 16 of 19 eligible foci received a response within 7 days, indicating strong operational responsiveness. User surveys showed 96.3% (733/761) of health centers were satisfied or very satisfied, 90.1% (686/761) reported rare or no technical issues, and 91.7% (698/761) found the app easy to navigate. Operational challenges included limited internet connectivity, transport to remote areas, and electricity interruptions. In-depth surveys confirmed high uptake, confidence in reporting, and routine use of surveillance data, although gaps in local analytical capacity were identified. CONCLUSIONS: Developed and managed locally to enhance sustainability, the MIS drove significant reductions in malaria case incidence, with the health center app contributing timely, complete, structured reporting at the point of care. Public health responses were facilitated by real-time analysis, targeted interventions, and decentralized decision-making. User engagement was sustained as malaria cases declined, and further enhancements are planned to ensure seamless transition to postelimination surveillance, reducing the risk of malaria reestablishment in Cambodia.
13 June 2026
Read appraisal →JMIR formative research
Digital Health Monitoring and Intervention Suite for Stress in Frontline Nurses: Prospective Cohort Trial
BACKGROUND: Stress among health care workers (HCWs) contributes to burnout, workforce attrition, and adverse patient outcomes. Although virtual reality (VR), psychoeducation, ecological momentary assessments (EMAs), and wearables have independently shown promise in stress research, no integrated digital suite has combined controlled stress induction, intervention delivery, and longitudinal real-world monitoring in HCWs. OBJECTIVE: This study aimed to evaluate the feasibility, engagement, and preliminary effectiveness of a multimodal Digital Health Monitoring and Intervention suite for Stress framework integrating VR simulation, psychoeducation, EMAs, and wearable biometrics. We examined (1) the impact of VR simulation and psychoeducation on stress outcomes and (2) associations between physiological and self-reported mental health outcomes. METHODS: Ninety-nine nurses (mean age 33.7, SD 8.9 yr, 87% female) were enrolled in 2023. We conducted a single-arm prospective cohort study (NCT05923398). Using convenience sampling, participants were recruited from social media advertisements, flyers, and email notices distributed through professional listservs. Participants completed ≥2-week baseline monitoring, a single VR session (2 runs separated by a brief psychoeducation intervention), and 12-week follow-up. In-VR stress was assessed using the Subjective Units of Distress Scale (SUDS) and 4-item Moral Injury Outcome Scale (MIOS-4), with synchronous heart rate variability. Longitudinal outcomes included weekly and biweekly EMAs alongside 70 wearable-derived features. Paired t tests, aligned rank transform ANOVA, and Pearson correlations informed study objectives, with P values adjusted for multiple comparisons. Qualitative content analysis classified emotional responses during and after VR. RESULTS: VR significantly increased subjective stress across checkpoints in both runs, with attenuation in Run B relative to Run A (all P<.001). No significant heart rate variability differences were observed between runs (P=.15). During VR, 92% (91/99) of participants felt stressed, 36% (36/99) reported anxiety or nervousness, and 51% (50/99)-78% (77/99) endorsed anger, guilt, shame, and/or betrayal. Most (59/99, 60%) HCWs returned to an emotional baseline post-VR, although 12% (12/99) reported lingering distress. Immediate reliable improvements in anger, guilt, shame, and/or betrayal occurred for 50% (50/99)-75% (74/99) of participants post intervention. Anxiety (mean -0.53, SD 2.34; P=.03) and stress (mean -3.05, SD 11.35; P=.01) decreased 2 weeks post intervention, but were not sustained at 12 weeks. Increased sleep restlessness was the only wearable feature showing significant changes (mean 2.46%, SD 5.43; Padj<.001). In-VR stress correlated with 12-week real-world stress (SUDS: r=0.57-0.58; MIOS-4: r=0.58-0.61; all P<.01). Data completion exceeded 90%, with 71% achieving full compliance. CONCLUSIONS: This study moves beyond single-tool interventions to demonstrate the feasibility and preliminary effectiveness of an integrated, multimodal stress platform within a single coordinated framework. This trial demonstrates high engagement, short-term symptom responsiveness, ecological validity, and emotional safety. The framework provides a scalable model for proactive stress identification, skills training, and implementation in high-risk occupational settings. Randomized controlled trials are needed to establish sustained efficacy and optimize deployment for real-world implementation.
13 June 2026
Read appraisal →Med (New York, N.Y.)
Rethinking healthcare data interoperability in the age of large language models
Electronic health records contain extensive real-world clinical data, but their effective use is hindered by data heterogeneity and interoperability challenges. Traditional post hoc standardization is costly and labor-intensive and reduces data granularity. Large language models enable analysis of unstructured data without full harmonization but lack precision for some tasks. We propose a hybrid strategy that combines large-language-model-based analysis of legacy data with prospectively standardized data, offering a scalable alternative that challenges the need for retrospective data harmonization and improves interoperability.
13 June 2026
Read appraisal →Current pain and headache reports
Artificial Intelligence in Neuropathic Pain: From Mechanisms to Neuromodulation and Regenerative Strategies
PURPOSE OF REVIEW: Neuropathic pain remains challenging due to its heterogeneous mechanisms and variable treatment response. This narrative review evaluates recent advances in artificial intelligence (AI) and machine learning (ML) for patient phenotyping, treatment selection, outcome prediction, neuromodulation, and regenerative therapies. RECENT FINDINGS: ML-based phenotyping integrates genomic, neuroimaging, sensory, and behavioral data. Multimodal automatic pain assessment is approaching clinical deployment, supported by recent expert consensus. Large language models have been benchmarked against multidisciplinary teams for spinal cord stimulation (SCS) candidate selection. Real-world cohort data confirm sustained 24-month SCS benefit. AI is increasingly applied to regenerative interventions, including pulsed radiofrequency, which is now linked to epigenetic remodeling of dorsal root ganglion pathways. Most studies remain single-center and retrospective. AI may enable mechanism-based stratification, individualized neuromodulation programming, and rational integration of regenerative therapies in the treatment of neuropathic pain. Robust prospective validation, transparent reporting, and ethical safeguards are required before approaching translational maturity in selected settings.
13 June 2026
Read appraisal →JMIR cardio
Experiences of Patients With Atrial Fibrillation Using Technology to Personalize Self-Care Decision-Making: Interpretive Description Study
BACKGROUND: Atrial fibrillation (AF) is the most common sustained heart rhythm disorder and is a challenging chronic disease to manage. Patients' daily self-care decisions are associated with improved AF outcomes, quality of life, and decreased hospital use and cost. However, many patients find these real-world or naturalistic decisions difficult, often because of their inherent complexity and ambiguity, coupled with the uncertainty of AF. Intervention research using technology to support AF self-care has largely emphasized making decisions with clinicians. Patients with AF are increasingly using consumer technology; yet, little is known about the use of technology by patients with AF in independent self-care decision-making. Addressing this gap will facilitate developing interventions that better leverage technology to enhance patients' naturalistic decision-making. OBJECTIVE: This study aimed to explore the experiences of older adult patients in using technology to support self-care decision-making. METHODS: Following an interpretive descriptive qualitative approach, older adult patients with AF were recruited from 3 specialty heart function clinics in a Western Canadian province to participate in 1 of 6 facilitated virtual focus groups for 1.5 hours. Patients were asked about their self-care decision-making since AF diagnosis, their AF-specific technology use and its use in making self-care decisions, their technology motivations, benefits, constraints, and other possibilities for use. Inductive thematic analysis was used to code the transcribed data, moving from open coding to clustering of common codes into categories, looking for patterns of meaning between and across categories to iteratively arrive at main themes and subthemes. RESULTS: Thirty patients (n=15, 50% women) with AF (mean age, 73, SD 5.7 years; range 63-85 years) participated in the focus groups. Participants' experiences of using technology to make daily self-care decisions were highly variable but centered on its personalized use to meet their individualized needs, preferences, and life context. The personalizing process of technology use in decision-making was characterized by three themes: (1) beginning technology use in their own times and ways, during their AF trajectory-pre-, at the time, at some point AF post diagnosis, and could be either self-initiated and/or provider recommended or influenced; (2) developing patterns of AF self-care decision-making using technology, including establishing their personal baseline, keeping out of the danger zone, watchful waiting, and seeking decision-making support; and (3) finding the place for technology in normalizing daily life, either settling on or limiting its use to normalize life. CONCLUSIONS: Findings expand understandings of naturalistic decision-making by elucidating the personalized process of technology use in AF self-care.
13 June 2026
Read appraisal →Biomedical physics & engineering express
Enhancing brain tumor classification with a simplified CNN through hyperparameter optimization
Brain tumors remain a major public health challenge because of their high mortality rate and the need for timely and accurate diagnosis. Magnetic resonance imaging (MRI) is widely used for brain tumor assessment, yet conventional diagnostic workflows still depend largely on the subjective interpretation of radiologists, which may lead to inter-observer variability. In this context, deep learning approaches-particularly convolutional neural networks (CNNs)-have gained increasing attention for automated medical image analysis, as they are capable of learning discriminative features directly from imaging data and improving classification accuracy. Nevertheless, the performance of CNN-based models is strongly influenced by the choice of hyperparameters, whose optimization remains a non-trivial task due to complex and non-linear interactions within the network.This study investigates the impact of different hyperparameter optimization strategies on the performance of a simplified CNN model for brain tumor classification from MRI images. Three widely used optimization methods, Genetic Algorithms (GA), Bayesian Optimization (BO), and HyperBand-are considered and systematically compared. To emphasize efficiency and practical applicability, the CNN architecture is deliberately kept lightweight, aiming to reduce computational complexity while preserving sufficient modeling capacity. Experiments are carried out on a publicly available brain MRI dataset that includes glioma, meningioma, pituitary tumor, and healthy subjects.To account for performance variability and ensure statistical reliability, each optimized model is evaluated over ten independent runs using different random initializations. The reported results therefore include both the mean performance values and the corresponding standard deviations. The experimental analysis shows that the GA-based approach achieves a mean accuracy of 96.41%±0.36%, while BO improves this performance to 97.37%±0.30%. Among the evaluated strategies, HyperBand yields the highest average accuracy of 97.73%±0.48%, indicating its ability to consistently identify effective hyperparameter configurations for the proposed CNN architecture.
13 June 2026
Read appraisal →PloS one
Mumtathil: Automatic PDPL compliance identification system of Arabic privacy policies documents.
In the digital era, the extensive collection and use of personal data by websites and applications has prompted various countries to implement data protection laws. The Kingdom of Saudi Arabia introduced the Personal Data Protection Law (PDPL) to regulate the handling of personal data. However, manually assessing compliance with these laws is time-consuming, and costly. This paper presents an automated system named Mumtathil (مُمتثِل) that evaluates the compliance of Arabic privacy policies with the PDPL using multi-label classification (MLC) techniques. Our experiments demonstrated that the Support Vector Machine (SVM) outperformed other machine learning models and transformer-based models like CamelBERT and AraBERT, achieving an F-score of 92%. The system addresses dataset imbalance by augmenting the Saudi Privacy Policy Dataset with recent privacy policies and synthetic data generated using ChatGPT. The Mumtathil tool simplifies compliance assessment, reducing time, cost, and effort while enhancing transparency and data protection in the digital landscape.
12 June 2026
Read appraisal →Journal of medical Internet research
Text Messaging Support for Patients Diagnosed With Impaired Glucose Tolerance During Pregnancy: Nonrandomized Pre-Post Implementation Study Assessing Impact on Postpartum Transitions of Care
BACKGROUND: Patients with impaired glucose tolerance (IGT) identified during pregnancy who do not develop gestational diabetes mellitus (GDM) often do not receive additional interventions for their long-term metabolic risks. OBJECTIVE: This nonrandomized pre-post implementation study reports the design process and initial program evaluation for Better Follow-up of Impaired Glucose Tolerance (BRIDGE), a 12-week text-based postpartum support program promoting hemoglobin A1c (HbA1c) completion and primary care provider (PCP) visit scheduling for patients diagnosed with IGT during pregnancy, assessing improvement in desired postpartum transition milestones. The 19-month program was divided into 2 arms lasting 9.5 months each, BRIDGE- (SMS text messaging support alone; October 2021-July 2022) and BRIDGE+ (SMS text messaging and IGT-focused postpartum visit; July 2022-April 2023). We aimed to assess whether BRIDGE improved desired postpartum transition milestones. METHODS: Patients were eligible for BRIDGE if they received prenatal care at the study site (a northeastern US academic tertiary care center), were diagnosed with IGT during pregnancy, never developed GDM, and could receive English text messages. We performed a program evaluation using a pre/postimplementation design, comparing outcomes for the BRIDGE population to a 19-month historical population. Primary outcomes were (1) completion of HbA1c testing by 1 year postpartum and (2) PCP visit scheduling by 12 weeks postpartum. A comparative analysis between BRIDGE- and BRIDGE+ was performed. Multivariable logistic regressions controlled for the history of IGT after stepwise backward elimination. RESULTS: In the program evaluation, 503 individuals were included (n=342 in historical population, n=82 in BRIDGE- population, and n=79 in BRIDGE+ population), with similar demographic and clinical characteristics across populations. A total of 212 individuals were screened for eligibility in BRIDGE, and 161 individuals participated in the program. BRIDGE participants had increased odds of HbA1c completion by 1 year postpartum (39.8% vs 12.5%; adjusted odds ratio [aOR] 4.28, 95% CI 2.71-6.78) and PCP visit scheduling (31.0% vs 12.0%; aOR 9.58, 95% CI 4.39-20.9) compared to the historical population. BRIDGE+ patients were more likely to complete HbA1c testing by 12 weeks postpartum than BRIDGE- participants. Most patients attended scheduled PCP visits, but rates of IGT counseling at PCP visits were low. CONCLUSIONS: Individuals with IGT rarely receive targeted interventions during pregnancy or delivery hospitalization. This innovative study demonstrates that individuals with IGT have high rates of uptake for postpartum SMS text messaging support, which tripled completion rates of HbA1c screening within 1 year postpartum and doubled the scheduling rate for PCP visits by 12 weeks postpartum. While attendance at scheduled PCP visits was very high, <60% of PCP visits included IGT counseling, highlighting key improvement areas in the quality of postpartum transitions to primary care. While a randomized trial is needed to ascertain definitive impact, SMS text messaging support may be an effective tool to improve postpartum transitions of care for this underserved population.
12 June 2026
Read appraisal →Nature communications
Adaptive Riemannian optimization for multi-scale diffeomorphic matching
Image matching is a fundamental task in quantitative biomedical and biological image analyses, enabling researchers to compare, integrate, and interpret imaging data across subjects, time points, modalities, and experimental conditions. Existing state-of-the-art registration methods are slow due to inefficient implementations and poor convergence rates because of the ill-conditioned nature of the optimization problem. Deep learning methods offer fast inference but require extensive training time, substantial inference memory, and fail to generalize across long-tailed distributions or diverse image modalities, necessitating costly retraining. We address these challenges by proposing FireANTs, a training-free, GPU-accelerated, multi-scale adaptive Riemannian optimization algorithm for fast and accurate dense diffeomorphic image matching. FireANTs more than doubles the speed of the community standard ANTs registration tool on a CPU, and is two orders of magnitude faster on a GPU. On the GPU, FireANTs performs competitively with deep learning methods on inference runtime while consuming up to 10 × less memory. FireANTs demonstrates robustness on a wide variety of matching problems across modalities, species, and organs, without any domain-specific training or tuning. Our framework allows hyperparameter grid search studies with less resources and time compared to traditional and deep learning registration algorithms alike.
12 June 2026
Read appraisal →PloS one
Gauging support for autonomous delivery drones powered by artificial intelligence: A public survey in Singapore
There has been emerging empirical evidence supporting the role of artificial intelligence (AI) knowledge in public support for AI. These findings stand in sharp contrast to the long-standing cognitive miser view in determining technological acceptance. In this study, by contrasting models of knowledge deficit against cognitive miser using data from a large-scale public opinion survey about autonomous delivery drones in Singapore (N = 1,002), we investigated whether knowledge and various heuristic cues were associated with AI support. Our results revealed that objective knowledge was not a direct determinant of support, while various heuristic cues such as propensity to trust in AI, news media attention, perceived source credibility in scientists and authorities, as well as risk and benefit perceptions presented significant associations. Mediation analyses further revealed knowledge was indirectly associated with support through heuristic cues such as source credibility perceptions as well as risk and benefit perceptions. Our findings were largely consistent with the cognitive miser model in that AI acceptance is immediately associated with cognitive heuristics rather than knowledge. Theoretical and practical implications are discussed.
12 June 2026
Read appraisal →JMIR formative research
Usability, Relevance, and User Engagement of a Government-Sponsored and Community Co-Designed Digital Mental Health Website During the COVID-19 Pandemic: A Pilot Study on Latino/a/x Adults
BACKGROUND: Latino/a/x adults have higher rates of unmet mental health needs than other racial and ethnic groups. One promising solution to help bridge this gap in care is digital mental health tools. Digital tools, such as self-help websites, have demonstrated the ability to enhance mental health literacy, reduce stigma, and improve mental health symptoms. Despite the potential benefits, engagement remains a critical challenge, and there has been a large oversight of unique considerations for Latino/a/x adults as end users. OBJECTIVE: Guided by the Technology Acceptance Model and the Behavioral Model for Vulnerable Populations, the study's overarching objective is to characterize within-group variation of Latino/a/x adults' engagement with a government-funded, prevention-focused mental health website that was co-designed with community partners during the COVID-19 pandemic. METHODS: The Together for Wellness/Juntos Por Nuestro Bienestar (T4W/Juntos) website offered free digital mental health resources to help Californians cope with the COVID-19 pandemic. A pilot evaluation of the website involved baseline and 4-week follow-up surveys about demographics, behavioral health needs, and overall website user experience. This current subanalysis focused on a stratified sample of Latino/a/x adult participants (baseline N=131; baseline and follow-up n=68). The baseline sample was mostly female (106/130, 81.5%); 66.9% (87/130) preferred to use the website in English and 30% (39/130) preferred Spanish. Behavioral health needs were assessed using the Patient Health Questionnaire-2, Generalized Anxiety Disorder 2-item scale, and a COVID-19 stressors checklist. We measured usability, comfort using the website, relevance of the website, and past-month use of resources. Data were analyzed using ordered and standard logistic regression methods. RESULTS: Latino/a/x adults who preferred using the website in English (odds ratio [OR] 13.76, P<.001) compared with Spanish were more comfortable using the website. Compared with adults aged 18-30 years, adults aged 41-50 years had significantly lower odds of agreeing that the website was easy to use. Sensitivity analyses revealed that participants who found the website easier to use (OR 2.22, P=.001) and those with greater behavioral health needs (OR 1.22, P=.045) were more likely to perceive the website's topics as relevant. Participants with higher behavioral health needs at baseline were more likely to use the website and engage with resources for anxiety or stress at follow-up (OR 1.42, P=.047). CONCLUSIONS: This study addresses gaps in understanding Latino/a/x adults' experiences with a prevention-focused mental health website. The language-based disparity in comfort highlights the need to significantly improve the user experience for Latino/a/x Spanish speakers. Still, the website can be a helpful resource for Latino/a/x adults with high behavioral health needs, bridging a critical gap in support. A collaborative approach to developing resource-rich websites with trusted community organizations is vital for effectively reaching Latino/a/x communities and tailoring resources to address their unique needs.
12 June 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Artificial intelligence-enabled histological analysis in pre-clinical respiratory disease models: a scoping review
Histological analysis is a cornerstone of pre-clinical respiratory disease research. It enables assessment of pathology, therapeutic effects, and mechanisms. However, conventional approaches rely on manual scoring, which is subjective, time-consuming, and difficult to scale. Artificial intelligence (AI), particularly deep learning, offers potential to automate histology workflows. To date, its use in pre-clinical respiratory models has not been synthesised.We conducted a scoping review following the Joanna Briggs Institute guidelines. We searched MEDLINE and Embase (inception - January 2025) for pre-clinical studies using AI to analyse histology in respiratory disease models. Screening, full-text review, and data extraction were performed in duplicate.Of 6271 studies screened, 29 met inclusion criteria. Most used murine models (76%) and investigated lung cancer (28%), pulmonary fibrosis (24%), or tuberculosis (17%). Haematoxylin and eosin was the most common stain (48%), with others targeting collagen or immune markers. AI tasks included image classification (n=20), segmentation (n=10), and object detection (n=4), predominantly using convolutional neural networks (69%). Pre-processing methods (e.g. stain normalisation) were common, but annotation and training practices were inconsistently reported. AI model performance was generally high (accuracy ≥90%; seven studies); however, validation metrics varied, and external validation was absent. Most studies used "black box" models, with minimal application of explainability techniques. Reproducibility measures, such as sharing datasets or code were rarely reported.AI tools are poised to transform histological analysis in pre-clinical respiratory research. The field will be able to further harness AI to automate pre-clinical respiratory histological analysis by addressing gaps that we have identified in validation, transparency, and standardisation.
12 June 2026
Read appraisal →Global health action
mHealth-supported tuberculosis contact investigation by community health workers in high-burden countries: a scoping review
Mobile health (mHealth) implementation strategies in tuberculosis (TB) contact investigations are intended to increase community health workers' (CHWs) engagement in TB contact investigations and case detection. This scoping review aimed to map and synthesize existing evidence on the design and development of acceptable and feasible mHealth implementation strategies for CHW-led TB contact investigations. We conducted a scoping review following the Arksey and O'Malley framework and PRISMA-ScR guidelines. We searched PubMed, Scopus, Cochrane Library, Web of Science, and Google Scholar for English-language studies published through October 2024 on mHealth interventions for TB contact investigation by CHWs. Eligible studies included empirical studies conducted in high-burden countries (HBCs) for TB, HIV-associated TB, and MDR/RR-TB. Studies were screened, and data were extracted using Rayyan AI-assisted software. We mapped implementation strategies, barriers, facilitators, acceptability, and feasibility of mHealth interventions. Quantitative findings were described, and qualitative data were synthesized thematically. Nine studies were included. Most interventions used hybrid mHealth technologies for communication, case registration, health records, and decision support. Implementation strategies specified actors, actions, targets, temporality, dose, and justifications. Barriers were related to CHWs, digital systems, service implementation, and TB-affected persons. Facilitators included stakeholder support, CHW training, mHealth design, community engagement, and patient-centered approaches. Overall, CHWs perceived mHealth interventions as acceptable and feasible. Successful implementation in HBCs requires strengthened CHW capacity, reliable technology, optimizing interaction between CHWs and digital tools, and continuous monitoring and evaluation throughout implementation. Main findings: Health interventions for tuberculosis contact investigation by community health workers in three global lists of high-burden countries for tuberculosis, human immunodeficiency virus-associated tuberculosis, and multidrug-resistant/rifampicin-resistant tuberculosis are generally acceptable and feasible, but their effectiveness depends heavily on a clear implementation strategy and the ability to address multilevel barriers at the individual, technology, and service system levels.Added knowledge: Given the ongoing development of digital health interventions for TB contact investigation by community health workers, this review provides a novel contribution by systematically mapping seven dimensions of implementation strategies, classifying barriers and facilitators at multiple levels, and highlighting the importance of synergies between human actors and digital systems in determining implementation success.Global health impact for policy and action: These findings confirm that strategic integration of mHealth into national tuberculosis policies and programs – with sustained funding, community health worker capacity building, digital system interoperability, and implementation-based monitoring – is crucial for closing the contact investigation gap and accelerating the achievement of the global tuberculosis elimination target.
11 June 2026
Read appraisal →Journal of neural engineering
Computational neuroelectrophysiology and artificial intelligence for drug-resistant epilepsy: recent advances, current challenges, and future directions
Drug-resistant epilepsy (DRE) affects approximately 30% of epilepsy patients, with surgical cure rates below 70%. This challenge drives a fundamental paradigm shift from localizing a discrete epileptogenic zone toward characterizing and modulating the dysfunctional brain networks that initiate and propagate seizures. This review critically synthesizes how computational neuroelectrophysiology and artificial intelligence (AI) converge to propel this shift. We chart the trajectory from quantifying local biomarkers, including high-frequency oscillations and excitation-inhibition balance metrics, to mapping the topological properties of epileptic networks through functional and effective connectivity. The integration of these network features with AI techniques, particularly spatiotemporal deep learning architectures, has demonstrated significant potential for enhancing both the localization of the epileptogenic network and the prediction of postoperative outcomes. However, the translation of this network-centric paradigm into clinical practice remains constrained by several challenges, including spatial sampling bias, the inherent label instability of surgical outcomes, and the 'black-box' interpretability crisis. Future directions must emphasize EEG foundation models, causal AI, and the development of interactive multimodal AI agents. The convergence of mechanism-driven network models and data-driven AI frameworks promises a new era of personalized, network-guided therapy for DRE.
11 June 2026
Read appraisal →Journal of medical Internet research
Using Ultra-Abridged Individual Difference Scales for Personalization in Digital Mental Health to Improve Uptake, Engagement, and Experiences: Three-Tiered Decision Framework for Scale Shortening
Given the diversity of human characteristics and experiences, personalization in nudges, messages, choice presentations, interventions, and overall product design has been increasingly adopted in digital health to promote engagement. Past studies on moderators and personalization in digital health and mental health services generally focused on demographic and symptom variables, with generally inconsistent findings or null findings. Cognitive, motivational, and decisional psychological attributes are largely overlooked. Psychology often uses long self-report scales to measure various psychological attributes. Although they are useful in tapping into individuals' psychological profiles, when applied in real-life, everyday settings to assess individual differences, people are most likely unwilling to complete them. With the pressing need to personalize digital health platforms to enhance uptake, retention, and engagement, ultrashort versions of these psychological scales may be considered to allow assessment of multiple attributes at the same time. Scale shortening can be achieved through regression analyses of each item, factor analyses, item response theory, ant colony optimization, and machine learning methods, with each method having advantages, disadvantages, and conditions required to make it suitable. To illustrate, we provided examples of regression analyses of each item and factor analyses, with potential implications for personalizing narrative versus research-based messages in digital mental health contexts. We present a 3-tiered decision framework for scale shortening method selection depending on goals and possible constraints, with guidelines on validation methods for ultrashort scales. Moving forward, more validation studies and field studies in digital health platforms are needed to evaluate the ecological validity, reliability, and generalizability of these methods, bearing in mind the limitations and conditions where such shortening methods may not work well. Researchers may compare the effectiveness and limitations of personalization using ultrashort scales with other commonly adopted personalization methods (eg, based on longer scales, behavioral data, and large language models). Ethical concerns need to be considered and mitigated carefully, respecting diverse preferences, informed choices, and the privacy of service users. Our viewpoint piece is primarily intended for digital mental health researchers and practitioners, but may also be informative for the fields of digital health and medicine as well as personalization (eg, personalized health care, personalized nudging, and message matching) more broadly, given the common goal of boosting uptake and engagement as well as improving service users' experiences.
10 June 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Advancing AI negotiations: A large-scale autonomous negotiation competition.
We conducted an international AI negotiation competition in which participants designed and refined prompts for AI negotiation agents. We then facilitated over 180,000 negotiations between these agents across multiple scenarios with diverse characteristics and objectives. Our findings revealed that principles from human negotiation theory remain crucial even in AI-AI contexts. Surprisingly, warmth-a traditionally human relationship-building trait-was consistently associated with superior outcomes across all key performance metrics. Dominant agents, meanwhile, were especially effective at claiming value. Our analysis also revealed unique dynamics in AI-AI negotiations not fully explained by negotiation theory, including AI-specific technical strategies like chain-of-thought reasoning and prompt injection. When we applied natural language processing methods to the full transcripts of all negotiations, we found positivity, gratitude, and question-asking (associated with warmth) were strongly associated with reaching deals as well as objective and subjective value, whereas conversation lengths (associated with dominance) were strongly associated with impasses. The results suggest the need to establish a new theory of AI negotiation, which integrates classic negotiation theory with AI-specific negotiation theories to better understand autonomous negotiations and optimize agent performance.
7 June 2026
Read appraisal →Journal of proteome research
AI-Based Digital Pathology-Enabled Spatial-Omics Data Analyses of the Human Kidney
Identification of tissue-region-specific changes in glycosylation is crucial for understanding the pathogenesis of kidney diseases, yet it remains a great challenge. We developed a workflow that combines matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) data with AI-based digital pathology annotations of kidney functional tissue units (FTUs) to profile N-glycan distributions within biopsy tissues. This approach can generate molecular-level data relevant to diverse pathological outcomes, thereby aiding in the elucidation of disease mechanisms. As a proof-of-concept, we demonstrate that this AI-based digital pathology approach to MALDI data segmentation enables the detection and differentiation of N-glycosylation within FTUs of healthy kidney tissue. We then elucidated differences in N-glycosylation between the diseased kidney tissue samples from patients with different diagnoses. Sialic acid N-glycans, which have been linked to various kidney diseases, displayed enrichment in the glomeruli and tubules of tissues from patients diagnosed with diabetic kidney disease (DKD), whereas they were enriched in the tubules and arteries from patients with acute kidney injury (AKI), in comparison to healthy tissue. Furthermore, we found that polylactosamine N-glycans were enriched only in the AKI samples, indicating their potential roles in tubular injury and inflammation. This workflow has the potential to bridge the gap between region-specific glycosylation and its implications on FTUs in diseases, paving the way for targeted molecular imaging studies in the kidney and other tissues.
7 June 2026
Read appraisal →Journal of medical Internet research
Characteristics of Human-Like Virtual Profiles in Relation to Audience Reach and Engagement on Instagram: Secondary Data Analysis
BACKGROUND: Prior research on human-like virtual profiles (VPs)-computer-generated imagery (CGI; displaying legible artificiality) or artificial intelligence (AI)-generated personas on social media-is often based on small samples and limited data. Furthermore, prior studies group highly photorealistic virtual influencers with abstract virtual characters, complicating comparisons. Recent generative AI has enabled large-scale production of synthetic content. These advances have drastically increased the quality and volume of synthetic media. Much of the prior literature predates these developments, leaving open questions about how posting behavior and VP design now relate to reach and engagement outcomes. OBJECTIVE: This study aimed to examine how posting behavior and key VP attributes, namely, photorealism; physical, behavioral, and narrative consistency; and human copresence related to reach and engagement across content formats (static images vs videos) on Instagram. METHODS: A total of 157 human-like female VPs were included in the final Instagram dataset. During the initial screening stage, 5 human-like male macro and mega VPs were identified but excluded because their number was too small to support meaningful subgroup comparison. Engagement was operationalized as like rate (likes or followers) for images and videos; reach was measured via examined absolute impressions. Performance was summarized using the single best-performing post and the arithmetic mean of the top 3 posts per format. Profiles and posts were further coded using predefined content variables covering visual realism, identity consistency, copresence structure, appearance patterns, and body-type representation. RESULTS: Higher-performing content was more commonly observed among larger profiles, particularly for video metrics. Among videos exceeding 20 million impressions, out of 12 videos, 7 (58%) were produced by CGI-like VPs, whereas high video like rates were more often observed among photorealistic or face-swapped profiles. In the image engagement analysis, out of 8 highest-like-rate posts, 6 (75%) featured VPs with dark hair and dark eyes, and out of the 8 posts, 4 (50%) included copresence of multiple subjects. Furthermore, of the 157 top-performing profiles reviewed, 122 (77.71%) demonstrated stable visual identity and consistent behavioral and narrative presentation, whereas noticeable inconsistencies were rarely observed in this subset. These findings represent descriptive patterns within established macro- and mega-level profiles rather than as causal predictors of growth. CONCLUSIONS: Among established human-like female VPs on Instagram, higher engagement and reach frequently co-occurred with larger profile scale, identity coherence, and transparent virtual presentation than with photorealism alone. CGI-like aesthetics aligned more with algorithmic distribution, whereas photorealistic motion content featured more prominently among posts with high active engagement. These results suggest that audiences may respond more favorably to coherent and legible virtual identities than to ambiguous realism. Because the study was restricted to macro- and mega-level profiles, the observed traits should be interpreted as characteristics of successful incumbents rather than as general predictors of VP growth.
7 June 2026
Read appraisal →PloS one
The application of large language models in bariatric surgery: A scoping review
BACKGROUND: Exploratory applications of large language models within the specialized field of metabolic and bariatric surgery have begun to emerge. Nevertheless, existing research remains fragmented, lacking comprehensive integration. OBJECTIVE: To conduct a scoping review of studies on the application of large language models in the field of metabolic and bariatric surgery, aiming to provide a reference for clinical practice and future research. METHODS: This scoping review adhered to the Joanna Briggs Institute methodological framework and followed the preferred reporting items for systematic reviews and meta-Analyses extension for scoping reviews (PRISMA-ScR) guidelines.PubMed, Web of Science, The Cochrane Library, Embase, CINAHL, CNKI, Wanfang, and VIP databases were searched for relevant studies, with the search timeframe from database inception to November 2025. The included literature was summarized and analyzed. RESULTS: A total of 21 English-language studies were included. LLMs were primarily applied in scenarios such as patient education and information consultation, clinical decision support, and professional knowledge assessment. While LLMs performed well in information-provision tasks, they showed low consistency with expert opinions in complex clinical tasks such as individualized surgical recommendations. Performance varied across different models, with GPT-4 generally demonstrating superior performance, and domain-specific models showing professional potential. Current research still faces challenges regarding information accuracy, readability, and clinical applicability. CONCLUSION: Large language models hold auxiliary potential in the field of metabolic and bariatric surgery, particularly for knowledge dissemination and patient education. However, their reliability in complex clinical decision-making remains limited. Future efforts should focus on conducting high-quality studies, advancing model specialization and standardized evaluation, and exploring safe and effective human-AI collaboration models.
7 June 2026
Read appraisal →Journal of medical Internet research
Daily Stress and Heart Rate Variability Among Mindfulness Meditation Practitioners: mHealth Observational Study
BACKGROUND: Mindfulness meditation has been reported to reduce stress and enhance well-being. However, its effects on heart rate variability (HRV)-a physiological marker of stress-remain underexplored. OBJECTIVE: This study aimed to examine how meditation practice is associated with subjective stress, HRV, and their interaction, using mobile health technologies. METHODS: This 3-week observational study included 90 participants-19 meditation practitioners (meditation group), 32 recreational runners as an active control group characterized by lower stress and higher HRV (running group), and 39 individuals without regular meditation or exercise habits (control group). HRV was continuously recorded using Garmin smartwatches. Subjective stress levels and activity states were assessed 3 times daily through a smartphone-based experience sampling method, yielding a total of 4557 responses (mean 50.6, SD 22.8 per participant). From the meditation group, start and end times of 632 daily meditation sessions (mean 33.3, SD 18.3 per participant) were also collected via the app. Standardized questionnaires on stress and related measures were administered at the end of the study period. RESULTS: The questionnaire survey confirmed that stress levels were significantly lower in both the meditation and running groups compared with controls (median Perceived Stress Scale scores: meditation 21, IQR 17-24; running 22, IQR 19-25; control 25, IQR 21-30; Kruskal-Wallis P=.02; adjusted Wilcoxon P=.05 and .04, respectively). Smartwatch-derived HRV (root-mean-square of successive differences [RMSSD]) was elevated in the running group relative to controls (median 47.0, IQR 44.0-54.2 and median 42.0, IQR 34.2-47.8, respectively; P<.001), whereas no significant difference was observed between the meditation and control groups (median 40.8, IQR 35.5-44.4 and median 42.0, IQR 34.2-47.8, respectively). Bayesian analysis of the experience sampling method data indicated that higher subjective stress levels were associated with a concurrent RMSSD reduction of -2.24 (95% credible interval -3.97 to -0.26) milliseconds. Although this pattern was consistent across groups, the steeper decline of -3.94 (95% CI -7.04 to -0.74) milliseconds was observed only in the running group, likely reflecting their elevated baseline HRV. Additionally, Bayesian modeling of 632 logged meditation sessions revealed an acute RMSSD increase of +4.68 (95% CI 2.96 to 6.38) milliseconds during meditation, with effects maintained for at least 30 minutes post practice. CONCLUSIONS: Although HRV among meditation practitioners did not appear elevated in overall daily life, the ability to increase HRV at arbitrary timings, along with the prolonged residual effect, may correlate with stress reduction. This hypothesis requires further exploration with adequate controls. Despite the preliminary nature of this study due to its limited sample size, our findings highlight the potential of mobile health-based methodologies to capture stress and HRV dynamics in real-world settings.
1 June 2026
Read appraisal →BMJ open
Can digital self-screening improve identification of chronic dyspnoea in Australian general practice? A proof-of-concept protocol for the BREATHE SMART trial.
INTRODUCTION: Chronic dyspnoea is a prevalent and clinically significant symptom, often indicative of underlying cardiorespiratory disease. It is frequently under-reported by patients and under-recognised in primary care, with these challenges exacerbated in rural and remote communities where disease burden is greater and patients experience barriers to timely diagnosis and management. The BREATHE SMART trial aims to implement and evaluate an innovative, fully digital self-screening system for chronic dyspnoea, integrated into general practice workflows and information technology infrastructure. This approach seeks to enhance early detection and management of chronic cardiorespiratory conditions across diverse practice settings. METHODS AND ANALYSIS: This multisite proof-of-concept study will test a software platform delivering a preconsultation self-screening questionnaire across 40 general practices in urban, rural and remote Australia. The system identifies eligible patients (≥18 years, consenting to SMS communication with their practice), issues an automated SMS that administers a validated dyspnoea screening questionnaire, and summarises responses for integration into the electronic medical record. Process evaluation will assess acceptability and utility using deidentified audit data, software metrics and qualitative feedback from patients, staff and general practitioners (GPs) via surveys, interviews and focus groups. Approximately 12 000 patients will be screened over 12 months. Primary outcomes will include the proportion completing self-screening and prevalence of chronic dyspnoea and secondary outcomes will include the rate of newly diagnosed chronic dyspnoea-related conditions (ie, asthma, chronic obstructive pulmonary disease and heart failure) in the preceding 12 months and during the intervention period. ETHICS AND DISSEMINATION: Ethics approval was granted by the University of New South Wales Human Research Ethics Committee (HREC) (iRECS6645) and the University of Notre Dame Australia HREC (2024-155). Participating practices and each GP will provide written, informed consent. All patients being screened will provide electronic informed consent. Results of the study will be disseminated through various forums, including peer-reviewed publications and presentation at national and international conferences. Following the study, participating practices will be provided with a summary of the findings of the study, together with a full copy of any publications and a plain language statement for participants, which will be made available in the practices. TRIAL REGISTRATION NUMBER: ACTRN12624001451594.
1 June 2026
Read appraisal →Archives of microbiology
AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding
The ability to reliably predict protective immunity against infectious diseases remains a central challenge in immunology, vaccine development, and public health. Despite major advances in immunological measurement, immunity prediction has historically relied on simplified correlates most notably antibody titers that often fail to generalize across pathogens, populations, and time. The emergence of large-scale, high-dimensional immune datasets spanning multi-omics, longitudinal cohort studies, electronic health records, and pathogen genomics has created new opportunities for artificial intelligence (AI)-driven modeling. However, early applications reveal that predictive accuracy alone is insufficient and is frequently undermined by spurious correlations, limited generalizability, and a lack of biological interpretability. This review synthesizes current advances and limitations in AI-based immunity prediction, arguing that meaningful progress requires embedding machine learning within immune biology, causal reasoning, and rigorous validation frameworks. We examine the biological foundations of immunity relevant to modeling including innate immune programming, trained immunity, adaptive immune dynamics, and immune memory and map these processes to appropriate data modalities and model classes. We critically evaluate predictive tasks across individual and population scales, from early infection outcomes and vaccine responsiveness to immunity waning and herd-level protection. Emphasis is placed on validation beyond internal cross-validation, on interpretability grounded in immune mechanisms, and on robustness under demographic diversity and pathogen evolution. We conclude that immunity is best understood and predicted as a dynamic, probabilistic, and multiscale system rather than as a static trait. AI has substantial potential to improve infectious disease preparedness and clinical decision-making.
1 June 2026
Read appraisal →BMC health services research
Anticipated barriers and facilitators of implementing a digital behavioral treatment for chronic pain: a qualitative interview study
BACKGROUND: In digital health innovations, sustainable implementation should be considered from the initiation of the development to ensure that the solution fits into the existing healthcare system. This study aims at identifying barriers and facilitators for the prospective implementation of a digital behavioral intervention for chronic pain in the Swedish healthcare system, as perceived by key stakeholders. METHODS: Eight semi-structured individual interviews were conducted with stakeholders (n = 2 IT developers, n = 4 healthcare managers, n = 2 healthcare professionals). Qualitative data were analyzed using a framework analysis following the Consolidated Framework for Implementation Research (CFIR). RESULTS: A total of 86 facilitators and 38 barriers to prospective implementation were identified across stakeholder interviews. Facilitators included perceived relevance of the digital intervention and organizational readiness. Identified barriers primarily related to insufficient training and supervision, unclear decision-making processes (e.g., specific actions, responsibilities of stakeholders), and the lack of systematic needs assessment to guide implementation planning. CONCLUSION: Overall, findings indicate a high level of openness toward digital interventions within the implementation context. While implementation context largely demonstrates readiness, successful integration will depend on proactively addressing identified barriers. Awareness of these factors enhances the likelihood of successful and sustainable implementation.
1 June 2026
Read appraisal →BMJ health & care informatics
Routine data are not 'dirty' data: how to approach using routine data for research.
The rise of electronic patient records has led to an abundance of routine healthcare data, and the development of NHS Secure Data Environments (SDEs) is transforming their potential for research. However, routine data differ fundamentally from research data; they are generated to support care processes rather than answer predefined research questions. This creates challenges when they are analysed without understanding the context in which they were produced.Using real examples from practice, this paper illustrates the variety of ways routine data can be shaped and how this leads to misinterpretation if treated as merely 'dirty' research data. These examples underpin practical strategies for researchers, including analysis at or by source and working closely with local data providers to understand how a dataset was created.We argue that SDEs must foster strong links between data users and creators to preserve data provenance and support accurate interpretation. By approaching routine data as meaningful records of healthcare processes-rather than flawed research datasets-researchers can fully realise their potential to drive healthcare improvement.
1 June 2026
Read appraisal →JMIR research protocols
Implementation of Mobile-Based Programs for Alcohol Cessation in the Treatment of Alcohol-associated Liver Disease: Protocol for a Type 1 Hybrid Implementation-Effectiveness Trial
BACKGROUND: The rising burden of alcohol-associated liver disease (ALD) calls for effective interventions. Alcohol cessation remains the only intervention known to reduce long-term ALD morbidity and mortality. Integrating treatment for alcohol use disorder with medical and hepatology care shows significant promise. OBJECTIVE: This study aims to conduct a randomized, controlled, type 1 hybrid implementation-effectiveness trial in patients with ALD to evaluate an evidence-based smartphone app for alcohol use disorder (Connections). The primary aim is to (1) compare the effectiveness of the Connections app plus usual care to the effectiveness of usual care alone on days of alcohol abstinence for patients with ALD and (2) assess the implementation of the Connections app in the ALD population using a multilevel model of system change to determine facilitators and barriers to the successful adoption of the app at the patient, provider, and clinic levels. METHODS: Study procedures were approved by the Institutional Review Board on March 13, 2024. Patients are recruited from General Hepatology and Multidisciplinary Clinics in Wisconsin and Michigan. Participants are randomized to an intervention (Connections + treatment as usual) or no intervention (treatment as usual). During the 6-month enrollment period, participants complete monthly e-surveys measuring alcohol consumption and quarterly e-surveys measuring patient health indicators and behaviors. Patients earn up to US $240 for participation. RESULTS: This study was funded in September 2023. Recruitment began in June 2024 and is ongoing. As of February 26, 2026, 144 participants have been enrolled in the study. Of these, 65 (45.1%) have completed all the study activities. We aim to recruit 298 participants through 2027, with analysis and results publication estimated in 2028. CONCLUSIONS: Implementation insights highlight the importance of flexibility and strong provider relationships. Challenges include reliance on technology, which limits access for patients with low digital literacy, and the risk of loss to follow-up. This trial is the first fully powered implementation-effectiveness study of a mobile health app for alcohol cessation in ALD, testing a new care model that integrates multidisciplinary expertise. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/94231.
1 June 2026
Read appraisal →BMC health services research
Does an app make patients happy? Impact of a novel medical history app on patient satisfaction in urgent care consultations in Germany: cluster-randomized interventional trial 'DASI'.
BACKGROUND: Patient satisfaction is an important indicator of healthcare quality. However, time constraints in primary care limit effective communication and history-taking. Digital medical history systems have the potential to improve the quality of medical consultations by enabling patient preparation and providing physicians with comprehensive pre-consultation information. This study aimed to evaluate the impact of a novel medical history-taking app (DASI app) on patient satisfaction in out-of-hours practices (OOHP) in Germany. METHODS: We conducted a two-center, cluster-randomized trial over 12 months. Within each practice, weeks were randomized to either an intervention or control group, resulting in a cluster-randomized trial (CRT) with clustering in weeks within the same practice. Patients either used the DASI app before consultations (intervention group) or received standard care (control group). The DASI app is a patient-facing tool that guides patients through a dynamic questionnaire adapted to the selected complaints and previous answers. Patient satisfaction was measured using 17 items from the EUROPEP instrument covering relation and communication, medical care, and information and support. Additionally, we collected sociodemographic data. Statistical analyses included Mann-Whitney U tests for individual items and t-tests for domain scores. Analyses were performed with R, version 4.5.2. RESULTS: Among 1,460 approached patients, 1,040 (71%) were enrolled and 1,034 included in analyses. Patient median age was 31 years, with 60% female participants. Patients in the intervention group (n = 496) showed significantly better ratings in 12 of 17 EUROPEP items compared to controls (n = 538) and demonstrated significantly higher satisfaction across all three EUROPEP domains. Between 78 and 96% of patients rated care as excellent or very good, with intervention group patients more likely to select top-level evaluations in 9 of 17 items. CONCLUSIONS: Digital medical history taking significantly enhanced patient satisfaction in urgent care settings. The app likely improved satisfaction through multiple pathways including increased patient empowerment, better consultation preparation, and more efficient physician-patient interactions. These findings demonstrate that user-friendly digital tools can meaningfully enhance patient experience without disrupting existing workflows, supporting healthcare digitalization efforts while maintaining high-quality patient-centered care. TRAIL REGISTRATION: German register for clinical trials (DRKS00026659; date of registration: 2021-11-03).
1 June 2026
Read appraisal →JMIR research protocols
Creation of an mHealth Infrastructure to Support the Development and Delivery of mHealth Interventions: Protocol for Demonstration Projects Addressing Smoking Cessation in Cancer Care
BACKGROUND: Cancer remains a leading cause of morbidity worldwide. To reduce this burden, scalable, effective approaches are needed to address modifiable risk factors for cancer and support behavioral self-management. With smartphone ownership now nearly ubiquitous, mobile health (mHealth) interventions offer a powerful means to extend the reach, accessibility, and sustainability of evidence-based treatments for a variety of modifiable risk factors (eg, excessive alcohol use, physical inactivity, poor diet, and smoking). Moreover, the flexibility of mHealth platforms enables efficient delivery of novel interventions, supports innovative study designs, and facilitates real-time data collection to advance public health research. OBJECTIVE: Despite the great potential of mHealth interventions, developing high-quality mHealth tools is complex, time-consuming, and resource-intensive. To address these challenges, we are developing a coordinated, accessible, research-grade infrastructure for mHealth app development, testing, and dissemination. METHODS: The mHealth Florida infrastructure (mFLi) will provide a comprehensive, low-code software platform that enables researchers to build apps compatible with major mobile operating systems, namely, Apple iOS and Google Android. Through a modular interface, users will select from a menu of prebuilt features to tailor functionality to specific study needs. The platform will include 3 integrated environments (development, testing, and production), allowing researchers to prototype, evaluate, and deploy mHealth interventions. This infrastructure will be developed and maintained by a multidisciplinary team, ensuring that the platform is technically robust and usable and adheres to institutional and regulatory standards. To demonstrate the platform's functionality, utility, and adaptability, a multisite study comprising three initial projects focused on smoking cessation among patients with cancer is being conducted: (1) participant screening and enrollment, (2) randomization and treatment delivery, and (3) data processing using machine learning methods with on-device and cloud-based approaches. RESULTS: This study was funded in May 2023, and ethics approval was obtained from all involved sites' institutional review boards between February 2024 and October 2025. Recruitment began in March 2025 and enrollment is ongoing. As of January 2026, 41% (37/90) of the target sample have been enrolled and 21% (19/90) have completed their 6-month assessment. Data collection will be completed once the final participant completes their 6-month assessment (expected May 2027), with analyses commencing thereafter. Study findings are anticipated to be published in a peer-reviewed journal in 2027. CONCLUSIONS: Collectively, these projects will illustrate how mFLi can streamline app development, facilitate rapid translation of research into practice, and reduce barriers for researchers and developers. Ultimately, mFLi is designed to accelerate innovation in mHealth research, enhance access to behavioral interventions, and improve health outcomes among diverse populations. TRIAL REGISTRATION: ClinicalTrials.gov NCT06909357; https://clinicaltrials.gov/study/NCT06909357. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/92288.
1 June 2026
Read appraisal →Journal of neural engineering
An online brain-computer interface for detecting incongruity in augmented reality applications
Objective.Augmented reality can provide digital information about physical entities presented within its real-world context. However, this information might disagree with the user's expectations due to factual errors in the data or cognitive biases. Such incongruity can impair user experience and undermine trust in the AR system. To address this issue, we propose detecting inconsistencies between physical objects and digital information through hybrid brain-computer interfaces.Approach.We conducted two complementary experiments. First, we implemented a strategy that integrates eye-tracking and brain signals for incongruity detection in an offline study. Subsequently, we assessed our approach in an online study in which participants received immediate feedback on the classification.Main results.The grand average event-related potentials revealed consistent electroencephalographic responses to incongruent augmentations, specifically a centroparietal N400 effect, across both experiments. We could further distinguish between congruent and incongruent information with an average balanced accuracy of 70% in the online study.Significance.These findings demonstrate the feasibility of detecting incongruity online, allowing for autonomous system adaptation, like presenting information in a more accessible format or providing contextual support.
31 May 2026
Read appraisal →Journal of medical Internet research
Acceptability of Technologies to Support Early Dementia Detection: Qualitative Study With the Boston University Alzheimer's Disease Center Cohort
BACKGROUND: Dementia is on the rise globally due to increasing life expectancies and population growth. Digital technologies may help detect early signs, enabling timely interventions to slow or reverse cognitive decline. However, to support the successful implementation of these digital technologies into health care settings, they must be acceptable to target users. Older adults and those with mild cognitive impairment (MCI) are at risk of developing dementia in later life and need to be able to use these technologies in order for this intervention to be approved and implemented in clinical practice. OBJECTIVE: This study explored the perspectives of older adults and those living with a clinical diagnosis of MCI on the acceptability of using various digital technologies that have the potential to support early dementia detection. METHODS: Participants were recruited from Boston University's Alzheimer's Disease Research Center. Participants selected at least 2 technologies from 9 different wearables and software to use for 2 weeks, at 3-month intervals, over a total duration of 2 years. A subgroup of self-selecting participants was interviewed after the first 2 weeks of use to gather initial perspectives regarding the acceptability of using the digital technologies. An inductive framework thematic analysis approach was used, assisted by NVivo (version 14.23.2; QSR International). RESULTS: In total, 13 individuals living with a clinical diagnosis of MCI and 11 adults aged 65 years and older were interviewed. Our analysis identified five key themes: (1) gamification, (2) wearability, (3) user guidance, (4) burden of use, and (5) usefulness. Gamified apps were generally liked, although users with little experience of digital games needed time to adjust. Wearables resembling everyday accessories (eg, watches) were preferred, but complaints about tight or uncomfortable straps were frequently reported. Clear instructions were critical to support correct use, but many participants would have liked more troubleshooting support when technical issues arose. The use of 5 or more devices led to a high burden, especially when devices had practicality issues such as not being waterproof. Devices offering personal feedback were perceived as useful to satisfy personal interests, though some questioned their usefulness within health care. Participants raised concerns about losing valued personal interactions with health care professionals and questioned how their existing health conditions and treatment for such conditions may affect the validity of the data collected by the devices. CONCLUSIONS: These findings can guide researchers in choosing appropriate devices and minimizing burden. Future work should explore the views of those experiencing digital exclusion to ensure equitable access to dementia-detection technologies.
31 May 2026
Read appraisal →PloS one
Three-dimensional shape cues affect human and artificial recognition systems differently
Humans and neural networks use shape and texture information differently. While humans weigh shape heavily in their ultimate classification decision, neural networks are more biased towards texture cues. Many tests of shape vs. texture bias have focused on shape recognition from an object's external contour. However, shape information is also conveyed through internal contours, shading, and attached shadows, especially when an object is viewed from noncanonical perspectives. Using models from ShapeNet, we created datasets of 120,000 texture-substituted images of objects from many viewpoints with and without shading and attached shadows. We tested humans' and several neural networks' ability to classify these objects by both their shape and their texture. Humans were much better at classifying texture-substituted objects by their shape than any network, although these differences were greater when shape was defined only by the external contour than when 3D cues were included. Our findings suggest that networks' texture bias is reduced when 3D cues are included in images. We next tested whether the inclusion of 3D cues benefitted humans and neural networks more for images of objects viewed from canonical or noncanonical perspectives. Consistent with earlier research, we found that 3D cues primarily benefitted humans for noncanonical images. For neural networks, the greatest performance gains were for canonical images. These findings suggest fundamental differences in how humans and networks use shading and attached shadows for object recognition. We argue that humans use these cues to infer objects' 3D structures while neural networks use them as another surface-level cue like texture.
31 May 2026
Read appraisal →JMIR medical informatics
The Regional Implementation of an Electronic Health Record-Integrated Ambient Scribe in Primary and Secondary Care in England: Real-Time Qualitative Evaluation
BACKGROUND: There is significant potential for ambient scribe technology to enhance health care productivity, with a growing range of applications being developed and implemented internationally. Strong organizational drivers to improve efficiency, coupled with the technology's potential to help address clinician burnout, are accelerating interest and adoption. However, limited attention has been paid to the integration of such systems within electronic health records and unintended consequences, as stakeholders navigate their implementation and integration into clinical practice. OBJECTIVE: This study therefore aimed to explore the processes involved in implementing and adopting ambient scribe technology across diverse health care settings. METHODS: We conducted a real-time, longitudinal qualitative evaluation of a pilot implementation of an integrated ambient scribe system across National Health Service primary care and secondary hospital settings within a care system in the Midlands region of England. Data collection involved in-depth one-to-one interviews conducted in several phases: an initial scoping study to identify key interests and stakeholders for inclusion, followed by an implementation study with participants involved in the pilot. We also conducted 16 hours of nonparticipant observations of consultations. The implementation study gathered data at 2 time points, before implementation and 3 to 4 weeks after, to capture experiences, changes, and emerging impacts over time. RESULTS: We collected data from 45 individuals. Use cases varied across settings and shaped how ambient scribe systems were deployed. Differences between general practice and secondary care in documentation purpose, format, and workflow created challenges for developing and validating templates, though the technology showed flexibility across contexts. Automated note-taking often improved patient interaction but required clinicians to adjust how they spoke to ensure the technology captured their reasoning. Outputs were sometimes generic, reinforcing defensive documentation and reducing personal tone or contextual recall. Integration pathways carried distinct trade-offs: stand-alone systems (which were used by many stakeholders in our study) were easier to adopt but offered limited long-term benefits, while integrated systems required greater effort and standardization, yet promised improved efficiency and safety. CONCLUSIONS: The ambient scribe market remains immature and volatile, creating strategic uncertainty for health systems. Careful procurement approaches are needed to balance the risks and benefits of integrated versus stand-alone systems, while aligning user demand with organizational needs for integration.
31 May 2026
Read appraisal →African journal of reproductive health
The role of information technology in promoting sexual and reproductive health awareness among young people in Lahore: A comparative study with Shanghai.
Information technology (IT) has transformed access to health information, yet its impact on sexual and reproductive health (SRH) awareness varies across contexts. This study investigates IT's influence on youth SRH awareness in Lahore, Pakistan, using primary data, and provides a comparative analysis with secondary data from Shanghai, China. Using a cross-sectional design in Lahore (n=250), structured questionnaires, PLS-SEM, logistic regression, and ANOVA assessed relationships between IT use, digital health literacy, trust in digital sources, cultural barriers, and awareness outcomes. Results indicate IT use (β=0.42), digital health literacy (β=0.38), and trust (β=0.26) significantly enhance awareness in Lahore, while cultural barriers exert a strong negative influence (β=-0.31). Significant mean differences (p<0.001) were found between cities. Shanghai demonstrated higher digital literacy, trust, awareness, and use of structured platforms, while Lahore exhibited substantially higher cultural barriers. Model comparisons showed stronger predictive pathways in Shanghai (R²=0.74) than Lahore (R²=0.68), reflecting advantages of institutionalized sex education and government-certified digital health applications. Overall, while IT holds considerable potential, its effectiveness is shaped by cultural norms, digital infrastructure, and institutional trust. The findings underscore Pakistan's need to develop culturally appropriate, reliable, and institutionally supported digital health platforms to improve youth access to accurate SRH information. La technologie de l'information (TI) a transformé l'accès à l'information sanitaire, mais son impact sur la sensibilisation à la santé sexuelle et reproductive (SSR) varie selon les contextes. Cette étude examine l'influence des TI sur la sensibilisation des jeunes à la SSR à Lahore, au Pakistan, avec une analyse comparative de Shanghai, en Chine. En utilisant un design transversal à Lahore (n=250), des questionnaires structurés, la PLS-SEM, la régression logistique et l'ANOVA ont évalué les relations entre l'utilisation des TI, la littératie numérique en santé, la confiance dans les sources numériques, les barrières culturelles et les résultats de sensibilisation. Les résultats indiquent que l'utilisation des TI (β=0,42), la littératie numérique en santé (β=0,38) et la confiance (β=0,26) améliorent significativement la sensibilisation à Lahore, tandis que les barrières culturelles exercent une forte influence négative (β=-0,31). Des différences moyennes significatives (p<0,001) ont été constatées entre les villes. Shanghai a démontré une littératie numérique, une confiance, une sensibilisation et une utilisation de plateformes structurées plus élevées, tandis que Lahore présentait des barrières culturelles substantiellement plus fortes. Les comparaisons de modèles ont montré des voies prédictives plus solides à Shanghai (R²=0,74) qu'à Lahore (R²=0,68), reflétant les avantages de l'éducation sexuelle institutionnalisée et des applications de santé numériques certifiées par le gouvernement. Globalement, bien que les TI aient un potentiel considérable, leur efficacité est façonnée par les normes culturelles, l'infrastructure numérique et la confiance institutionnelle. Les conclusions soulignent la nécessité pour le Pakistan de développer des plateformes de santé numériques culturellement adaptées, fiables et soutenues institutionnellement pour améliorer l'accès des jeunes à des informations précises sur la SSR.
31 May 2026
Read appraisal →BMJ open
Patterns and determinants of primary care virtual health service use among rural Aboriginal and Torres Strait Islander adults with chronic diseases: a cross-sectional study
OBJECTIVE: To examine virtual health service (VHS) device usage patterns and identify factors associated with VHS engagement among Indigenous Australian adults with chronic conditions. DESIGN: Cross-sectional survey. SETTING: An Aboriginal Community Controlled Health Organisation providing primary healthcare services across rural and remote Queensland, Australia. PARTICIPANTS: 74 consenting Indigenous Australian adults with at least one chronic condition who were registered VHS users. METHODS: Participants completed surveys assessing their use of four Bluetooth-enabled monitoring devices (pulse oximeter, blood glucose monitor, blood pressure monitor, weight scale) over a 2-week recall period. The primary outcome was VHS device usage status, categorised as active versus inactive users. Active users were defined as participants who reported any frequency of device use, while those reporting no use were classified as inactive users. All participants had access to Indigenous health coach support as part of the VHS model. Binary logistic regression was used to identify sociodemographic and geographical factors associated with VHS engagement. RESULTS: Sixty-four per cent (n=47) were active users, with 73% of these using all four devices concurrently. Among active users, blood pressure monitors showed the highest utilisation (98%), followed by weight scales (91%), blood glucose monitors (89%) and pulse oximeters (86%). Three factors were significantly associated with VHS usage using binary logistic regression: residing in medium rural towns (adjusted OR 4.71, 95% CI 1.23 to 17.94, p=0.02), age 18-65 years (adjusted OR 3.59, 95% CI 1.05 to 12.22, p=0.04) and having multiple chronic conditions (adjusted OR 10.95, 95% CI 1.25 to 95.87, p=0.03) compared with those in more remote areas, aged ≥66 years and with single condition, respectively. CONCLUSION: Indigenous-led VHS achieve substantial engagement through culturally grounded health coach support. However, addressing digital connectivity in remote areas, age-appropriate support for older adults and Indigenous workforce development is essential to ensure equitable access and sustained engagement.
31 May 2026
Read appraisal →JMIR infodemiology
Early Depression Detection in Social Media: Monitoring of Individual Nighttime Dynamics and Large Language Model Analysis
BACKGROUND: Depression has become a major global public health challenge, and early intervention is critical for improving patient outcomes. Current depression detection techniques based on social media data (traditional risk detection) rely heavily on users' complete historical information, which cannot meet the timeliness requirements of early intervention. This underscores the need for early risk detection (ERD) methods emphasizing early-stage, real-time warning. However, existing ERD studies have notable limitations such as (1) they overlook temporal activity patterns hidden in posting time stamps, missing vital warning signals; and (2) they depend on static templates or resource-intensive sequence models, resulting in limited interpretability and inefficient use of early data, ultimately constraining their clinical applicability for early intervention. OBJECTIVE: This study aims to develop an efficient, reliable, and interpretable ERD model. The core objectives are to extract temporal activity patterns features from posting time stamps, thereby enriching feature dimensions for risk detection; to leverage large language models (LLMs) for improved text filtering precision and depression-related factor analysis; and to achieve accurate early detection of depression to support clinical intervention. METHODS: We propose the Monitoring of Individual Nighttime Dynamics (MIND) and LLM analysis model, which integrates two key innovations: (1) circadian activity dynamics: posting time stamps are transformed into temporal activity patterns, analyzing fluctuations in posting frequency and timing to derive sleep-related features, thereby compensating for the limitations of text-only approaches, and (2) LLM depression profiler: LLMs are used for dynamic text filtering, automatically removing irrelevant noise and focusing on potential depression-related cues. Based on LLM semantic understanding, latent depression risk factors are identified, enhancing interpretability for clinical treatment and robustness to noise. RESULTS: Experiments on the eRisk2017 (data source: Reddit [Reddit Inc]) benchmark dataset demonstrated that MIND significantly outperformed existing baseline models in early detection sensitivity, specificity, and accuracy. By combining sleep-related features with text analysis, the model achieved interpretable, traceable predictions that can support clinical treatment. ALL relevant experimental code is publicly available. CONCLUSIONS: The MIND model combines temporal activity pattern features with LLM-based text analysis, addressing the challenges of poor interpretability and inefficient use of early-stage data in existing ERD methods. It significantly enhances early detection performance, offering a new paradigm for applying social media data in ERD, thereby enabling earlier intervention and reducing the public health burden of depression.
31 May 2026
Read appraisal →PloS one
Effect of digitizing Community Health Information System on the delivery and use of maternal and child health services: Propensity score matching analysis
INTRODUCTION: In 2018, Ethiopia's government digitized its Community Health Information System (eCHIS) to enhance Health Extension Program service delivery. However, the impact of eCHIS on health outcomes remains unclear. This study, therefore, examined the effects of eCHIS on the uptake of maternal and child health services. METHODS: A post-test-only, non-equivalent group household survey design was used, collecting data from July to August 2024 in rural communities with and without eCHIS. A stratified multistage sampling technique was employed to recruit respondents. A sample of 1,728 women of reproductive age (271 in eCHIS and 1,457 in non-eCHIS woredas), 1,118 women with children ages 0-11 months (188 in eCHIS and 930 in non-eCHIS), and 569 women with children ages 12-23 months (301 in eCHIS and 268 in non-eCHIS) were included in this study. Propensity scores were used to match intervention and comparison communities based on women's age, religion, parity, education, household wealth, distance to health facilities, and autonomy. A modified Poisson regression analysis, adjusting for covariates, was conducted to estimate the adjusted prevalence ratios (PR) for maternal and child health outcomes. RESULTS: Modern contraceptive use was 66% in intervention areas versus 56% in comparison areas. Institutional delivery rates were 95% in intervention and 79% in comparison areas. In intervention areas, 94% of children received the first dose of the Pentavalent vaccine compared to 83% in comparison areas, while 85% and 68%, respectively, received the third dose. Adjusted analyses showed that eCHIS intervention areas had statistically significant increases in contraceptive use (13.0 percentage points; PR: 1.25; p-value < 0.01) and institutional deliveries (8.9 percentage points; PR: 1.18; p-value < 0.01) compared to comparison areas. Similarly, the average treatment effect of eCHIS on Pentavalent 1, Pentavalent 3, and full vaccination coverage showed significant increases of 10.3 (PR: 1.13; p-value < 0.01), 13.6 (PR: 1.26; p-value < 0.01), and 14.6 (PR: 1.36; p-value < 0.01) percentage points, respectively. CONCLUSION: This study underscores the transformative potential of eCHIS in improving maternal and child health outcomes in Ethiopia. Sustained investment in digital health systems can help scale effective health care services to rural and underserved communities.
31 May 2026
Read appraisal →BMC musculoskeletal disorders
The effectiveness of telerehabilitation in upper limb musculoskeletal disorders: a systematic review.
BACKGROUND: Telerehabilitative services have been increasingly used in recent years, raising questions about their effectiveness. In musculoskeletal disorders, evidence supports telerehabilitation but methodological quality of evidence is limited and focused on lower limb disorders. Hence, this systematic review aimed to assess the effectiveness of telerehabilitation in upper limb musculoskeletal disorders. METHODS: We conducted electronic searches in MEDLINE, Embase, and AMED (up until 11/27/2024), complemented by hand searches. Randomized (RCTs) and non-randomized studies of interventions (NRSIs) assessing upper limb musculoskeletal disorders reporting on pain, activities of daily living (ADL), or health-related quality of life (HrQol) were included. We focused on studies from high-income countries in English or German. Risk of bias was assessed using RoB 2 and ROBINS-I. Standardized mean differences (SMDs) and 95% confidence intervals were derived from meta-analysis, where negative values favor telerehabilitation. Certainty of evidence was rated using GRADE. We grouped studies comparing telerehabilitation to standard care (subgrouped into in-person or minimal intervention) and separately examined studies with a tele-adjunct. Compared to in-person rehabilitation we assessed whether outcomes were similarly improved with telerehabilitation. For tele-interventions as adjunct or compared to minimal care we assessed whether telerehabilitation improved outcomes. RESULTS: We included twenty RCTs representing 1,285 participants. All NRSIs were excluded due to critically high risk of bias. Compared to in-person rehabilitation, telerehabilitation showed similar improvements with very low certainty of evidence in pain (SMD: -0.33 [-1.16 to 0.5] VAS/NRS), moderate in ADL (SMD: -0.07 [-0.31 to 0.17] QuickDASH/DASH), and low in HrQol (SMD: -0.26 [-0.82 to 0.31]). Compared to brochure-based exercising, certainty of evidence was low for improved pain (SMD: -0.28 [-0.58 to 0.03]), moderate in ADL (SMD: -0.56 [-0.88 to -0.24]), and low in HrQol (SMD: -0.31 [-0.65 to 0.02]). Tele-adjunct yielded low certainty of evidence for improvements in pain (SMD: -0.38 [-0.71 to -0.05]) and moderate in ADL (SMD: -0.51 [-0.88 to -0.14). CONCLUSIONS: Our results tend to support the effectiveness of telerehabilitation across different comparisons although certainty of evidence is inconclusive. Future studies should particularly investigate the non-inferiority of telerehabilitation compared to in-person rehabilitation and the added benefit of tele-adjunct. TRIAL REGISTRATION: Prospero registration ID: CRD42024622465.
31 May 2026
Read appraisal →BMJ open
Impacts of human and digital scribes on improving physician wellness and reducing administrative burden: a scoping review protocol
INTRODUCTION: The rise in administrative burden on physicians is becoming increasingly recognised as a significant contributor to burnout and job dissatisfaction among primary care practitioners. Human scribes (HS) and digital/artificial intelligence scribes (DS) have emerged as potential tools to reduce clerical workload and improve physician well-being. There has been extensive research conducted on HS, and DS show promise but require validation across diverse healthcare settings. This scoping review aims to assess the effects of HS and DS in primary care settings to evaluate their impact on administrative work, job satisfaction, burnout and clinical operational efficiency. METHODS AND ANALYSIS: This review will follow the Arksey and O'Malley framework with enhancements from Levac et al, along with Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) reporting guidelines. A comprehensive search strategy was developed, including Ovid MEDLINE, Ovid Embase and Scopus databases in June 2025 with an academic health sciences librarian. Database searches will be conducted between March and June 2026. The inclusion criteria consist of empirical research on licensed physicians in primary care settings that implement HS or DS systems and measure physician-related outcomes. Two researchers will review and extract data independently using Covidence. The results will be analysed through thematic methods followed by descriptive summarisation. ETHICS AND DISSEMINATION: This review conducts an analysis of existing publications without needing ethical clearance. Findings will be disseminated through peer-reviewed publications and presented at academic conferences where possible. The research team will make supporting materials accessible via the Open Science Framework. TRIAL REGISTRATION NUMBER: https://doi.org/10.17605/OSF.IO/T5BHY.
31 May 2026
Read appraisal →Science (New York, N.Y.)
Strain-resilient intrinsically stretchable electrochemical biointerfaces
Stretchable bioelectronics promise seamless integration with dynamic tissues, yet their electrochemical performance often collapses under strain owing to cracking, interlayer delamination, and signal distortion. We introduce an intrinsically stretchable interface for resilient electrochemical sensing (SIRES) built from a strain-resilient conductor, an electrically tunable interlayer, and a stretchable functional coating. By offsetting strain-induced resistance increases with gains in active surface area, SIRES maintains near-constant resistance and high-fidelity electrochemical readouts under strains up to 300%. The platform supports voltammetric, potentiometric, and amperometric modalities and enables multiplexed molecular monitoring across dynamically deforming tissues in wearable and implantable formats. Its unified architecture provides delamination-resistant interfaces suitable for long-term use, and the design rules generalize to affinity-based transduction, establishing a pathway toward strain-resilient molecular sensing for precision diagnostics and therapy.
31 May 2026
Read appraisal →JMIR medical informatics
Advancing Alzheimer Disease Prediction With Large Language Model-Based Linguistic Feature Analysis: Development and Validation Study
BACKGROUND: Alzheimer disease (AD) is a progressive neurodegenerative disorder with rapidly growing global prevalence. Early detection is critical for timely intervention; yet, conventional diagnostic methods remain costly and invasive. Speech-based assessment has emerged as a noninvasive alternative, as AD characteristically impairs linguistic abilities including fluency, coherence, and informational content. Recent advances in large language models (LLMs) offer new opportunities to extract structured linguistic features from transcribed speech for automated AD classification. However, existing LLM-based approaches often lack transparency and clinical interpretability, limiting their adoption in clinical workflows. OBJECTIVE: This study aims to investigate the influence of linguistic features extracted from transcribed speech, as analyzed by LLMs, on the accuracy and interpretability of AD prediction. METHODS: We propose a framework that leverages LLMs to analyze linguistic features extracted from transcribed speech for AD classification. Our approach focuses on 4 key aspects, including readability, fluency, richness of detail, and keyword relevance. To enhance classification accuracy, the framework integrates transcript embeddings with feature explanation embeddings, forming a comprehensive linguistic representation. We conducted extensive ablation studies to evaluate the contributions of individual features and benchmarked our framework against existing LLM-driven methodologies through pairwise explainability evaluations. Output stability was assessed across 3 independent pipeline runs. A fully local configuration (Llama 3 8B + nomic-embed-text) was tested to evaluate privacy-preserving deployment feasibility. Explainability was assessed via LLM-based pairwise comparison (Gemini-3.1-flash-lite) against the method of Bang et al across 54 correctly classified cases and by blinded evaluation from 2 neurologists. RESULTS: The proposed framework achieved a mean precision of 91.52%, a sensitivity of 91.08%, a specificity of 96.29%, and F1-score of 91.05% across 3 independent runs on the ADReSSo 2021 dataset, outperforming existing LLM-based approaches. A fully-local configuration (Llama 3 8B+nomic-embed-text, requiring no cloud application programming interface access) achieved an F1-score of 81.58%, demonstrating framework transferability to privacy-preserving deployment environments. Keyword relevance was the most influential feature (F1-score drop of 13.22 pp when removed). Explainability evaluations showed our method was preferred in 49 out of 54 cases via Gemini-3.1-flash-lite, with human experts preferring our method in 89 of 108 blinded assessments. CONCLUSIONS: These findings highlight that a structured linguistic feature analysis using LLMs provides a robust and interpretable framework for preliminary AD detection. Our approach offers a scalable and accessible solution that bridges artificial intelligence-driven text analysis with clinical applications, supporting early detection of cognitive decline through noninvasive assessment methods.
31 May 2026
Read appraisal →Medicine
A bibliometric analysis of artificial intelligence in anatomy education: Current situation, hot spots, and global trends
INTRODUCTION: Artificial intelligence (AI) is increasingly being integrated into anatomy education, yet a comprehensive overview of the field's development and emerging trends remains lacking. This study identifies research hotspots and trends in AI applications in anatomy education through a bibliometric analysis. METHODS: English-language publications on AI in anatomy education were systematically collected from the Web of Science database. Using bibliometric tools, including R, VOSviewer, CiteSpace, and Excel, data were synthesized to explore global research trends and focal areas. RESULTS: A total of 184 papers published between 2005 and 2024 were included. The United States emerged as the leading contributor in terms of publication output and citation impact. Over the past 2 decades, research focus has shifted from general medical education applications toward AI-driven image segmentation, deep learning, and surgical guidance. More recently, large language models (e.g., ChatGPT) have begun to attract attention in anatomy education. CONCLUSION: This bibliometric analysis maps the intellectual landscape of AI in anatomy education, identifying key thematic shifts and emerging frontiers. The findings provide a foundation for guiding future research directions in this rapidly evolving field.
31 May 2026
Read appraisal →JMIR formative research
Wearable- and Mobile App-Based Activity Pacing and Fatigue Management in Post-COVID-19 Condition: Exploratory Observational Study
BACKGROUND: Post-COVID-19 fatigue affects millions worldwide; yet, evidence-based management strategies remain limited. Activity pacing, regulating activity to match available energy and minimize symptom exacerbation, may support symptom management, although optimal pacing approaches remain unclear. OBJECTIVE: This study aimed to explore associations between activity pacing strategies delivered through a mobile app and daily fatigue levels in individuals with post-COVID-19 fatigue. METHODS: In this exploratory observational study, 19 adults with post-COVID-19 fatigue used wearable devices (Fitbit Inspire 3) for objective activity monitoring and a mobile app (FatigueSense) to self-report daily symptoms (fatigue and energy levels) and optionally select activity pacing goals (light, balanced, or active) over a period of 3 months. We examined associations between pacing strategies and symptom outcomes by using mixed-effects linear models with random intercepts. Same-day outcomes (fatigue and energy reported on the day of goal selection) were analyzed controlling for age and sex. Next-day outcomes (fatigue and energy reported the day following goal selection) were analyzed controlling for age, sex, and prior-day symptoms. RESULTS: Across 2182 observation days, participants self-selected pacing goals on 816 (37.4%) days, demonstrating symptom-responsive behavior with higher baseline fatigue on pacing days (mean score 1.74, SD 0.59 vs 1.58, SD 0.54 on nonpacing days on a 0-3 scale where 0="none" and 3="severe"; P=.004). On pacing days (584/816, 71.6% with complete data from 18 participants), active pacing was associated with reduced same-day fatigue (β=-0.34, 95% CI -0.49 to -0.19; P<.001) and increased same-day energy (β=8.8, 95% CI 4.7-12.9; P<.001) compared to light pacing. Balanced pacing also showed significant reductions in fatigue (β=-0.15; P=.008) and increases in energy (β=5.8; P<.001) compared to light pacing. Next-day effects were attenuated and nonsignificant (fatigue: β=-0.05, P=.53; energy: β=1.1, P=.61). Individual heterogeneity was substantial, with an intraclass correlation coefficient of 0.32, indicating that 32% of the variance was attributable to between-person differences. Among participants trying multiple strategies, 58.3% (7/12) showed meaningful responses (≥0.3-point fatigue reduction) to structured pacing strategies. CONCLUSIONS: Structured activity pacing strategies (active and balanced) were associated with improved same-day symptom management in individuals with post-COVID-19 fatigue. However, substantial confounding by indication (self-selection of pacing based on symptom state) and individual heterogeneity limit causal interpretation. These exploratory findings warrant testing in randomized controlled trials to establish efficacy and identify responder characteristics.
30 May 2026
Read appraisal →Biomedical physics & engineering express
Stabilizing neuromorphic ECG processors via adaptive fractional fatigue
Spiking neural networks (SNNs) deployed on wearable devices can exhibit runaway firing when processing noisy electrocardiogram (ECG) signals, increasing switching activity and dynamic power and destabilising power regulation. To suppress redundant firing while preserving ECG classification performance, we introduce the adaptive fractional fatigue (AFF) SNN, which equips each hidden neuron with a power-law weighted memory buffer of its past membrane states, thereby forming a fractionally-inspired activity trace that drives an adaptive threshold: as the accumulated trace rises with sustained firing, the effective threshold increases and suppresses further spikes, implementing an intrinsic negative feedback loop. Ten independent seeds were evaluated with a fixed random train/test split of2969/743heartbeat windows (lengthT=180;3712balanced windows total;1856normal,1856arrhythmia) drawn from three publicly available Massachusetts Institute of Technology-Beth Israel Hospital records, with spikes per sample as the primary energy proxy and test accuracy and the area under the receiver operating characteristic curve as secondary endpoints. Spikes/sample decreased from2384.990±537.719(integer-order) to891.481±227.654(AFF), a62.62%reduction (bootstrap 95% confidence interval[54.00%,69.35%]), which was statistically significant (pairedt-testp=0.000015two-sided; Wilcoxon signed-rankp=0.001953two-sided; paired effect sizedz=-2.662). Accuracy was preserved (78.923±1.725%vs79.206±1.541%; pairedt-testp=0.227761), and cross-seed spike variance decreased by a factor of5.58×. AFF suppresses noise-driven hyperactivity and stabilises event traffic, delivering a statistically significant reduction in spikes/sample without accuracy loss and improving predictability for low-power neuromorphic ECG deployment.
30 May 2026
Read appraisal →Journal of medical Internet research
Effects of Multicomponent Digital Health Interventions on Multidimensional Physical Activity in Older Adults: Systematic Review, Meta-Analysis, and Meta-Regression of Randomized Controlled Trials
BACKGROUND: The comprehensive effects of multicomponent digital health interventions (DHIs) on multidimensional physical activity indicators and sedentary behavior (SB) remain controversial. OBJECTIVE: This systematic review aimed to evaluate the impact of multicomponent DHIs on daily steps, moderate-to-vigorous physical activity (MVPA), light physical activity, total physical activity, and SB in older adults. METHODS: PubMed, Web of Science, Embase, The Cochrane Library, and CINAHL were searched up to February 20, 2026. Randomized controlled trials concerning multicomponent DHIs for promoting exercise behavior in older adults were included. RoB 2.0 was used to evaluate study quality. Meta-analyses were performed using the Hartung-Knapp-Sidik-Jonkman random-effects model, and 95% prediction intervals (PIs) were calculated via Nagashima adjustment to evaluate effect dispersion. The GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) system was used to evaluate evidence certainty. RESULTS: A total of 26 randomized controlled trials (n=4129) were included. The results showed that multicomponent DHIs significantly improved daily steps (mean difference [MD] 822.8, 95% CI 198.3 to 1447.3 steps/d; 95% PI -1452.4 to 3098.0) and MVPA (MD 45.9, 95% CI 23.9 to 67.9 min/wk; 95% PI -9.4 to 101.2). However, the improvements in SB (MD -283.7, 95% CI -610.8 to 43.5 min/wk; 95% PI -984.5 to 417.1), total physical activity (MD 104.4; 95% CI -109.2 to 318.0 min/wk; 95% PI -444.4 to 653.2), and light physical activity (MD 39.3, 95% CI -96.2 to 174.7 min/wk; 95% PI -227.6 to 306.2) did not reach statistical significance. As some included studies combined digital tools with human support, the independent contribution of digital technology remains uncertain. PIs indicated a certain degree of dispersion across different clinical contexts. Subgroup analysis showed higher effect sizes for standalone wearables, human-assisted interventions, and populations with chronic disease risks. Meta-regression showed that effect sizes remained stable across different ages and durations. The trim-and-fill method confirmed the robustness of MVPA results. GRADE assessment indicated "moderate" certainty for MVPA and "low" for daily steps and other indicators. CONCLUSIONS: This systematic review suggests that multicomponent DHIs may serve as an effective means for enhancing daily steps and MVPA in older adults. The innovation lies in evaluating the true effect distribution of multicomponent DHIs through Hartung-Knapp-Sidik-Jonkman random-effects models and Nagashima PIs. Compared with previous studies, this review identified the impact of population characteristics and control group differences on effect estimates using PI and subgroup models, confirming that advanced age did not significantly diminish the good adaptability of older adults to DHIs. Evidence limitations include high heterogeneity, lack of long-term follow-up, and differences between objective and subjective measurement tools. In practice, priority should be given to hardware carriers with simplified interaction and integrated human support, with tailored strategies developed for different risk subgroups.
30 May 2026
Read appraisal →Journal of neurology
Artificial intelligence for gait and balance in neurological disorders: a scoping review of clinical applications and technologies
BACKGROUND: Artificial intelligence (AI) has rapidly emerged within healthcare systems and neurological rehabilitation with the potential to revolutionize clinical decision-making and therapeutic strategies. However, a comprehensive understanding of how AI is currently applied to gait and balance rehabilitation in stroke, Parkinson's disease (PD), and multiple sclerosis (MS) is still lacking. OBJECTIVE: To map the current use of AI in neurological rehabilitation, focusing on clinical purposes, geographical distribution, and applied technologies for gait and balance rehabilitation. METHODS: Following the PRISMA-ScR statement, we conducted a literature search through MEDLINE, Cochrane CENTRAL, EMBASE, and Google Scholar up to July 2025 to identify studies applying AI-based methods to gait and balance outcomes in adults with stroke, PD, or MS. Study characteristics, AI methods, validation strategies, clinical purpose, and motor outcomes were extracted and synthesized narratively. RESULTS: Eighteen studies published from 2009 to 2025 were included. Most studies were conducted in Asia (50%) and involved people with stroke (77.8%). AI was predominantly used for prognostic purposes (72.22%), such as predicting falls, gait recovery, or treatment response and diagnostic applications (33.3%). Machine learning was the most common approach (88.9%) with Random Forest, Support Vector Machine, logistic regression, and eXtreme Gradient Boosting being the most frequently applied algorithms. None of the included studies performed prospective or external validation on independent datasets. CONCLUSIONS: This scoping review provides a comprehensive overview of current AI applications, highlighting promising but still immature approaches in the neurological rehabilitation of gait and balance. However, substantial methodological limitations remain major barriers to clinical translation.
30 May 2026
Read appraisal →African journal of reproductive health
Artificial intelligence-enabled digital reproductive health education: corpus-based strategies for strengthening Spanish health communication capacity
This study examines how corpus-grounded artificial intelligence (AI) can strengthen Spanish reproductive health communication capacity within China's digital health ecosystem. A mixed-methods design was employed, combining corpus linguistics, AI-assisted message generation, expert-informed evaluation, and quantitative user assessment. A domain-specific Spanish reproductive health corpus was constructed from 3,052 documents, including clinical guidelines, patient education materials, and FAQs/user queries, yielding 1,065,110 tokens for linguistic analysis and AI grounding. Corpus-derived readability benchmarks, lexical simplification rules, and discourse patterns were integrated into an AI content generation pipeline to produce reproductive health messages, which were then compared with non-corpus-grounded AI outputs. The user evaluation phase was conducted among 240 Spanish-speaking or Spanish-proficient adults in selected Chinese metropolitan cities. Data were collected through a structured questionnaire measuring Corpus-Grounded AI Generation, Spanish Communication Quality, and Health Communication Capacity, and were analyzed using reliability testing, correlation analysis, and structural equation modeling. The findings showed that corpus-grounded AI significantly improved Spanish communication quality, while communication quality had the strongest effect on users' comprehension, confidence, and help-seeking intention. Mediation analysis further demonstrated that Spanish communication quality significantly mediated the relationship between corpus-grounded AI generation and health communication capacity. The study concludes that linguistically informed AI design can enhance the clarity, accessibility, and effectiveness of reproductive health education and offers a practical framework for multilingual digital health communication in sensitive healthcare contexts. Cette étude visait à examiner le rôle de l'IA guidée par la linguistique de corpus dans l'amélioration de la qualité de la communication en espagnol sur la santé reproductive au sein de l'écosystème numérique chinois. En utilisant une approche à méthodes mixtes, la recherche a combiné l'analyse de corpus de textes espagnols sur la santé reproductive, la génération de contenu basée sur l'IA et l'évaluation par les utilisateurs. Un corpus espagnol spécifique au domaine de la santé reproductive a été construit et intégré dans un pipeline de génération d'IA ancré dans le corpus pour optimiser la lisibilité, l'exactitude et la sensibilité culturelle. La qualité de la communication et la capacité de communication en santé ont été mesurées à l'aide d'une analyse de fiabilité, de tests de corrélation et de modélisation par équations structurelles. Les résultats ont révélé que l'IA ancrée dans le corpus améliore significativement la qualité de la communication en espagnol, ce qui améliore à son tour la compréhension des utilisateurs, l'auto-efficacité et les intentions de recherche d'aide. La qualité de la communication est apparue comme un mécanisme médiateur clé entre la conception de l'IA et les résultats en matière de communication santé. Sur le plan théorique, l'étude établit une base pour intégrer la linguistique de corpus dans les systèmes d'éducation à la santé pilotés par l'IA, tandis que sur le plan pratique, elle propose un cadre évolutif pour les plateformes multilingues d'éducation à la santé reproductive. Ces résultats ont des implications importantes pour les politiques mondiales de santé numérique, la gouvernance de l'IA et les stratégies de communication en santé reproductive.
30 May 2026
Read appraisal →Journal of the Egyptian National Cancer Institute
Artificial intelligence in cancer immunotherapy: current trends in predicting response and personalizing treatment
Artificial intelligence (AI) can transform cancer immunotherapy by enabling more accurate prediction of treatment responses, the discovery of specific biomarkers, and the development of personalised treatment plans. Traditional single-marker biomarkers (PD-L1, TMB, MSI) lack consistency across tumour types and cannot be used to assess tumour heterogeneity or the dynamic tumour microenvironment (TME). This review synthesises developments in multimodal AI models that combine genomics, transcriptomics, radiomics, digital pathology (pathomics), circulating biomarkers, and clinical evidence to create composite predictive signatures with significantly better discriminatory value. AUCs over 0.8 have been seen in a few retrospective studies with deep learning and ensemble models on whole-slide images, CT/MRI/PET radiomics, spatial and single-cell omics, and multi-omics fusion models, but prospective and multicentre validation is scarce, and external validation often shows deterioration in performance. AI is also used to enhance the translational pipelines of adoptive cell therapies (e.g., CAR-T) by improving patient selection, manufacturing (e.g., digital twins), and early toxicity prediction (e.g., CRS, ICANS). Nevertheless, clinical implementation remains hindered by data heterogeneity, bias, poor longitudinal validation, limited reproducibility, and a lack of transparency in most models, even though prospective, multicenter validation and explainable AI are crucial for clinician trust and regulatory acceptance. New systems such as federated learning, foundation models, spatial omics, digital twins, and wearable monitoring represent paths to generalizable, privacy-preserving, and actionable systems in clinical practice. To achieve the potential of AI, the generation of data will need to be standardized, reporting must be transparent, interdisciplinary, and regulatory frameworks must be strengthened focusing on the practical use of AI and patient safety. By taking these steps, AI could be shifted to prospective clinical decision support, which uses AI to meaningfully enhance personalization and outcomes in cancer immunotherapy based on a retrospective research tool.
30 May 2026
Read appraisal →American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting
Artificial Intelligence in Oncology: Clinical Applications, Challenges, and Opportunities
Artificial intelligence (AI) is reshaping cancer research and clinical oncology by enabling large-scale analysis of complex biomedical data. Although early AI efforts focused on single-modality tasks such as imaging interpretation or pathology classification, contemporary oncology increasingly requires integrated reasoning across heterogeneous inputs, including imaging, digital pathology, and genomics. Multimodal artificial intelligence (MMAI) models represent a major evolution by fusing these data types into unified predictive systems capable of informing diagnosis, risk stratification, treatment selection, and disease monitoring. This manuscript reviews the state of AI in oncology and highlights emerging foundation models within the field. There will be a focus on histopathology, and how this can be incorporated into risk assessment models. There will also be examples of MMAI utility in radiology and spatial proteomics. Additionally, we will discuss ethical and legal implications of MMAI in modern oncology, and recommendations for responsible application within clinical oncology practice.
30 May 2026
Read appraisal →JMIR medical informatics
Blockchain Smart Contracts for Automating Clinical Trials: Systematic Review and Proposed System Architecture
BACKGROUND: Blockchain technologies have revolutionized the financial sector through their ability to generate immutable, cryptographically secure records. Clinical trials and health care data possess several synergies with those of the financial sector, specifically pertaining to the importance of tamper-resistant recording of processes. The evolution of blockchain to autonomously execute tasks contingent upon predefined contractual terms via smart contracts (SCs) allows a dynamic chain of interlinked events to unfold independently and in sequence, with time-stamped records. In recent years, mistrust in clinical trial data has grown significantly. Recording the entire clinical trial lifecycle from application, registration, recruitment to conduct, finance management, statistical analysis, and reporting in an immutable, cryptographically secure ledger with SC execution of trial processes could limit the potential for human intervention and tampering. This would produce a time-stamped record of all events within the trial lifecycle. Leveraging the capabilities of SCs could alleviate recruitment challenges and address ongoing concerns regarding data transparency, ownership, and integrity that currently undermine clinical trial processes. OBJECTIVE: This study aimed to review the existing literature on SC applications in clinical trials and propose a system architecture for using SCs to automate key processes throughout the clinical trial lifecycle. METHODS: A systematic search was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, identifying peer-reviewed studies and open-source repositories pertaining to the implementation of SCs in clinical trials. Data were extracted specific to the stage of the trial lifecycle described, SC architecture, and technical specifications for real-world implementation. Data were synthesized to propose an architecture for automating clinical trial processes within the lifecycle using SCs. RESULTS: A total of 144 records were screened; 10 studies met the inclusion criteria. Most implementations used private Ethereum-based networks (7/10, 70%). Reported applications included automated patient matching (5/10, 50%), consent management with dynamic permissioning (6/10, 60%), protocol enforcement and time-stamped audit logs (9/10, 90%), adverse event reporting (3/10, 30%), and financial or workflow automation (3/10, 30%). SC-based recruitment systems demonstrated rapid matching performance (eg, 6000 simulated patients matched in 2.13 s). However, all included systems were prototypes or simulations, and none were tested in real-world regulatory settings. Scalability, interoperability limitations, regulatory ambiguity (eg, General Data Protection Regulation right-to-erasure conflicts), and high infrastructural complexity were common gaps noted across studies. CONCLUSIONS: Current evidence suggests that SCs can enhance transparency, traceability, and automation throughout the clinical trial lifecycle. However, the literature remains dominated by simulation-based prototypes, primarily Ethereum-dependent architectures, and lacks analyses of cost-effectiveness, governance, and integration with institutional workflows. Future research should evaluate hybrid architectures, develop interoperability standards, and assess regulatory and ethical implications in real deployments.
30 May 2026
Read appraisal →African journal of reproductive health
Reproductive health in the digital and social age: AI, education, law, and community-based interventions.
Reproductive health has increasingly emerged as a complex, multidimensional field that extends far beyond clinical care into the domains of education, technology, law, culture, and socio-economic systems. This special issue of the African Journal of Reproductive Health brings together fifteen diverse yet interconnected papers that collectively illuminate how reproductive health outcomes are shaped by dynamic interactions across disciplines, geographies, and social structures. The contributions - spanning China, Spain, and Pakistan - underscore a unifying premise: that sustainable reproductive health improvements depend on integrative approaches that harness innovation while remaining grounded in cultural and social realities. La santé reproductive s'affirme de plus en plus comme un domaine complexe et multidimensionnel qui dépasse largement le cadre des soins cliniques pour englober l'éducation, la technologie, le droit, la culture et les systèmes socio-économiques. Ce numéro spécial de l'African Journal of Reproductive Health rassemble quinze articles divers mais interconnectés qui, ensemble, mettent en lumière la manière dont les résultats en matière de santé reproductive sont façonnés par des interactions dynamiques entre disciplines, territoires et structures sociales. Ces contributions, provenant de Chine, d'Espagne et du Pakistan, soulignent un principe unificateur : l'amélioration durable de la santé reproductive repose sur des approches intégratives qui exploitent l'innovation tout en restant ancrées dans les réalités culturelles et sociales.
30 May 2026
Read appraisal →ACS applied materials & interfaces
Breathable and Washable E-Textile with Readily Integrated Piezoelectric Perfluoroalkoxy Fiber for Wide Temperature Range Human Machine Interfaces
Flexible wearable sensors are widely explored for human-machine interaction and health monitoring. Electronic textiles (E-textiles) offer a promising pathway toward comfortable wearable devices by integrating sensing fibers directly into fabric structures. Among them, compared with piezoresistive and capacitive fiber sensors, piezoelectric fiber sensors require no external power supply. Compared with triboelectric fiber sensors, which are highly sensitive to humidity and temperature, piezoelectric fiber sensors provide more stable and reliable electrical signals. However, conventional piezoelectric fiber sensors still face challenges in achieving long-term wearing comfort and reliability under harsh environments. Herein, we develop a breathable, washable, and wide-temperature-tolerant based on piezoelectric perfluoroalkoxy (PFA) fibers, which can be readily integrated into E-textiles. The textile is produced via a weft knitting process that integrates coaxially structured PFA fibers with wool yarns, combining stable piezoelectric performance with wearing comfort. The fibers exhibited a charge sensitivity of (2.6 ± 0.2) pC/N at 0.2 MPa. The resulting E-textile exhibits a charge sensitivity of up to (316.6 ± 45.9) pC/N and a voltage sensitivity of (744.9 ± 73.9) mV/N. The textile maintains stable operation over a wide temperature range from -78 °C to 150 °C. Its weft-knitted structure provides breathability, while the textile also offers washability, UV resistance, and mechanical durability exceeding 10,000 stretching or bending cycles. Furthermore, the textile can effectively monitor various human motions. A smart glove constructed from five-channel PFA fibers achieved recognition accuracies above 95% for five hand gestures, demonstrating the potential offer self-powered E-textile as a wearable sensor for human-machine interaction.
30 May 2026
Read appraisal →ACS applied materials & interfaces
A High-Sensitivity MXene Tactile Sensor with Dynamic Point-Contact Networks for a Wearable Healthcare Device
The self-stacking of the MXene material can diminish its interlayer spacing and active surface area, thereby diminishing the sensitivity and response speed of pressure sensors. Here, we introduce a high-performance flexible tactile sensor utilizing a composite of MXene, polystyrene (PS) microspheres, and bacterial cellulose (BC). PS microspheres are integrated within the MXene nanosheet layers to mitigate the self-stacking. By employing an innovative point-to-point conductive network design, the self-aggregation issue of MXene materials is effectively mitigated, leading to the formation of dynamic point-contact structures among PS microspheres. This configuration notably enhances detection sensitivity within the low-pressure range to 568.8 kPa-1 (20-200 Pa). Furthermore, the incorporation of BC enhances the interfacial bonding strength through hydrogen bonding, thereby enhancing sensor stability. The sensor exhibits a rapid response time of 29 ms, a wide detection range of up to 0-50 kPa, a low limit of detection of 2 Pa, and consistent signal stability over 20,000 cycles. It has demonstrated the capability to monitor a broad spectrum of physiological activities, including joint movements, facial microexpressions, and radial pulse. Integration with wireless transmission technology enables remote health monitoring, flexible touch keyboard functionality, and human-computer interaction. Additionally, the electromyography (EMG) skin sensor based on the MXene/PS/BC (MPB) composite film accurately distinguishes muscle movements, gestures, acoustic vibrations, and facial microexpressions by detecting surface EMG signals. This comprehensive approach offers a comprehensive solution for wearable medical devices that combine high sensitivity with practicality.
30 May 2026
Read appraisal →Nano letters
Hierarchical Structural-Interfacial Engineering with Dynamic Soft-Hard Cross-Linking Enables Full-Range, Ultrasensitive MXene Piezoresistive Sensors
Human tactile perception inspires flexible piezoresistive sensors, yet simultaneously achieving high sensitivity, a wide pressure range, and mechanical robustness remains challenging. Here, we report a hierarchical stress-regulation strategy that integrates multiscale surface microstructures with a dynamically cross-linked MXene/carboxymethyl cellulose/borax sensing network. Replicated microtopographies induce progressive and spatially distributed stress localization, while heterogeneous soft-hard cross-linking regulates nanoscale deformation through adaptive hydrogen bonding and rigid borate anchoring. This coupled structural-interfacial regulation generates abundant stress-concentrated sites, stabilizes conductive pathways, and enables continuous resistance modulation across a broad pressure spectrum. Consequently, the sensor exhibits ultrahigh sensitivity (774.48 kPa-1), a wide working range (334.16 kPa), and fast response/recovery times (8.58/17.22 ms). It reliably captures both subtle physiological signals and large mechanical loads and further supports gesture recognition and robotic control when integrated with real-time feedback and machine learning. This work establishes a general framework for designing robust, full-range tactile sensors through hierarchical stress regulation.
30 May 2026
Read appraisal →Science robotics
Embodied or virtually represented: Navigating the embodiment debate in human-robot interaction.
The validity of virtually represented robots in HRI experiments depends on when, where, and for whom it matters.
30 May 2026
Read appraisal →Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Predictability-Computability-Stability workflow for veridical data science in the age of artificial intelligence
Data science is a pillar of artificial intelligence (AI), which is transforming nearly every domain of human activity, from the social and physical sciences to engineering and medicine. While data-driven findings in AI offer unprecedented power to extract insights and guide decision-making, many are difficult or impossible to replicate. A key reason for this challenge is the uncertainty introduced by the many choices made throughout the data science life cycle (DSLC). Traditional statistical frameworks often fail to account for this uncertainty. The Predictability-Computability-Stability (PCS) framework for veridical (truthful) data science (VDS) offers a principled approach to addressing this challenge throughout the DSLC. This paper presents an updated and streamlined PCS workflow, tailored for practitioners and enhanced with guided use of generative AI (GenAI). We include a running example to display the PCS framework in action and conduct a related case study that showcases the uncertainty in downstream predictions caused by judgement calls in the data cleaning stage. This article is part of the theme issue 'Statistical workflow'.
30 May 2026
Read appraisal →World journal of urology
Assessing multiple chatGPT versions on novel content in the Taiwan urology board examination: accuracy, speed, and domain-specific performance
PURPOSE: Large language models demonstrate increasing utility in healthcare; however, their capacity to handle newly developed medical content beyond training data boundaries remains unclear. This study evaluated the performance of four ChatGPT models on 120 newly developed questions from the 2024 Taiwan Urology Board Examination, all created after the models' October 2023 training cutoff, thereby reducing the risk of prior model exposure. METHODS: Model performance was assessed using accuracy and processing time. Of 150 examination items, the first 120 newly developed questions constituted the primary analysis set; the remaining 30 previously used archival questions were excluded to reduce potential bias from prior exposure. The primary paired accuracy comparison evaluated o1-preview versus GPT-4o across all 120 questions. Additional analyses across 12 urological subspecialties and question-complexity strata were treated as exploratory. RESULTS: Across all 120 questions, o1-preview achieved 66.7% accuracy and significantly outperformed GPT-4o (55.8%; p = 0.012), although with longer processing time (19.20 vs. 14.94 s). Performance varied significantly across 12 urological subspecialties (p < 0.001). Unlike the other tested models, o1-preview showed slightly higher accuracy on high-complexity than on low-complexity questions (68.3% vs. 65.0%). On high-complexity surgical anatomy items, o1-preview achieved 80.0% accuracy, whereas GPT-4o and GPT-4o mini both scored 0.0% (p = 0.024). CONCLUSION: On this newly developed post-cutoff, text-based urology board question set, o1-preview achieved higher overall accuracy than GPT-4o, particularly on high-complexity surgical anatomy items, at the cost of longer response times. These findings suggest that reasoning-oriented models may offer advantages for selected specialty-level assessment tasks, although generalizability to other languages, modalities, and clinical settings requires further validation.
30 May 2026
Read appraisal →Current atherosclerosis reports
Patient Engagement Strategies for Implementing Evidence-Based Care in Atherosclerosis
PURPOSE OF THE REVIEW: Despite continuing therapeutic advances in cardiovascular disease, it remains a leading cause of morbidity and mortality in the US. The current challenge is not the development of therapeutics but rather patient engagement and compliance with medical recommendations. Here in this narrative expert review we discuss strategies to improve patient engagement in cardiovascular care. RECENT FINDINGS: There is a varying level of data surrounding different patient education strategies in the care of atherosclerosis. There are three main categories of interventions reviewed with evidence of efficacy: personalized and accessible patient education, digital health interventions, and implementation of personalized medicine via genetic testing. Implementing strategies in the clinic such as personalizededucation at an accessible health literacy level, digital health interventions,and even genetic testing may improve patient engagement and outcomes.
30 May 2026
Read appraisal →BMJ open
Feasibility and acceptability of therapist-guided, asynchronous, internet-delivered trauma-focused CBT for adolescents with PTSD: a single-group feasibility trial in Sweden
OBJECTIVES: Trauma-focused cognitive behavioural therapy (TF-CBT) is the established first-line treatment for paediatric post-traumatic stress disorder (PTSD), but access to evidence-based care remains limited. This study aimed to evaluate the feasibility and acceptability of a therapist-guided, 12 week, internet-delivered TF-CBT (iTF-CBT) programme for adolescents with PTSD and to explore preliminary changes in PTSD symptoms. DESIGN: Single-group feasibility trial. SETTING: Save the Children, Sweden. PARTICIPANTS: Twenty-two adolescents (13-17 years, 82% female) with primary PTSD. INTERVENTIONS: A 12 week, therapist-guided, asynchronous, internet-delivered TF-CBT comprising eight modules and parallel caregiver modules with joint adolescent-caregiver activities. OUTCOMES: Feasibility measures included recruitment pace, participant retention, treatment adherence (module completion) and therapist time. Acceptability was evaluated through satisfaction, credibility, negative effects and reported adverse events. Within-group changes in PTSD severity using independent evaluator-rated Clinician-Administered PTSD Scale (CAPS-CA-5) and the self-reported Child and Adolescent Trauma Screen 2 (CATS-2) were used as indicators of potential clinical change. Assessments occurred at baseline, during treatment, post-treatment and at 1 month follow-up (primary endpoint). RESULTS: Recruitment was completed after 7 months of active enrolment. Retention and adherence were high, satisfaction and credibility ratings were favourable, and no intervention-related serious adverse events occurred. Within-group improvements were observed at the primary endpoint, with large reductions on CAPS-CA-5 (Cohen's d=1.27) and CATS-2 (Cohen's d=1.51). At follow-up, 47.6% of participants no longer met criteria for PTSD. CONCLUSIONS: Therapist-guided iTF-CBT for adolescents with PTSD was safe, feasible, acceptable and associated with potentially meaningful symptom improvements. These findings support further evaluation in larger, controlled trials to determine efficacy, cost-effectiveness and long-term outcomes. TRIAL REGISTRATION NUMBER: NCT06185244.
30 May 2026
Read appraisal →JMIR formative research
Real-World Implementation of EndoConnect in Brazilian Primary Care: Formative Study of Usability, Engagement, and Equity in Digital Endometriosis Care
BACKGROUND: Endometriosis is a chronic gynecological condition affecting approximately 10% of women of reproductive age worldwide and is associated with chronic pelvic pain, infertility, and reduced quality of life. In Brazil's Unified Health System (Sistema Único de Saúde [SUS]), diagnostic delays frequently range from 7 to 10 years and disproportionately affect socially vulnerable populations, including rural, low-income, Black, and Indigenous women. Digital health interventions have been proposed as scalable solutions; however, most available applications are developed in high-income settings and do not align with the structural and operational realities of low- and middle-income countries (LMICs). OBJECTIVE: This study aimed to evaluate feasibility, usability, acceptability, and user engagement associated with the real-world implementation of EndoConnect Alpha in primary health care settings, and to explore preliminary patterns of change in symptom burden, knowledge, and care navigation. METHODS: A single-arm, prospective, formative implementation study was conducted in 10 primary health care units in Ceará, Brazil. A convenience sample of 60 participants, including women with suspected or confirmed endometriosis and primary care professionals, used the platform over an 8-week period under real-world conditions. Usability (assessed using the System Usability Scale), acceptability (assessed using the Technology Acceptance Model), engagement metrics, and exploratory outcomes were assessed. All analyses were exploratory, with no control group and no causal inference. RESULTS: High usability and acceptability were observed, with strong user engagement, including a 79% completion rate of educational modules and consistent platform use. Observed decreases in pelvic pain and anxiety were identified, alongside increases in disease-related knowledge, self-reported therapy adherence, and reported gynecological referrals. A positive association between usability and acceptability was also observed. These findings should be interpreted as exploratory signals given the study design. Descriptive subgroup analyses suggested more pronounced trends among rural participants and those with a lower education level. CONCLUSIONS: The real-world implementation of EndoConnect Alpha demonstrated high feasibility, usability, and acceptability within a public primary care setting in a middle-income country. Observed trends suggest potential benefits, particularly among underserved populations; however, causal inference cannot be established. These findings support further controlled evaluation and highlight the relevance of equity-oriented digital health strategies tailored to LMIC contexts.
30 May 2026
Read appraisal →PLoS neglected tropical diseases
Feasibility study of an AI-powered mobile app to support cutaneous leishmaniasis diagnosis in the Brazilian Amazon
BACKGROUND: Cutaneous leishmaniasis (CL) remains a major public health challenge, especially in Brazil's Amazon, where environmental and economic pressures sustain transmission. Delayed diagnosis drives morbidity, stigma, and costs. Community health workers are pivotal yet under-equipped for early triage. Artificial intelligence, effective in dermatologic imaging, is underused for CL; feasible, offline clinical tools could accelerate referral and timely care. This study aimed to evaluate the feasibility of an AI-assisted tool for early CL triage in the Brazilian Amazon by: (i) developing an AI model for CL identification from clinical skin lesion images; (ii) integrating the model into an offline-optimized mobile application for resource-limited settings; and (iii) conducting initial, real-world clinical validation. METHODS: Exploratory, IRB-approved feasibility study. Retrospective images from Brazilian Amazon sites informed a two-stage AI pipeline (lesion segmentation+classification) integrated into an offline mobile app. Multicenter validation in ideal- and real-world scenarios. Primary metric AUC-ROC; secondary sensitivity/specificity. One-sided tests: AUC-ROC [Formula: see text]0.70; ideal-world sensitivity [Formula: see text]0.75. RESULTS: For the Classification Model, 64 images were assigned to the test set while 1,160 were used for training and validation (80:20 split), with DenseNet121 yielding the highest accuracy of 0.88. The full AI pipeline (Segmentation and Classification models) achieved an accuracy of 0.81, an F1-score of 0.80, an AUC-ROC of 0.90, and a sensitivity of 0.76. In the ideal-world analysis, sensitivity reached 0.92, the F1-score was 0.84, and specificity was 0.42. CONCLUSIONS: We demonstrate the feasibility of an offline, AI-assisted mobile tool to support triage and referral for cutaneous leishmaniasis in the Brazilian Amazon. Performance reflects preliminary, descriptive point estimates from an initial diagnostic accuracy assessment and should be interpreted with caution; the tool is not intended for standalone diagnosis. Next steps include prospectively powered clinical validation, usability refinements, and regulatory evaluation, including alignment with ANVISA requirements in Brazil. Overall, this work represents an initial step toward closing the gap in clinically supported diagnostic tools for neglected tropical diseases in resource-constrained settings.
30 May 2026
Read appraisal →JMIR cardio
Barriers and Facilitators to Maintaining Physical Activity After an mHealth Intervention for People Poststroke or Transient Ischemic Attack: Reflexive Thematic Analysis
BACKGROUND: Regular physical activity is critical for preventing secondary stroke following a stroke or transient ischemic attack (TIA). Although mobile health (mHealth) interventions have shown promise for promoting short-term increases in physical activity, evidence on their long-term effects and the mechanisms that support sustained behavior change remains limited. In particular, little is known about how people poststroke or TIA integrate the skills, knowledge, and habits gained through mHealth interventions into their daily lives once structured intervention support ends. OBJECTIVE: This study aimed to explore the perceived barriers and facilitators of maintaining physical activity among individuals poststroke or TIA after completing an mHealth intervention. METHODS: A qualitative approach was used, involving a strategic sample of 12 participants recruited after they had completed a 6-month mHealth intervention for people poststroke or TIA. The intervention included supervised physical therapy with mHealth support for physical exercises and behavior change (eg, counseling, goal-setting, and self-monitoring), followed by a 6-month postintervention period with access to self-managed mHealth support. To enable richness and depth in participants' accounts, the dataset consisted of 2 semistructured interviews conducted 3 months and 6 months after completing the intervention, along with participant-generated photographs. Between the interviews, participants took photos to reflect their experiences of maintaining physical activity after the intervention. These images served as prompts for dialog and reflection during the second interview. Data were analyzed using reflexive thematic analysis. RESULTS: We generated 3 themes: building experience and knowledge to maintain physical activity, staying physically active in a complex life situation, and the meaning of context for maintaining physical activity after the intervention. Barriers and facilitators were conceptually integrated into a comprehensive understanding of maintaining physical activity, symbolized by the metaphor of a soap bubble, which requires persistence and a supportive environment to stay afloat. The complexity of participants' health and life situations created barriers to maintaining physical activity. To overcome these barriers, developing personal strategies within a supportive social and physical context was crucial. This development was facilitated by the mHealth intervention, which enabled knowledge acquisition of the principles of physical activity after stroke or TIA, along with increased awareness of its health benefits. CONCLUSIONS: mHealth interventions for people poststroke and TIA can serve as a catalyst for physical activity engagement and an enhancer of the knowledge and experience necessary to maintain physical activity after the intervention. Despite health-related and contextual barriers, participants may use personalized strategies, supported by their social and environmental context, to navigate these challenges. These insights highlight opportunities for future mHealth interventions to strengthen the interaction between individuals and their environment and empower tailored strategies for behavior change and long-term physical activity maintenance.
30 May 2026
Read appraisal →Journal of medical Internet research
Can Digital Tools Fix Bias in Mental Health Triage?
Recent advances in digital technologies hold both promise and potential pitfalls for mental health triage. In this News and Perspectives article, JMIR Correspondent Beth Rush reports on the issue of bias and the role of digital tools in the mental health context.
29 May 2026
Read appraisal →Journal of medical Internet research
Detection of Microbehavior Intervals for Predicting Mental Health: Clinically Relevant and Advanced Multimodal Temporal Analysis
BACKGROUND: Health care workers (HCWs) face sustained psychological demands that place them at heightened risk for burnout and posttraumatic stress disorder (PTSD). However, assessing psychological distress in this population remains challenging because of stigma, underreporting, and the limitations of self-report tools. Although nonverbal behaviors such as facial expressions and gaze hold diagnostic promise, most approaches overlook the fine-grained, temporal fluctuations in these signals. In this study, we focused on microbehavior intervals-brief, involuntary changes in multimodal nonverbal signals-that emerge during emotion-eliciting interviews. OBJECTIVE: This study aimed to determine whether microbehavior intervals improve the discrimination of psychological distress profiles among HCWs with symptoms of burnout and PTSD. METHODS: HCWs participated in a semistructured interview that included 5 work-related, emotionally charged questions and that was recorded via Webex (online video platform). Participants also completed validated questionnaires for burnout (Maslach Burnout Inventory General Survey 9-item) and PTSD (PTSD checklist for Diagnostic and Statistical Manual, 5th edition). Recordings were analyzed with computer vision models to generate time-series data of facial expressions, head movement, gaze, body posture, and hand gestures. An unsupervised anomaly detection model (MOMENT [a Family of Open Time-Series Foundation Models]) isolated microbehavior intervals without requiring manual labels. Features derived from these intervals were used to train a deep learning classifier that predicted 4 symptom classes of psychological distress: "moderate-severe burnout," "subthreshold-provisional PTSD," "burnout+PTSD," and "resilient." We conducted an ablation study by systematically removing one behavioral data stream at a time. Finally, we conducted an explainability analysis to characterize the features driving model predictions. RESULTS: We analyzed 258 interview recordings from 151 HCWs. Per interview, an average of 19.65 (SD 6.01) microbehavior intervals were detected, each lasting an average of 1.31 (SD 1.10) seconds. The classifier demonstrated robust performance across classes, achieving a macro- F1-score of 0.75 and a macro area under the receiver operating characteristic curve of 0.80 on held-out data. Ablation analysis showed that excluding gaze or arousal-valence signals caused the largest performance declines, particularly in recall and F1-score. The explainability analysis revealed distinct temporal patterns across symptom classes, with irregularity and variability in microbehaviors emerging as key predictors. CONCLUSIONS: Focusing on microbehavior intervals yields a scalable, interpretable, and annotation-free framework for detecting psychological distress from nonverbal signals. By moving from whole-video features to fine-grained multimodal temporal modeling, we successfully captured subtle, involuntary fluctuations in nonverbal responses to emotion-eliciting questions. This multimodal approach enables an objective, robust, and explainable assessment of psychological distress and offers a promising complement to conventional psychometric assessments.
29 May 2026
Read appraisal →JMIR aging
Developing Customized Personas to Capture Intrinsic Capacity Profiles and Digital Monitoring Intentions in Older Adults: Mixed Methods Study
BACKGROUND: Integrated Care for Older People (ICOPE), focused on monitoring and optimizing the intrinsic capacity (IC) of older adults, is a new model of geriatric care that is currently being accelerated globally. Digital health technologies are recommended for longitudinal IC monitoring to provide precise and timely interventions. However, little is known about the psychological intentions of engaging in digital monitoring of IC according to the profile heterogeneity of IC among older adults. OBJECTIVE: This study aims to map a set of customized personas to capture the profiles of IC and match psychological intentions that support personalized digital IC monitoring. METHODS: An explanatory sequential mixed methods study was conducted at 16 sites in Beijing, China. Older adults aged ≥60 years (n=481) were selected to complete the quantitative survey. Latent profile analysis, descriptive statistics, and logistic regression analyses were performed to cluster subgroups using Mplus (Muthén & Muthén) and SPSS (IBM Corp). A subsample of participants from each profile (n=25) was purposively sampled for semistructured interviews. An inductive-deductive content analysis was used to identify similar attributes and to affirm the personas gradually. A joint statistical and thematic visualization method was used to integrate the customized personas. RESULTS: Three profiles of IC patterns emerged: "multisubdomain decline-IC imbalance group," "multisubdomain moderate-sensory deficit group," and "multisubdomain robust-whole balance group." The distribution of latent profiles was influenced by age, education, monthly per capita household income, self-rated health, and number of chronic diseases, while positively impacting older adults' functional ability. The following customized personas were captured regarding established themes: "affects my mood-anxious evader," characterized by avoidance and anxiety, low digital interest, and perceived social isolation; "capitalize on what comes-accommodative adopter," pragmatically oriented toward disease detection, with moderate digital openness but limited self-efficacy; and "more autonomy-active improver," who exhibited proactive engagement, high digital literacy, and motivation rooted in self-management and social participation. CONCLUSIONS: This study is the first to integrate latent profile analysis with customized qualitative personas to link the heterogeneity of IC with the psychological intentions underlying digital monitoring. The resulting personas model provides an actionable framework for tailoring digital IC monitoring strategies in community-based integrated care. The findings emphasize the need to align monitoring approaches with older adults' IC characteristics, psychological readiness, digital literacy, and social support to enhance engagement in digital IC monitoring.
29 May 2026
Read appraisal →Journal of medical Internet research
Using the NASSS-Complexity Assessment Tool to Evaluate the Implementation of "Cadê O Kauê?": Chat-Story Intervention for Youth Participation in Mental Health Promotion in Brazil
BACKGROUND: The global public health challenge of young people's mental health is particularly evident in Brazil, which is considered the most anxious and the fifth most depressed country globally. In response to this, a digital intervention, that takes the form of a storytelling chatbot named "Cadê o Kauê?" (translation: "Where is Kauê?"), was coproduced by an interdisciplinary team of researchers, young people, and narrative designers as part of a project titled Engajadamente. The intervention was designed to strengthen Brazilian adolescents' skills in promoting their peers' mental health through direct support and collective action for mental health. The intervention was rolled out in select schools in Brazil and online via social media, but it encountered challenges during the initial implementation phase in school settings. OBJECTIVE: This project's primary objective is to evaluate the implementation of "Cadê o Kauê" in school settings in Brazil, assess current and potential complexities, and formulate recommendations to reduce or manage these complexities. The secondary objective of this project is to understand the current and potential sociotechnical change resulting from the implementation of the "Cadê o Kauê" in a real-world setting. METHODS: The Non-adoption, Abandonment, Scale-up, Spread, Sustainability (NASSS) framework was selected as the theoretical framework to identify different areas of complexity across domains and further thinking of ways to reduce or manage these complexities. Data were gathered by conducting semistructured online interviews with the Engajadamente team members based in the United Kingdom and Brazil. The NASSS-Complexity Assessment Tool was used as a sensemaking device to facilitate a collective understanding of the implementation, and narratives were mapped onto specific NASSS domains. Key themes were identified across overarching domains using thematic analysis to meet the objectives of this evaluation. RESULTS: The results mapped onto the NASSS domains generated a narrative about the initial implementation of "Cadê o Kauê?" in schools. Existing and potential complexities across technology, organization, and wider system domains were characterized by interdependencies, unintended consequences, and uncertainties. The implementation of "Cadê o Kauê?" highlighted challenges related to internet dependency, infrastructure limitations, and varying levels of organizational readiness, teacher preparedness, and school culture. Addressing these through offline solutions, additional teacher training, whole-school engagement strategies, and a value-informed complexity approach can enhance accessibility, alignment with school priorities, and adaptability within Brazil's dynamic sociopolitical context. CONCLUSIONS: Using the NASSS framework, the evaluation captured both current and emerging complexities in implementing "Cadê o Kauê?" in schools. By engaging with theory-informed approaches to technology implementation projects and deep-diving into discursive dynamic interactions between technology and its "context" of implementation, we have explored ways to manage these complexities and developed suitable recommendations to guide the Engadajamente team and support similar projects in the future.
29 May 2026
Read appraisal →JMIR formative research
Self-Monitoring Risk Factors for Diabetic Foot Ulceration With the Feetchecker App: Mixed Methods Study
BACKGROUND: A prevalent and serious complication of diabetes mellitus is the development of diabetic foot ulcer (DFU). There is a need for effective solutions that help prevent DFU to support our increasingly stressed health care systems. The use of mobile health (mHealth) tools has been shown to improve awareness and effective self-care management skills in people at risk of developing diabetic foot ulceration. OBJECTIVE: In this study, we aimed to investigate the perceived usefulness, engagement, and overall user experience of the Feetchecker app, a self-monitoring mHealth app for people at risk of DFU. METHODS: A total of 24 patients (mean age 71, SD 8.6 years) with type 2 diabetes mellitus at risk of developing diabetic foot ulceration completed a 3-month evaluation period (70 recruited, 36 included, 12 dropped out) of a self-monitoring mobile app called Feetchecker app. A mixed methods approach was used to combine insights from app data with qualitative data from a pre- and postsurvey as well as interviews with patients and involved podiatrists. Data were analyzed using descriptive statistics and thematic analysis. We evaluated overall use of the app, patient engagement, and user experiences. RESULTS: Patients who fully completed the study conducted 393 feetchecks. In total, 7 patients sent in 9 pictures; all 7 were called for follow-up by a podiatrist. Overall, patients had a positive experience with the app and perceived the Feetchecker app as a valuable tool to monitor their feet for potential risk factors of DFU. Ease of use in performing a feetcheck and sending the podiatrist a picture was described as an important feature. Three main types of engagement with the Feetchecker app emerged: continuous, frequent, and no to little engagement. These patterns highlight enablers for self-monitoring such as ease-of-use, easy access to a podiatrist, and social support, as well as barriers such as digital skills and sustained engagement. Podiatrists highlighted the benefits of having patients report potential issues quicker and the ability to monitor their patients remotely. Challenges remain in integrating the promotion of the Feetchecker app into their consultations. CONCLUSIONS: The Feetchecker app supported patients in self-monitoring risk factors associated with DFU through routine checks and quick contact with a health care professional in case of a potential issue. Overall, patients described a positive user experience and considered the app helpful. While mHealth tools are not for everyone, user engagement for many patients was high and shows that such apps can offer support for people able to use them. Future research should focus on improving usability and engagement with the app as well as extend the way patients can communicate with health care professionals beyond a picture.
29 May 2026
Read appraisal →Journal of medical Internet research
Beyond Time Saved: Implementation, Equity, and the Utility Threshold for Nursing AI Scribes
Schwabe et al's pre-post time-motion study of a domain-specific artificial intelligence (AI) speech assistant used by nurses in German long-term care provides one of the few real-world, full-shift evaluations of an AI scribe deployed to a nonphysician workforce, with paired objective observation and self-reported outcomes. This commentary points to the implications of these findings that extend well beyond the time savings headline. The study reports substantial reduction in self-reported documentation time and increased satisfaction with the documentation system, yet workplace satisfaction and the perception that AI scribes are "a good idea to implement" did not improve. Taken together, these findings show three undertheorized issues for AI scribe implementation in nursing and long-term care. First, postimplementation increases in time spent reviewing entries and retrieving information indicate that AI scribes redistribute cognitive effort from authoring to verification, with unknown consequences for satisfaction, mastery, and error detection. Second, the apparent paradox of rising documentation satisfaction alongside falling expectations of AI quality represents user calibration. Third, the substantial equity considerations of automatic speech recognition documentation reflect a broader trend of AI scribe studies that treat equity as a caveat, rather than treating equitable performance as empirically measurable and testable across variations in linguistic styles, dialects, and social linguistic dimensions. To advance the field, the next generation of nursing AI scribe research must treat documentation as a heterogeneous bundle of authoring, reviewing, retrieving, and verifying activities with distinct satisfaction and error profiles; specify and validate end-user-defined anchor utilities, rather than having a narrow focus on diffuse improvement; and treat equity testing and reporting of both automatic speech recognition systems and workforce adoption as standard reporting expectations, rather than caveats.
29 May 2026
Read appraisal →BMC surgery
Improvement of mobility and motivation in patients with elective colorectal resection using tracking devices and utilizing self-awareness (IMPETUS): a randomized controlled trial in a traditional non-ERAS clinical setting
BACKGROUND: Early postoperative mobilization is a cornerstone of modern perioperative care, including in elective colorectal surgery. Here we evaluated the effectiveness of smart wearables for improving patients' mobility and motivation during the recovery period after elective colorectal resection. METHODS: This prospective randomized two-armed clinical trial enrolled 62 patients undergoing elective colorectal resection. These patients were randomized into an intervention group that received hourly active reminders to mobilize via a smartwatch, and a control group that received only passive monitoring. All patients were given a wearable device that tracked their daily step counts. The primary end-point was the total number of steps taken within five postoperative days. Secondary end-points included complication rates, length of hospital stay, and subjective motivation, assessed using a structured questionnaire. RESULTS: Compared to the control group, the intervention group achieved significantly higher step counts (p = 0.039), and reported increased motivation for mobilization (p = 0.016). The groups did not significantly differ in overall complication rates or duration of hospital stay. CONCLUSION: Smart wearable devices with hourly active reminder functionalities effectively promoted postoperative mobility and motivation. TRIAL REGISTRATION: German Clinical Trials Register (DRKS), DRKS00039731. Registered 24.03.2026. Retrospectively registered.
29 May 2026
Read appraisal →PloS one
Adaptive and multi-scale feature fusion for Chinese news headline classification
The rapid growth of online news has led to an explosion of short Chinese headlines, which often suffer from sparse features, limited context, and high ambiguity-posing significant challenges for accurate classification. To address these issues, this paper proposes two tailored deep learning models: ERNIE-AAFF-SECNN for large-scale datasets, which enhances semantic representation via adaptive fusion of multi-layer ERNIE features and improves local feature extraction with SE-empowered CNN; and ERNIE-MSSE-DSCNN for small-scale datasets, which integrates multi-scale SE attention, depthwise separable convolutions, and adversarial training to boost robustness under data scarcity. A large number of experiments have shown that both of these models have achieved the most advanced performance. It is worth noting that the accuracy of ERNIE-AAFF-SECNN on the THUCNews and Toutiao datasets is 1.28% and 0.55% higher, respectively, than that of the lightweight SOTA model TinyBERT. The accuracy of ERNIE-MSSE-DSCNN on a 10% training dataset is 2.62% and 3.76% higher than that of the lightweight SOTA model TinyBERT, respectively. It demonstrates outstanding effectiveness under both standard and low-resource Settings. These results demonstrate that targeted architectural enhancements-such as adaptive feature fusion and multi-scale attention with adversarial training-can significantly improve the accuracy and robustness of short-text classification in practical Chinese news applications.
29 May 2026
Read appraisal →BMJ open
Protocol for evaluating the effects of the intelligent multimodal symptom assessment and response system in patients with pemphigus vulgaris: a randomised controlled trial
INTRODUCTION: Pemphigus vulgaris is a rare autoimmune blistering disease characterised by recurrent mucocutaneous erosions, high symptom burden and unpredictable relapse. Current management relies mainly on pharmacological therapy and hospital-based follow-up, with limited real-time monitoring and individualised support in home-based disease management. To address these challenges, this trial aims to evaluate the effectiveness of an Intelligent Multimodal Symptom Assessment and Response System, integrating patient-reported outcomes, wearable physiological data and image-based lesion assessments, to improve symptom management and quality of life in pemphigus vulgaris patients. Primary objective is to evaluate the effectiveness of the Intelligent Multimodal Symptom Assessment and Response System in improving symptom alerting and symptom management outcomes in patients with pemphigus vulgaris. Secondary objectives are to enhance patients' self-management ability in symptom monitoring and control, to improve treatment adherence throughout the follow-up period, to promote health-related quality of life among pemphigus vulgaris patients, to assess the usability and acceptability of the system from the patients' perspective. METHODS AND ANALYSIS: This is a multicentre, parallel-group, randomised controlled trial. 160 participants will be randomly assigned to either the intervention group (receiving the Intelligent Multimodal Symptom Assessment and Response System) or the control group (receiving standard care). Eligible participants will be adults aged 18 years or older with a confirmed diagnosis of pemphigus vulgaris and active skin or mucosal lesions, who are able to use digital devices and provide written informed consent. Individuals with severe comorbidities, concurrent participation in other clinical trials or cognitive impairments that may interfere with study adherence will be excluded. The intervention will be delivered via a digital platform, integrating electronic patient-reported outcomes, wearable physiological data and lesion images over a 12-week follow-up period. ETHICS AND DISSEMINATION: Ethical approval was obtained from the Institutional Review Board of the School of Nursing, Fudan University (IRB 2025-07-13), the Medical Ethics Committee of the Institute of Dermatology, Chinese Academy of Medical Sciences (2025-KY-034) and the Ethics Committee of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (2025-452). Approvals were granted on 10 September, 18 July and 23 July 2025, respectively. This protocol is based on V1.0, 13 July 2025 of the protocol. The results of this study will be disseminated through peer-reviewed publications and academic conferences. TRIAL REGISTRATION NUMBER: ChiCTR2500109711.
29 May 2026
Read appraisal →Journal of medical Internet research
Digital Literacy and Interpersonal Trust as Predictors of Willingness to Share Patient-Generated Health Data Among Korean Internet Users: Cross-Sectional Study Using Privacy Calculus and Communication Privacy Management Theories
BACKGROUND: The proliferation of wearable devices and advances in data analytics are accelerating the adoption of personalized digital health care, relying heavily on patient-generated health data (PGHD). However, the sensitive nature of this data creates significant privacy boundaries. While previous research has focused on rational cost-benefit trade-offs, there is a limited understanding of how social and cognitive factors-specifically interpersonal trust and digital literacy (DL)-shape the data-sharing decisions of the general public. OBJECTIVE: This study aims to identify the factors predicting individuals' willingness to share health data (WS) by integrating privacy calculus and communication privacy management theories. It specifically examines the comparative influence of DL, interpersonal trust, and moral motivation on data-sharing decisions. METHODS: We analyzed data from the 2023 Korea Panel Survey on the Digital Society (n=4518), a nationwide representative sample of internet users. Survey-weighted structural equation modeling with weighted least squares mean and variance adjusted estimation was used to examine the relationships among perceived risk (PR), perceived benefit (PB), DL, interpersonal trust, and WS. RESULTS: PR was the strongest negative predictor of WS (standardized coefficient [std β]=-0.189, P<.001), whereas PB was the strongest positive predictor (std β=0.076, P=.009), followed by moral motivation (std β=0.062, P=.03) and interpersonal trust. DL showed a significant negative direct association with willingness to share (std β = -0.060, P<.001). However, subdimension analysis revealed heterogeneous mechanisms: the "understanding" dimension was associated with lower PR and indirectly promoted sharing, whereas use and engagement were associated with higher PR. Age-stratified analyses suggested potential heterogeneity in these relationships, although the overall interaction was not statistically significant. CONCLUSIONS: PBs and risks were the strongest determinants of PGHD sharing, with benefits increasing and risks decreasing willingness to share. Beyond this risk-benefit balance, DL (particularly understanding) and interpersonal trust also played important roles, highlighting the need for trust-based and user-centric strategies to promote PGHD sharing and support the expansion of digital health care.
29 May 2026
Read appraisal →Journal of medical Internet research
Biocomputing: Beyond the Hype
Biocomputing is a nascent but rapidly developing field at the intersection of biology and computer science. In this News and Perspectives article, JMIR Correspondent Simon Spichak reports on its current and potential applications for health care research and beyond.
29 May 2026
Read appraisal →Progress in biomedical engineering (Bristol, England)
Wearable sensors for monitoring neurogenic dysphagia: a scoping review
Neurological disorders, such as Parkinson's disease and stroke, often lead to neurogenic dysphagia, a swallowing disorder that compromises nutrition and increases the risk of malnutrition, aspiration of food, and even death. Although instrumental assessments remain the gold standard for evaluating swallowing function, their invasiveness, cost, and reliance on specialized clinical facilities limit their routine use. A systematic search was conducted in PubMed, Scopus, IEEE Xplore, and Web of Science, including only peer-reviewed studies using non-invasive wearable devices for assessing and monitoring dysphagia in neurological disorders. The initial search identified 1669 records, and an updated search retrieved 128 additional records. After screening, 24 studies met the inclusion criteria. Accelerometers and surface electromyography sensors were the most frequently used, either alone or in multimodal configurations. Multimodal approaches generally provided more accurate and comprehensive assessments than single-sensor systems. However, validation with gold-standard methods was performed in only half of the studies, and considerable heterogeneity was observed in sensor placement, study protocols, and signal-processing strategies. Wearable sensing technologies represent a non-invasive and scalable solution for assessing neurogenic dysphagia. To enable clinical translation, future research should focus on standardized methodologies, robust validation, and the integration of artificial intelligence for more precise swallowing assessment in individuals with neurological disorders.
29 May 2026
Read appraisal →Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Reproducible workflow for online artificial intelligence in digital health
Online artificial intelligence (AI) algorithms are an important component of digital health interventions. These online algorithms are designed to continually learn and improve their performance as streaming data are collected on individuals. Deploying online AI presents a key challenge: balancing adaptability of online AI with reproducibility. Online AI in digital interventions is a rapidly evolving area, driven by advances in algorithms, sensors, software and devices. Digital health intervention development and deployment is a continuous process, where implementation-including the AI decision-making algorithm-is interspersed with cycles of re-development and optimization. Each deployment informs the next, making iterative deployment a defining characteristic of this field. This iterative nature underscores the importance of reproducibility: data collected across deployments must be accurately stored to have scientific utility, algorithm behaviour must be auditable and results must be comparable over time to facilitate scientific discovery and trustworthy refinement. This article proposes a reproducible scientific workflow for developing, deploying and analysing online AI decision-making algorithms in digital health interventions. Grounded in practical experience from multiple real-world deployments, this workflow addresses key challenges to reproducibility across all phases of the online AI algorithm development life cycle. This article is part of the theme issue 'Statistical workflow'.
29 May 2026
Read appraisal →JMIR research protocols
Virtual Reality for Preoperative Anxiety in Patients Undergoing Odontectomy Under General Anesthesia: Protocol for a Randomized Controlled Trial
BACKGROUND: Odontectomy is a common surgical procedure often associated with significant preoperative anxiety, particularly when performed under general anesthesia. Anxiety can lead to physiological changes, such as increased blood pressure and heart rate, and may affect anesthetic requirements and postoperative recovery. Virtual reality (VR) has emerged as a promising non-pharmacological intervention to reduce anxiety through immersive distraction. OBJECTIVE: This study aims to evaluate the effectiveness of VR in reducing preoperative anxiety in patients undergoing odontectomy under general anesthesia. The study will assess anxiety using both subjective measures (Amsterdam Preoperative Anxiety and Information Scale, APAIS) and objective biomarkers (salivary alpha-amylase, salivary cortisol, and vital signs). METHODS: This is a prospective, randomized controlled trial. Patients aged 17-40 years undergoing odontectomy under general anesthesia at the Dental and Oral Hospital, Universitas Padjadjaran, will be recruited. Participants (n=32) will be randomly assigned to either the intervention group (VR experience of the operating room) or the control group (standard verbal education). Data collection includes salivary samples (alpha-amylase and cortisol), vital signs (blood pressure, heart rate, respiratory rate), and APAIS scores measured at baseline, immediately post-intervention, and 15 minutes post-intervention. Data will be analyzed using independent and paired t tests or Mann-Whitney and Wilcoxon tests, depending on normality. RESULTS: The study is scheduled to be conducted between October 2026 and December 2026. The findings will provide evidence regarding the efficacy of VR as a standard anxiolytic tool in oral surgery. CONCLUSIONS: If effective, VR could serve as a non-invasive, safe alternative to pharmacological premedication for managing preoperative anxiety, potentially improving surgical outcomes and patient comfort.
29 May 2026
Read appraisal →JMIR research protocols
Digital Cognitive Behavioral Therapy for Insomnia Versus Sleep Hygiene Education for Prevention of Perinatal Depression: Protocol for a Randomized Clinical Trial
BACKGROUND: Insomnia during pregnancy is a modifiable risk factor for depression. Research in nonpregnant populations and preliminary findings in a pregnant population suggest that targeting insomnia with digital cognitive behavioral therapy for insomnia (CBT-I) may prevent depression. OBJECTIVE: This study aims to evaluate the efficacy of digital CBT-I for the prevention of depression through 12 months post partum compared to that of a credible, clinically relevant comparator intervention, sleep hygiene education (SHE). This project will also provide valuable information about how and for whom CBT-I prevents perinatal depression. METHODS: The Perinatal Research for Improving Sleep and Mental Health study is a fully remote, 2-arm parallel-group randomized controlled trial designed to test the superiority of digital CBT-I versus SHE for the prevention of perinatal depression. We aimed to randomize a target sample of at least 443 pregnant participants with insomnia disorder to digital CBT-I or SHE at a 1:1 ratio. Participants randomized to digital CBT-I received SleepioRx (Big Health, Inc), which uses sleep consolidation, stimulus control, cognitive therapy, relaxation techniques, and SHE in 6 interactive modules (5-15 min each). Participants randomized to SHE received information about sleep hygiene, the impact of sleep on performance, healthy sleep habits, lifestyle influences on sleep, and creating a sleep-friendly bedroom in 6 weekly digital handouts. Data are collected at baseline, 5 weeks after randomization, 10 weeks after randomization, 36 weeks' gestation, and 3, 6, 9, and 12 months post partum. The primary outcome is depression incidence, measured using the Structured Clinical Interview for DSM-5 (Diagnostic and Statistical Manual of Mental Disorders [Fifth Edition]) Disorders, Research Version. Other outcome measures include the Edinburgh Postnatal Depression Scale, Patient Health Questionnaire-9, Columbia Suicide Severity Rating Scale, Generalized Anxiety Disorder Scale, and a birth outcomes questionnaire. Mediator measures include the Insomnia Severity Index, Consensus Sleep Diary, Structured Clinical Interview for Sleep Disorders, Perseverative Thinking Questionnaire, Dysfunctional Beliefs about Sleep Scale, Pre-sleep Arousal Scale, and Difficulties in Emotion Regulation-Short Form. RESULTS: Data collection began in November 2022 and is projected to end in December 2026. CONCLUSIONS: If study hypotheses are supported, then this project will provide a new, scalable approach for preventing perinatal depression.
29 May 2026
Read appraisal →Journal of medical Internet research
Rerouting Eye Care: How AI and Telemedicine Are Reshaping Ophthalmology Patient Journeys in the United Kingdom and Germany
Artificial intelligence (AI) as a medical device is now progressively entering routine ophthalmic care, yet its impact is still mostly evaluated in terms of diagnostic performance rather than how it reshapes patient care pathways. This viewpoint argues that careful pathway design is crucial to implementing AI in ophthalmology so that it translates into real, practical benefits for patients and services. We propose a framework that classifies AI- and telemedicine-enabled eye care pathways by the initial point of contact and the role of AI: from direct human assessment to grader- or telemedicine-based use to AI-first contact and fully autonomous AI gatekeeping. We apply this framework to 2 contrasting health systems, the United Kingdom and Germany, focusing on common retinal diseases and cardiovascular risk assessment from retinal images. In the United Kingdom, AI is being introduced mainly as a modular upgrade to standardized programs layered onto a gatekeeper model centered on community optometrists and general practitioners. In Germany, direct access to office-based ophthalmologists, opportunistic screening, and commercial retail offerings are producing more fragmented clinician- and market-driven AI adoption. Our comparison shows that the same AI technologies can generate very different patient journeys across health systems. Therefore, their value depends not only on diagnostic performance but also on intentional pathway design, including clear escalation rules and structured information transfer. This includes appropriate task shifting from ophthalmologists to trained staff or new roles, such as image grading, tele-ophthalmology triage, or pathway management. Without such design, AI risks duplicate testing, incomplete or poorly coordinated referrals, and further fragmentation of care.
29 May 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
The evolution of scientific knowledge in childhood asthma over time
Paediatric asthma management has undergone a significant transformation from rudimentary assessments in the early 20th century to sophisticated diagnostic and therapeutic approaches today. Early clinical observations lacked paediatric specificity, but mid-20th-century studies introduced functional assessments, spirometry and recognition of asthma as a chronic inflammatory condition. The introduction of inhaled corticosteroids transformed long-term management, offering targeted control with reduced systemic risks. Advances in noninvasive diagnostics, such as fractional exhaled nitric oxide, induced sputum analysis, exhaled breath condensate and electronic nose technology, have improved inflammation monitoring, phenotype classification and therapeutic responsiveness. The integration of omics technologies, i.e. genomics, proteomics and metabolomics, has enabled deeper insights into disease mechanisms and facilitated early, individualised interventions. Concurrently, artificial intelligence (AI) and machine learning are emerging as tools for predicting exacerbations, identifying clinical subtypes and enhancing decision-making through large-scale data integration. Despite these advancements, challenges remain around standardisation, data quality and ensuring equitable access. This narrative review synthesises decades of progress in paediatric asthma care, emphasising the transition from empirical treatment to personalised, biomarker-driven strategies. It highlights current gaps, particularly in algorithm transparency, paediatric-specific validation and holistic care integration. As asthma management enters an era of digital health and AI-assisted precision medicine, future success will depend on interdisciplinary collaboration, real-world validation and policies that close care disparities.
29 May 2026
Read appraisal →JMIR aging
e-Counseling for Fall Prevention in Older Adults: Scoping Review
BACKGROUND: Falls are a major contributor to injury, disability, and mortality among older adults worldwide. Digital health-based e-counseling has emerged as a scalable strategy to support fall prevention by addressing physical, behavioral, and cognitive risk factors. OBJECTIVE: This scoping review aimed to map the scope, characteristics, and outcomes of e-counseling interventions for fall prevention among adults aged 60 years or older. METHODS: Guided by Arksey and O'Malley's framework and reported in accordance with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews), we searched PubMed, ScienceDirect, CINAHL, and ProQuest for studies published between January 2019 and May 2025. Eligible studies evaluated digital or telehealth-based counseling interventions for fall prevention. Data were synthesized descriptively, and methodological quality was appraised using the Mixed Methods Appraisal Tool. RESULTS: A total of 13 studies were included, comprising randomized controlled trials, feasibility studies, qualitative research, and retrospective cohorts conducted across Asia, Europe, and North America. e-Counseling modalities included virtual reality, mobile health application, teleconsultations, and wearable-triggered feedback systems. Reported outcomes included reductions in fall incidence ranging from 15% to 90.9%, improvements in balance and mobility, increased fall risk awareness, and enhanced self-efficacy. Interventions incorporating cultural tailoring and digital literacy support demonstrated higher engagement and adherence. CONCLUSIONS: e-Counseling appears to be a promising and scalable approach for fall prevention in older adults, with benefits across physical and behavioral domains. Future research should focus on long-term effectiveness, equitable access in low- and middle-income settings, and integration into routine health care systems.
29 May 2026
Read appraisal →BMJ open
Voice-assisted artificial intelligence in cardiovascular disease management: a systematic review and meta-analysis protocol
INTRODUCTION: Cardiovascular disease (CVD) remains a leading cause of global morbidity and mortality, with self-management playing a pivotal role in improving outcomes. Voice-assisted artificial intelligence (AI) technologies such as virtual assistants and voice-controlled applications have emerged as innovative tools for healthcare delivery. While the technologies show promise in areas like primary prevention and chronic disease management, their effectiveness in supporting self-management for patients with CVD remains underexplored. This study aims to evaluate the impact of voice-assisted AI technologies on CVD self-management, specifically focusing on cardiovascular-related mortality, health-related quality of life (HRQoL) and adherence to lifestyle modifications. METHODS AND ANALYSIS: A systematic review and meta-analysis will be conducted following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols. A comprehensive search will be performed across databases such as MEDLINE, Scopus, Embase and Cochrane Central Register of Controlled Trials (CENTRAL), from 2010 to 2025. The review will include randomised controlled trials (RCTs), non-RCTs and observational studies that evaluate voice-assisted AI interventions (eg, voice-controlled fitness apps, smart health assistants) aimed at CVD self-management. The primary outcome will be cardiovascular-related mortality. Secondary outcomes will include HRQoL, clinical outcomes (eg, high blood pressure), lipid profiles (eg, cholesterol and glucose levels) and lifestyle modifications (eg, dietary habits and levels of physical activity). Data management and analysis will be conducted using Comprehensive Meta-Analysis software V.2.0. The methodological quality of the included studies will be assessed using the Cochrane Risk of Bias tool for RCTs and the Newcastle-Ottawa Scale for observational studies. The meta-analysis will use random-effects models, with heterogeneity assessed using Q and I² statistics. Subgroup analyses and meta-regression will be conducted to explore potential sources of heterogeneity. ETHICS AND DISSEMINATION: No formal ethical assessment is required, as this study involves analysis of publicly available secondary data. Findings will be disseminated through publications in peer-reviewed scientific journals, conference presentations and media coverage to inform healthcare providers, policymakers and patients. PROSPERO REGISTRATION NUMBER: CRD42024568702.
29 May 2026
Read appraisal →JMIR mHealth and uHealth
Effects, Acceptability, and Use of a Dynamically Tailored Mobile What Do You Drink Intervention to Reduce Excessive Drinking Among Adolescents and Young Adults in the Netherlands: Randomized Controlled Trial
BACKGROUND: Excessive alcohol consumption among adolescents and young adults is a serious health problem. Dynamically tailored interventions could reduce their excessive drinking. We therefore developed "What Do You Drink" (WDYD), a 17-week dynamically tailored mHealth (mobile health) intervention providing personalized support on alcohol consumption. OBJECTIVE: We aim to evaluate the effectiveness, acceptability, and use of WDYD in reducing alcohol consumption of adolescents and young adults at risk. METHODS: We conducted a 2-arm, parallel-group randomized controlled trial using ecological momentary assessments. Recruitment was via an educational alcohol program, an online lifestyle monitor, social media advertisements, or news items on websites. Participants downloaded the standalone WDYD app, and when having given active informed consent, were randomized to the intervention or control group. Participants in the intervention group received dynamically tailored feedback sessions on alcohol consumption (wk 0-5, 7, 9, 13, and 17) and goal-monitoring reminders. Both groups completed an online baseline survey, 2 follow-up surveys (wk 9 and 33), and various ecological momentary assessments (7 daily assessments during wk 1, 7, 13, 19, 25, 31, and 33). Participants provided consent before randomization, in which they were informed that 2 study groups existed. After randomization, no disclosure of group assignment was provided, although participants could potentially infer it from receiving tailored sessions vs no tailored sessions. Primary outcomes were excessive drinking, binge drinking, and weekly alcohol consumption. Secondary outcomes were intrinsic motivation, self-confidence, and mood. Acceptability of WDYD was measured by survey questions; use was tracked via app data logs. RESULTS: Analyses were based on data from 1767 participants; 720 in the intervention group and 1047 participants in the control group. Almost half of them were female (2276/4795, 47.5%), and most (3471/4595, 72.4%) participants were aged 18-24 (median 19.40, IQR 2.92) years. The dropout rate was high, up to 96% (4603/4795) in the final 33rd week. No significant effect of WDYD was found on primary outcomes and mood, except for week 1 (excessive drinking: standardized β=-0.35, SE 0.15; 95% CI -0.64 to -0.05; binge drinking: standardized β=-0.36, SE 0.16; 95% CI -0.68 to -0.04; mood: standardized β=0.20, SE 0.06, 95% CI 0.08 to 0.32). Both groups reduced their alcohol consumption. Significant positive effects were found for intrinsic motivation and self-confidence up to 25 weeks (wk 25: standardized β=0.54, SE 0.24; 95% CI 0.06 to 1.02 for motivation; standardized β=0.72, SE 0.26; 95% CI 0.22 to 1.23 for self-confidence). Participants evaluated WDYD as acceptable and usable. CONCLUSIONS: WDYD did not significantly reduce excessive drinking compared to control, but improved motivation and self-confidence. High dropout rates highlight challenges in sustaining engagement in long-term mHealth interventions. Future research should explore strategies to enhance retention and optimize dynamic tailoring.
28 May 2026
Read appraisal →Journal of medical Internet research
Virtual Exposure With Response Prevention for Obsessive-Compulsive Disorder: Randomized Controlled Trial
BACKGROUND: Despite being highly effective and guideline-recommended for obsessive-compulsive disorder, exposure with response prevention is often underused due to barriers from both therapists and patients. Virtual reality may help address these barriers. OBJECTIVE: This study aimed to assess the efficacy and acceptance of virtual exposure with response prevention (VERP) in patients with contamination obsessive-compulsive disorder (conOCD) and checking obsessive-compulsive disorder (checkOCD) and to identify factors influencing treatment success. METHODS: Within a randomized controlled trial, 80 participants (40 with conOCD and 40 with checkOCD) were allocated to an intervention group, which received outpatient care as usual (CAU) supplemented by 6 weekly VERP sessions (VERP group), or to a control group receiving CAU alone (CAU group). Assessments occurred at baseline (t0), postintervention (t1), and at 3-month follow-up (t2). The reduction in obsessive-compulsive symptoms, as measured with the Yale-Brown Obsessive-Compulsive Scale, served as the primary outcome. RESULTS: Overall, obsessive-compulsive symptoms showed no greater improvement in the VERP group compared to the CAU group from t0 to t1 or t2 (ηp2=0.003-0.014). In exploratory subsample analyses, we observed a trend toward an interaction effect, showing greater reduction in obsessions among participants with checkOCD from t0 to t1 (F1,52=3.91; P=.05; ηp2=0.070) and greater anxiety reduction among participants with conOCD from t0 to t2 (F1,45=4.11; P=.05; ηp2=0.084). Exploratory moderation analysis suggested that adding VERP to CAU was more effective for those in the VERP group who had not prematurely discontinued prior psychological treatment and those with checking behavior. Sense of presence was only moderate, unrelated to outcome, and patient satisfaction with VERP was positive overall. CONCLUSIONS: While overall symptom reduction did not significantly differ between groups, exploratory analyses suggested trend-level differences in treatment response across obsessive-compulsive disorder subtypes and patient characteristics. These findings should be interpreted cautiously and are best viewed as hypothesis-generating.
28 May 2026
Read appraisal →JMIR medical education
Generative AI's Impact on the Mental Health of Medical Students: Scenario Analysis
BACKGROUND: Generative artificial intelligence (AI) is quickly changing medical education, even as medical students still face high levels of stress, anxiety, and burnout. These simultaneous trends-technological upheaval and ongoing mental health issues-bring up important questions about how future doctors will be trained and supported. Understanding how these factors might influence each other is crucial for developing resilient, future-ready medical education systems. OBJECTIVE: We carried out a foresight study using scenario analysis to examine potential futures at the crossroads of generative AI adoption and medical students' mental health. An initial environmental scan of the literature was conducted to pinpoint emerging trends and weak signals related to AI in medical education and well-being. These phenomena were categorized within a macro-meso-micro framework and analyzed through a multilevel sociotechnical change perspective. The study focused on 2 principal factors: the extent of generative AI integration into medical curricula and the availability of mental health support, as key drivers and critical uncertainties influencing future trajectories. METHODS: These dimensions resulted in 4 distinct scenarios: Analog Happiness (high support and low AI integration), Gen AI Paradise (high support and high integration), Disconnected Struggles (low support and low integration), and Gen AI Takeover (low support and high integration). Each scenario demonstrates how various institutional responses can impact students' digital readiness, psychological well-being, and professional growth. For each one, we identified the main systemic risks and suggested immediate institutional measures to address them. RESULTS: The findings suggest that technological innovation and mental health support must coevolve in medical education. Prioritizing one without the other risks producing either digitally unprepared or emotionally fragile physicians. Faculty readiness, ethical frameworks, and participatory curriculum design are critical to ensuring balanced integration. We formulated practical recommendations tailored to students, educators, and other stakeholders to guide balanced adaptation. CONCLUSIONS: Generative AI is more than just an additional tool in medical education; it is a systemic force that redefines how future physicians learn and operate. If technological change and student mental health are tackled separately, medical education risks creating graduates who are either unprepared for digital demands or mentally overwhelmed. This study highlights key systemic risks and suggests initial institutional steps to address them, providing a foresight-driven framework to assist educators and policymakers in responsible AI integration while safeguarding the well-being of future doctors.
28 May 2026
Read appraisal →Journal of medical Internet research
Artificial Intelligence in Patient-Centered Care and Macro-, Meso-, and Micro-Level Determinants of Rehumanization and Dehumanization: Qualitative Interview Study
BACKGROUND: Patient-centered care remains a foundational principle of modern health care. The digital transformation of health systems has accelerated the adoption of artificial intelligence (AI) across diagnostic, predictive, and communicative functions, with implications for efficiency and clinical workflows. At the same time, AI integration raises concerns regarding transparency, equity, accountability, and trust, positioning it as a potential driver of both rehumanizing and dehumanizing dynamics in health care practice. OBJECTIVE: This study examines how the adoption of AI in health care may influence patient-centered care, exploring its potential to promote rehumanization or contribute to dehumanization. The objective is to identify the factors that shape these outcomes at the macrolevel (policy and infrastructure), mesolevel (institutional practices), and microlevel (individual behaviors and interactions). METHODS: This study adopts an exploratory qualitative design informed by grounded theory principles, drawing on 20 semistructured interviews with health care leaders, clinicians, researchers, legal experts, and industry consultants who have substantial professional experience across European health care systems, with some participants also contributing experience from the US health care context. To enhance analytical rigor and transparency, the study applied the Gioia methodology, enabling inductive coding from first-order concepts to second-order themes and aggregate dimensions. This multistakeholder approach facilitated a nuanced examination of how AI integration is perceived and experienced across macro-, meso-, and microlevels of health care. RESULTS: The analysis identified key system-level factors shaping rehumanizing or dehumanizing outcomes of AI integration. At the macrolevel, 8 factors-including regulatory frameworks, policy priorities, and infrastructure-were identified as influencing whether efficiency pressures outweigh patient-centered values. At the mesolevel, 5 factors related to institutional strategies, workflows, and leadership shape how AI tools are embedded into care delivery. At the microlevel, 7 factors related to individual behaviors, trust, and doctor-patient interaction dynamics influence whether AI supports empathy and engagement or diminishes them. Rehumanizing potentials include reduced administrative burden, improved care pathways, clearer health communication, and enhanced decision-making, while risks include shorter consultations, reduced empathy, overreliance on automation, and erosion of professional identity. Without deliberate alignment with patient-centered principles, efficiency gains risk undermining the human dimensions of care. CONCLUSIONS: This study represents one of the first empirical examinations of how AI shapes health care practices through rehumanizing and dehumanizing dynamics. The findings demonstrate that outcomes depend not only on technical capabilities but also on regulatory frameworks, institutional strategies, and cultural adaptation. By systematically mapping influencing factors across macro-, meso-, and microlevels, the research provides actionable insights for decision-makers to ensure that efficiency gains remain aligned with patient-centered principles. Realizing AI's promise requires coordinated action to preserve empathy, trust, and interpersonal connection, ensuring that innovation strengthens rather than weakens the human dimensions of care.
28 May 2026
Read appraisal →JMIR perioperative medicine
AI-Generated Avatar Videos for Postoperative Patient Education Among Health Care Workers: Pilot Randomized Controlled Trial
BACKGROUND: Effective postoperative communication is vital for patient recovery, yet traditional text-based discharge instructions often lead to poor comprehension and adherence, particularly among patients with limited health literacy. Although educational videos improve understanding and retention, their widespread use has been hampered by high production costs. Generative artificial intelligence (AI) offers a scalable solution for creating engaging video content. OBJECTIVE: The primary objective of this pilot study was to assess the feasibility of creating and deploying AI-generated, avatar-led videos for postoperative instruction delivery. Secondary objectives included comparing knowledge retention, engagement, perceived clarity, and user experience between AI-generated video and traditional text-based handout formats among health care workers. METHODS: In this randomized pilot study, 38 health care worker volunteers were recruited as a convenience sample to pilot-test the intervention before patient implementation. Participants were assigned to either a text handout group (n=19, 50%) or an AI-generated video group (n=19, 50%). Both groups received information on 10 common postoperative topics. The primary outcome was objective knowledge, assessed via a 10-item quiz. Secondary outcomes, measured through surveys with 5-point Likert scales, included engagement time, subjective engagement, perceived clarity, usefulness, confidence in understanding, and information retention. Qualitative feedback was also collected. RESULTS: Objective knowledge quiz scores did not differ significantly between groups (mean 8.89, SD 1.20 for the AI-generated video group vs mean 8.21, SD 1.78 for the text handout group; P=.17; Cohen d=0.45). Participants in the AI-generated video group demonstrated significantly higher engagement time (mean 15.11, SD 7.78 minutes vs mean 8.84, SD 4.03 minutes; P=.004; Cohen d=1.04). They also rated instructions as significantly clearer (mean 4.63, SD 0.50 vs mean 4.00, SD 0.82; P=.007; Cohen d=0.93), more engaging (mean 4.05, SD 0.78 vs mean 3.32, SD 1.00; P=.02; Cohen d=0.81), and more effective for retention (mean 4.42, SD 0.84 vs mean 3.37, SD 0.68; P<.001; Cohen d=1.38). Qualitative feedback highlighted the engaging nature of AI-generated videos but noted areas for avatar refinement. CONCLUSIONS: In this pilot study with health care workers, AI-generated avatar videos did not improve objective knowledge scores but significantly enhanced engagement, perceived retention and perceived clarity (Cohen d=0.81-1.38). Future studies in actual patient populations with diverse health literacy levels are needed to determine whether these engagement advantages translate into improved knowledge outcomes.
28 May 2026
Read appraisal →JMIR formative research
Differences in Safety Risks Across Languages in Health-Relevant Queries: Vulnerability Analysis of Large Language Model Responses
BACKGROUND: Large language models (LLMs) such as ChatGPT are increasingly used to support health-related queries and decision-making. However, these models can be "jailbroken" through adversarial prompts that bypass safety filters and elicit harmful or medically inappropriate responses. In health care contexts, such vulnerabilities pose serious risks. Understanding how jailbreak susceptibility varies across languages is essential for developing robust safeguards and promoting equitable access to safe health information. This paper may contain examples that may be deemed harmful in terms of violence, self-harm, and drug abuse. OBJECTIVE: This study aims to systematically compare and contrast the vulnerability of a health LLM for jailbreaking across 3 languages: English, Spanish, and Hindi (transliterated using the Latin alphabet), based on emoji and permutation cipher attacks. METHODS: We analyzed 1000 input prompts per language, drawn from the BeaverTails dataset, across 3 harm categories: self-harm, violence, and drug abuse. Each prompt was modified using emoji and permutation cipher techniques, resulting in 6000 input-output pairs. Model responses were evaluated by human coders to determine the success rate of jailbreak attempts across languages and cipher types. RESULTS: Hindi prompts showed the highest vulnerability, with 787 successful jailbreaks using emoji ciphers and 873 using permutation ciphers. Spanish and English followed, with lower success rates across both cipher types. Differences in jailbreak success across languages and cipher strategies were statistically significant. Additionally, attacks targeting violence-related prompts were more successful overall than those targeting drug-related or self-harm content, indicating variation in vulnerability by harm type. CONCLUSIONS: The findings of this formative study reveal that LLM safety performance varies substantially across languages and harm categories, raising concerns about equitable protection in multilingual health communication. Disparities in access to harmful content may contribute to downstream health risks. Strengthening multilingual content moderation and developing language-aware safety mechanisms are critical steps toward creating safer and more inclusive health AI systems.
28 May 2026
Read appraisal →BMJ open
Assessment of patient preferences in digital health trials: procedure development and implementation in a preference-based trial
OBJECTIVES: To develop and evaluate a standardised, fully automated assessment procedure for patient treatment preferences suitable for use in randomised controlled trials (RCTs) with remote or digital health interventions. DESIGN: Multiphase developmental study followed by implementation in a fully randomised preference trial (FRPT). SETTING: University and outpatient cancer care settings in Switzerland; implementation in RCTs with a remote digital health intervention. PARTICIPANTS: 50 medical students for pilot testing; 11 patients with cancer for qualitative testing; 124 adult patients with cancer experiencing fatigue or sleep problems in an FRPT with a remote digital health intervention. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcome: feasibility and applicability of a standardised digital preference assessment procedure. SECONDARY OUTCOMES: distribution and strength of treatment preferences, reasons for preference. RESULTS: A procedure with three items for assessing treatment preferences was developed and refined based on pilot testing, stakeholder meetings and patient interviews. The stepwise procedure ensured that study participants with an initially unclear preference were finally able to report their preference. The majority of students (91.7%) indicated their preference already in the first assessment step. Similar in the RCT phase, 110 of 124 participants (88.7%) indicated their preference in the first step. Preference drivers included interest, perceived ease and conceptual appeal in addition to expected treatment benefits. CONCLUSIONS: Our fully automated standardised preference assessment with three items appears feasible in trials with digital health interventions. Future trials with two active treatment conditions should consider implementation of such a preference assessment to evaluate non-specific treatment effects driven by the match of patients' preferences with the respective treatment. TRIAL REGISTRATION NUMBERS: DRKS00035596; DRKS00035541; SNCTP000006209.
28 May 2026
Read appraisal →JMIR mHealth and uHealth
Reducing Sedentary Time After Knee Replacement Using a Multicomponent mHealth Intervention: Randomized Controlled Trial
BACKGROUND: Total knee replacement (TKR) is a common surgery for end-stage knee osteoarthritis. Although reductions in pain and improvements in mobility occur after surgery, physical activity levels often do not change. Given the challenges of increasing physical activity in this population, targeting reductions in sedentary behavior may be a first step; however, no prior studies have examined the feasibility and effects of a sedentary reduction intervention after TKR. OBJECTIVE: This study examined the effects of a 2-month multicomponent mobile health sedentary reduction intervention (NEAT!2) on sedentary time in adults with TKR. METHODS: Adults (N=83; mean age 65.3, SD 9.4 years; mean BMI 32.7, SD 6.9 kg/m2; 62/83, 74.7% female; 64/83, 77.1% White) with a TKR ≤1 year ago were randomized to the NEAT!2 group (n=42, 50.6%) or the attention-matched control group (n=41, 49.4%). The NEAT!2 intervention focused on reducing sedentary time via a smartphone app designed to interrupt prolonged bouts (≥30 minutes) of sedentary behavior and through 5 coaching calls emphasizing goal setting and problem solving. The control group focused on surgery recovery via an app or website and 5 educational calls. Sedentary time, total physical activity, physical function, and pain were measured at 2 and 5 months. Linear mixed-effects models examined intervention effects and differences between groups at each time point. RESULTS: Retention was 96% and 95% at 2 and 5 months, respectively, with no differences between groups. Participants in the NEAT!2 group completed an average of 4.95 (SD 0.2) calls, used the app on an average of 40.3 (SD 13.8) days (out of 56 days), and received an average of 9.6 (SD 6.0) notifications per day. At 5 months, there was a significant increase in sit-to-stand transitions in the NEAT!2 group and a substantial decrease in the control group, resulting in a significant difference between groups (mean difference 4.06, 95% CI 0.13-7.99; P=.04); however, the NEAT!2 intervention did not result in significant effects on any of the other study outcomes at 2 or 5 months. Additionally, more days of app use were associated with greater increases in moderate-to-vigorous intensity physical activity (r=0.335; 95% CI 0.017-0.585; P=.04). CONCLUSIONS: This study highlights the challenges of reducing sitting time in adults with TKR. Future studies should explore alternative behavior change techniques across different levels of influence (eg, environmental and social) to support interventions implemented within the first year after TKR. TRIAL REGISTRATION: ClinicalTrials.gov NCT04482400; https://clinicaltrials.gov/ct2/show/NCT04482400.
28 May 2026
Read appraisal →Scientific reports
Uncertainty estimation and probabilistic skull shape reconstruction using bayesian neural networks
3D shape reconstruction is an active area of research and a fundamental problem in computer vision, with growing applications in the medical domain, where it enables the recovery of missing or fine anatomical structures. Numerous approaches have been proposed for medical shape reconstruction, emphasizing accurate and anatomically plausible reconstructions. However, 3D shape reconstruction remains an inherently ill-posed problem, meaning (1) both neural network-based methods and conventional shape modeling approaches naturally introduce uncertainty in their predictions, and (2) multiple anatomically plausible reconstructions exist for a given partial or low-resolution input. While the uncertainty aspects have been widely explored in general computer vision, they remain relatively under-explored in the context of medical shape reconstruction. In this paper, we developed a 3D Bayesian U-Net and investigated its use for uncertainty estimation and probabilistic reconstructions across three key tasks: cranial reconstruction, facial reconstruction, and skull shape super-resolution. Our findings show that the Bayesian model is able to produce a range of anatomically plausible skull reconstructions while capturing natural skull variations arising from the learned weight uncertainty. Notably, these variations are primarily expressed through differences in bone thickness, which aligns with anatomical expectations, particularly relevant in real-world applications like cranial implant design. Additionally, we propose a principled framework to study the relationship between weight uncertainty and reconstruction uncertainty by analyzing the learned posterior distribution of the weights, demonstrating that our Bayesian U-Net achieves comparable reconstruction performance to a deterministic U-Net baseline while providing reliable uncertainty estimates. Our study also reveals a clear cross-task uncertainty pattern, where tasks with stronger structural constraints, like super-resolution, yield lower predictive uncertainty, while less constrained tasks, like facial reconstruction, result in higher uncertainty. Refer to the project page for more visual results https://git.zib.de/jli/uncertainty-aware-skull-reconstruction/ .
28 May 2026
Read appraisal →JMIR mental health
When AI Colludes: Clinical Reliability of Training and Preference Data as a Trustworthy-AI Criterion
Research on artificial intelligence (AI) and mental health has focused largely on harms at deployment, including chatbot safety, sycophancy, and AI-associated delusions. Less attention has been paid to a prior question: whether the human-generated text and preference judgments that shape large language models are themselves clinically reliable, particularly when self-report may be distorted. This Viewpoint aims to develop the clinical psychiatric construct of collusion-the uncritical acceptance of an unreliable account-as an analytic lens for AI training and deployment, and to argue that the clinical reliability of training and preference data should be treated as an explicit trustworthy-AI criterion in mental-health-relevant systems. A conceptual synthesis of psychiatry, clinical psychology, and AI safety literature was undertaken. The analysis distinguishes three pipeline layers: pretraining corpora, preference data and posttraining methods, and deployment-time interaction. It maps the clinical construct of collusion against adjacent technical concepts, including sycophancy, reward overoptimization, grounding, refusal training, red-teaming, and live monitoring. The synthesis suggests that collusion-like dynamics are least applicable at the pretraining layer and most applicable at the preference-data and deployment layers, where unassessed user or labeler input can be reinforced without corroboration. Existing mitigations, including data curation, Constitutional AI, reward-model evaluation, grounded generation, refusal training, red-teaming, and postdeployment monitoring, address parts of this problem. However, these approaches are not yet organized around a clinically informed account of when self-report is unreliable. The central novelty is therefore not a generic claim about bias, but the proposal that clinical self-report reliability should be assessed as a distinct data-quality and governance dimension. Trustworthy-AI frameworks for mental-health-relevant applications should incorporate clinical expertise in self-report reliability into preference-data design, red-teaming, and postmarket surveillance. Adding the clinical reliability of training and preference data as an explicit criterion could complement existing technical safeguards while leaving empirical evaluation of clinician involvement as an open research agenda.
28 May 2026
Read appraisal →JMIR research protocols
Coconstructing CHAMP, an Artificial Intelligence Chatbot for Pediatric Infectious Symptoms Management: Protocol for a Multiphase Participatory Study
BACKGROUND: Acute infectious symptoms are a leading cause of pediatric emergency department visits in Canada, many of which are low acuity and could be safely managed at home. Artificial intelligence (AI) chatbots offer a promising avenue for delivering accessible, evidence-based guidance to support families in managing these symptoms. OBJECTIVE: This study aims to adapt and coconstruct CHAMP (CHatbot to Assist the Management of Pediatric patients), an AI chatbot to support patients and families with acute pediatric infectious symptoms. CHAMP aims to deliver timely, tailored, and validated health information to support safe at-home self-management and informed care-seeking. METHODS: This multiphase, mixed methods participatory study will be conducted at the Montreal Children's Hospital in Montreal, Quebec, Canada. A coconstruction committee comprised of youth, parents, caregivers, and partners will be engaged as coresearchers. Eligible participants will include (1) youth aged 14-17 years and (2) parents and caregivers of children aged 0-17 years. The study comprises 5 phases. Phase 1 involves a qualitative needs assessment using focus groups with 20 participants to explore their informational needs, preferences, and concerns regarding pediatric infections and the use of AI chatbots. Phase 2 focuses on coconstructing and validating CHAMP's knowledge database through 3-5 workshops. Coresearchers will review pediatric clinical guidelines, map care questions and decision-making processes, and shape CHAMP's conversational framework. Phase 3 consists of iterative prototyping and testing through 3-5 workshops. Coresearchers will engage in prototyping and scenario testing, alongside preliminary usability and acceptability assessments. Phase 4 examines equity and accessibility through focus groups with 20 participants at risk of digital exclusion, as well as multilingual evaluation of an automated large language model-based translation layer. Phase 5 uses collaborative ethnography to explore the process of participatory coconstruction and its impact on CHAMP's design. RESULTS: Funding was secured in 2024, and Research Ethics Board approval was obtained in December 2024. As of December 2025, the coconstruction committee is being assembled, and Phase 1 recruitment is underway. CONCLUSIONS: This study will produce a functioning CHAMP prototype grounded in participatory, equitable, and responsible pediatric AI development. Findings will inform usability testing and an implementation-effectiveness evaluation, contributing to best practices for pediatric-centered AI health tools. By providing timely, tailored, and validated health information on acute infections, CHAMP may support safe at-home self-management, reduce preventable emergency department visits, ensure at-risk children are directed to appropriate care, and improve patient and family health care experiences.
28 May 2026
Read appraisal →Journal of medical Internet research
Blockchain-Enabled Self-Sovereign Identity Applications in Health Care: Scoping Review
BACKGROUND: Self-sovereign identity (SSI) provides a decentralized approach to digital identity management, enabling individuals to control their personal data without reliance on centralized authorities. Blockchain technology offers a tamper-resistant and distributed infrastructure that can support secure and verifiable identity systems. In health care, where identity fragmentation, privacy risks, and interoperability challenges persist, blockchain-enabled SSI (BC-SSI) has been proposed as a potential solution. However, existing research remains heterogeneous, with varying levels of technical maturity and limited evidence of real-world deployment. OBJECTIVE: This study conducts a scoping review to systematically map BC-SSI applications in health care and to analyze their application domains, development stages, study aims, targeted challenges, and technological infrastructures. In addition, this study aims to identify structural gaps in current research and assess the readiness of BC-SSI systems for clinical deployment. METHODS: This review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) methodology. A comprehensive literature search conducted between September 2024 and August 2025 identified 37 peer-reviewed studies that met predefined inclusion criteria. Data were extracted and synthesized using descriptive and thematic analyses across application areas, system maturity, technological components, and reported challenges. RESULTS: The findings indicate that BC-SSI research in health care remains at an early stage of maturity, with most studies proposing conceptual models or prototype implementations and limited real-world validation. Applications predominantly focus on identity verification, credential management, and privacy-preserving data exchange across domains such as electronic health records, mobile health, and access control systems. Commonly used technologies include decentralized identifiers, verifiable credentials, smart contracts, and privacy-enhancing mechanisms such as zero-knowledge proofs and selective disclosure. Despite rapid technical development, persistent challenges include interoperability limitations, governance gaps, usability concerns, and insufficient integration with health care infrastructures. Notably, a structural gap was identified between technological capability and system-level readiness for clinical deployment. CONCLUSIONS: BC-SSI technologies demonstrate potential for enabling secure, interoperable, and patient-centric identity management in health care. However, current research is predominantly technology-driven and lacks sufficient system-level validation. This study highlights the need for integrated architectural approaches, governance frameworks, and real-world evaluation to bridge the gap between conceptual innovation and clinical implementation. Advancing BC-SSI toward health care adoption will require coordinated progress across technical, organizational, and regulatory dimensions.
28 May 2026
Read appraisal →JMIR research protocols
Development of a Digital Multimodal Pain Intervention (CHRONIC): Protocol for a Randomized Controlled Trial Study
BACKGROUND: Chronic pain affects approximately 12 million individuals in Germany and significantly impairs quality of life. Although multimodal treatment approaches-combining physical, psychological, and behavioral strategies-are considered the gold standard, access to specialized multidisciplinary pain treatment, including physiotherapy and psychological interventions, is often hampered by long waiting times, which highlights the need for scalable, low-threshold treatment options. Digital health interventions, particularly those grounded in established therapeutic models, offer a promising solution to bridge this treatment gap. The German Digital Health Care Act has opened pathways for reimbursable digital applications, such as CHRONIC, which is grounded in acceptance and commitment therapy as a third-wave cognitive behavioral therapy framework and integrates selected cognitive behavioral techniques, mindfulness-based strategies, and physiotherapy into a structured web-based program. OBJECTIVE: This study protocol outlines a randomized controlled trial (RCT) aimed at evaluating the effectiveness of CHRONIC, a 12-week web-based, multimodal self-help program for individuals with chronic pain, with a focus on reducing pain intensity, reducing pain-related interference, and improving quality of life. METHODS: This is a monocentric, 3-arm RCT with assessments up to 6 months after baseline. A total of 200 participants with chronic nonmalignant pain will be randomized into one of three groups: (1) intervention with individualized psychological feedback, (2) intervention with standardized computer-generated feedback, and (3) treatment-as-usual waitlist control. Weekly modules in the intervention arms include psychotherapeutic video sessions, interactive acceptance and commitment therapy-based and cognitive behavioral therapy-based exercises, mindfulness practices, and physiotherapeutic training. Outcomes will be assessed via validated self-report instruments: West Haven-Yale Multidimensional Pain Inventory for pain intensity and interference, Assessment of Quality of Life-8 Dimensions for quality of life, German version of Chronic Pain Acceptance Questionnaire for pain acceptance, Patient Health Questionnaire-9, German version for depression, and Generalized Anxiety Disorder 7-item Scale for anxiety. Data will be analyzed using mixed models for repeated measures based on the matched ~6-month assessment. Missing data will be addressed using multiple imputation. RESULTS: Recruitment started in January 2026 and is projected to end in July 2026. As of January 22, 2026, 18 participants had been enrolled. Primary end point data collection is expected to be completed in January 2027, with primary analyses and dissemination planned for 2027. CONCLUSIONS: This study protocol describes an RCT designed to evaluate the clinical utility of CHRONIC as a scalable, low-threshold intervention for chronic pain. CHRONIC aims to address the multifactorial nature of chronic pain by integrating psychotherapeutic, physiotherapeutic, and mindfulness-based strategies into a single digital program. Future studies will be required to determine how this multimodal format compares to unimodal interventions. The findings aim to inform future digital health developments and contribute to the implementation of biopsychosocial care models in routine practice.
28 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Effectiveness of eHealth interventions in long-term follow-up care for breast and gynecological cancer survivors: a systematic review
PURPOSE: As survival rates for breast and gynecological cancers improve, many survivors experience persistent long-term challenges, including fatigue, psychological distress, and reduced health-related quality of life (HRQoL). eHealth interventions may offer a scalable and accessible approach to support survivorship care. This systematic review aimed to evaluate the effectiveness of eHealth interventions in long-term follow-up care for breast and gynecological cancer survivors. METHODS: A systematic literature search was conducted across six databases (PubMed, PsycINFO, Web of Science, Cochrane Library, MedRxiv, and PsyArXiv) for studies published between January 2010 and February 2026. Eligible studies included adult breast or gynecological cancer survivors and evaluated eHealth interventions in follow-up care compared with usual care. Outcomes included outcomes such as burden of treatment, HRQoL, self-management, hospital readmissions, complications, infections, fatigue, or malnutrition. Data were extracted, synthesized narratively, and methodological quality was assessed using established risk-of-bias tools. RESULTS: A total of 41 studies were included, predominantly involving breast cancer survivors, with very limited representation of gynecological cancer populations. Overall, methodological quality ranged from low to moderate, with higher risk of bias observed in nonrandomized studies. eHealth interventions were associated with small to moderate improvements in fatigue, anxiety, depression, HRQoL, and treatment adherence, particularly among breast cancer survivors. However, effect sizes were generally smaller in studies with lower risk of bias. Evidence for gynecological cancer survivors was scarce and inconclusive, with only one study exclusively focusing on this population. Long-term outcomes were rarely assessed, and no studies evaluated the defined outcomes infections, malnutrition and unplanned hospital admissions. CONCLUSIONS: eHealth interventions show potential to support long-term survivorship care, particularly in improving psychological outcomes and quality of life among breast cancer survivors. However, the overall evidence base is limited by methodological heterogeneity, short follow-up periods, and the underrepresentation of gynecological cancer populations. Further high-quality, long-term, and cancer-specific research is needed to better establish the effectiveness and generalizability of these interventions in survivorship care. IMPLICATIONS FOR CANCER SURVIVORS: As survivorship care needs continue to increase, eHealth interventions may contribute to more accessible, scalable, and patient-centered models of follow-up care. However, their long-term effectiveness and applicability across diverse cancer populations remain unclear. More rigorous, long-term, and cancer-specific research is required to better define their role in survivorship care.
27 May 2026
Read appraisal →PloS one
A hybrid machine learning and explainable AI framework for optimizing risk-based authentication
As online platforms continue to grow, the need for strong authentication mechanisms becomes increasingly important to protect sensitive information and networks. Risk-Based Authentication (RBA) is an adaptive approach that dynamically adjusts authentication decisions based on user behavior and contextual information, thereby improving both security and user experience. This study proposes a hybrid RBA framework that integrates machine learning ensemble techniques, fuzzy logic, clustering, and optimization to enhance account takeover detection and dynamic risk assessment. The ensemble classifier, combining Gradient Boosting, SVM, and XGBoost, predicts the probability of account compromise based on login behavior, device attributes, and network information. K-Means clustering is used to generate initial risk thresholds (low, medium, and high), which are further refined using a fuzzy logic system to map probabilities to risk levels. The L-BFGS-B optimization algorithm is employed to fine-tune fuzzy membership boundaries and improve threshold consistency. Experimental results demonstrate strong performance, achieving 97.77% accuracy, 99.41% precision, 98.04% recall, 98.72% F1-score, and an EER of 0.0303. On large-scale datasets ranging from 2M to 30M records, the proposed framework demonstrates consistent improvement in authentication decisions. For the 2M dataset, Allow Login actions increase from 349,432-349,923, while Deny Login actions decrease from 1,462-1,228, along with a slight reduction in additional authentication prompts. Furthermore, the use of Explainable AI techniques, particularly SHAP, enhances the transparency and interpretability of the model, supporting more informed decision-making. Overall, the proposed framework is accurate, adaptive, and suitable for real-world risk-based authentication applications.
27 May 2026
Read appraisal →Journal of medical Internet research
Large Language Model-Generated Patient Instructions for Prescriptions in Primary Health Care: Preclinical Algorithm Validation
BACKGROUND: The application of generative artificial intelligence to simplify medication use instructions has the potential to enhance people's health by improving treatment adherence. OBJECTIVE: We evaluated the performance of large language models (LLMs) in generating medication usage instructions to complement prescriptions in primary health care. METHODS: This randomized, blinded experimental preclinical study used prescription-inducing scenarios, assigned to 62 health care professionals, to validate instructions generated by LLMs during electronic prescriptions. The instructions were generated by ChatGPT-4.0 (OpenAI), Llama3.1-8B (Meta), and Llama3.1-8B-RAG (Meta) using retrieval-augmented generation based on patient information leaflets. Performance metrics assessed adequacy, completeness, clarity, language simplification, usefulness, and errors in the generated instructions, with scores to analyze overall and individual metrics. RESULTS: The 3 models yielded high overall scores for producing qualified instructions (ChatGPT-4.0: median 88.4, IQR 22.8; Llama3.1-8B: median 66.5, IQR 50.9; Llama3.1-8B-RAG: median 79.9, IQR 34.4; Kruskal-Wallis test P=.003). Llama3.1-8B-RAG received evaluations with similar overall scores to ChatGPT-4.0 (post hoc test, P=.05) and similar to Llama3.1-8B (post hoc test, P=.44). ChatGPT-4.0 outperformed Llama3.1-8B (Bonferroni test, P<.001). Regarding specific domains, Llama3.1-8B-RAG received scores equivalent to those of ChatGPT-4.0 for adequacy (mean 6.24, SD 2.3 vs mean 6.82, SD 2.1; post hoc test, P=.54); completeness (mean 5.94, SD 2.2 vs 6.55, SD 1.9; post hoc test P=.38), clarity (mean 5.77, SD 2.4 vs mean 6.68, SD 1.9; post hoc test P=.09), and usefulness (mean 5.42, SD 2.4 vs mean 5.96, SD 2.2; post hoc test P=.63). ChatGPT-4.0 received higher scores in the language simplification criterion than Llama3.1-8B-RAG (mean 7.05, SD 1.5 vs mean 5.44, SD 2.6; post hoc test P<.001). Interrater variability in assigning scores ranged from 4.2% (n=3) to 85.8% (n=6) among primary health care professionals. Instructions leading to incorrect use of the medication had similar frequency among the models(ChatGPT-4.0: n=15, 22.7%; Llama3.1-8B: n=19, 22.8%; Llama3.1-8B-RAG: n=19, 22.8%; chi-square test P=.71). The frequencies of hallucination were similar (ChatGPT-4.0: n=7, 10.6%; Llama3.1-8B: n=9, 13.6%; Llama3.1-8B-RAG: n=6, 9.1%; chi-square test P=.67). CONCLUSIONS: The open-source LLM enhanced with external information presented similar performance to the closed-source model, except for ChatGPT4.0, which was superior in language simplification of messages. LLM generation demonstrated potential for instructing patients on medication use. Nonetheless, the introduction of this innovation into the electronic prescribing workflow demands prescriber validation for human oversight of the technology and requires a strategy for LLM performance governance. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.12688/verixiv.1359.1.
27 May 2026
Read appraisal →Journal of neurology
Man vs. Machine: can ML algorithms diagnose headaches as accurately as clinicians? A systematic review.
BACKGROUND: Headache disorders are frequently misdiagnosed. We aimed to systematically evaluate the diagnostic accuracy, methodological quality, and clinical applicability of artificial intelligence (AI) and machine learning (ML) models for classifying adult headache disorders against clinician diagnoses using the International Classification of Headache Disorders (ICHD) criteria. METHODS: In this systematic review, we searched PubMed, Embase, and the Cochrane Library (January 2015-December 2025) for AI/ML diagnostic headache studies. Two reviewers extracted data and assessed risk of bias using the QUADAS-2 tool with the QUADAS-AI extension. Main outcomes were sensitivity, specificity, area under the receiver operating characteristic curve (AUC-ROC), and risk of bias. RESULTS: We included 74 studies encompassing 154,856 participants. Models utilized traditional ML (n = 47), deep learning (n = 18), and hybrid or rule-based approaches (n = 9). Data inputs included neuroimaging (n = 27), multimodal datasets (n = 20), neurophysiological signals (n = 17), and clinical questionnaires (n = 10). Only 4 studies performed independent external validation. Overall sensitivity ranged from 47.5% to 100.0%, specificity from 50.4% to 100.0%, and AUC-ROC from 0.658 to 1.000. Models using structured questionnaires reported realistic accuracies (74-86%), whereas neuroimaging models frequently produced near-perfect, likely overfit estimates. Most studies (65 of 74) exhibited a high risk of bias driven by artificial case-control designs, data leakage, and absent external validation. CONCLUSIONS: AI-based headache diagnostic systems demonstrate promising but highly variable accuracy. Pervasive methodological flaws-specifically severe spectrum bias, data leakage, and a lack of independent external validation-currently preclude clinical implementation. Future studies require prospective, real-world validation to safely integrate these tools into practice.
27 May 2026
Read appraisal →ACS nano
All-Printed MXene-Based Springs for Concurrent Bidirectional Hand Motion Capture
Accurately capturing hand movements remains a challenge because the skin undergoes stretching, compression and bending at the same time, yet most flexible sensors respond only to tension. Achieving high sensitivity and a wide range often means making the sensor thick, which causes the sensing layer to shift under complex deformations and distorts the signal. Here, we address this by placing the strain-accommodating space within a plane parallel to the strain direction. Using a MXene/polyurethane ink with glycerol as a molecular bridge, we create a dense hydrogen-bonding network that enables high-resolution printing of a planar spring architecture. The resulting all-printed sensor achieves a gauge factor of 83.7 across a linear bidirectional range of ±60% strain. With just two sensors, we track and decode complex wrist motions with ±10° accuracy. When attached to finger joints, they enable real-time, dexterous control of a virtual hand. This work provides an alternative approach for skin strain capture through synergistic innovation in materials and architecture.
27 May 2026
Read appraisal →JMIR formative research
Testing a Smartphone-Based Intervention Targeting Anxiety Sensitivity Among Women Presenting for Emergency Care After Sexual Assault: Pilot Randomized Controlled Trial
BACKGROUND: Anxiety sensitivity (AS), defined as the fear of anxious arousal, is a promising therapeutic target for reducing posttraumatic stress disorder (PTSD) symptom development after trauma exposure. Initial research suggests that smartphone-based AS interventions may be acceptable to sexual assault survivors at risk for PTSD symptoms and effective for symptom reduction, but only small one-arm proof-of-concept studies have been conducted. OBJECTIVE: The goal of this study was to extend prior proof-of-concept work by conducting a pilot randomized controlled trial. The aims were to evaluate intervention efficacy, AS and PTSD symptom change, the acceptability and credibility of a control intervention, and the feasibility of a larger randomized controlled trial. METHODS: A total of 60 women with high AS presenting for emergency care after sexual assault were recruited and randomized to either the AS intervention or a control condition, and they were followed up with for over 6 months via remote self-report questionnaires. RESULTS: The findings indicated that the study population is at risk and in need of intervention: 88.8% (40/45) and 80.6% (29/36) of women sexual assault survivors in the sample met the criteria for probable PTSD at 7 weeks and 6 months post assault, respectively. Most (16/27, 59.3%) individuals receiving the AS intervention who completed it rated it as acceptable (eg, 18/21, 85.7% reported that the treatment was helpful). Early within-group reductions were not statistically significant, but by month 6, statistically significant reductions in AS and PTSD symptoms were observed in both conditions. Recruitment and retention data supported the feasibility of the study design, although some suggestions were noted for future research (eg, improving intervention and ecological momentary assessment compliance). CONCLUSIONS: This pilot study replicated the proof of concept and acceptability of a novel smartphone-based intervention targeting AS delivered to women sexual assault survivors presenting to emergency care. Intervention completion was in line with or better than traditional therapy but remained a challenge. Our selected control condition demonstrated a larger effect than expected, and we were unable to track its initiation or completion, causing difficulty in drawing conclusions. Overall, the results highlight the need for additional research.
27 May 2026
Read appraisal →Journal of medical Internet research
Sequencing AI Automation and Data Interoperability in Oncology Using a Scenario-Planning Framework Coupled With Discrete-Event Simulation: Proof-of-Concept Study
BACKGROUND: As oncology workflows integrate increasingly autonomous artificial intelligence (AI) agents, health systems face uncertainty regarding operational impacts. Traditional linear forecasting methods fail to capture second-order effects such as governance saturation, induced demand, and bottleneck migration. To navigate this complexity, the emerging field of medical futures studies requires methodologies that bridge qualitative strategic foresight with quantitative operational modeling. These system-level dynamics directly influence timely diagnosis, treatment delays, and overall health system resilience. OBJECTIVE: This study aimed to develop a proof-of-concept framework coupling qualitative scenario planning with computational discrete-event simulation to stress-test oncology AI adoption strategies. METHODS: We defined a strategic state space using 2 orthogonal axes, AI automation intensity and data interoperability, resulting in 4 distinct futures scenarios. We translated these qualitative narratives into a quantitative discrete-event simulation model of a 3-year operational horizon. The model quantified system performance (referral-to-treatment interval [RTTI] and throughput), volatility, and resource constraints across different adoption trajectories. RESULTS: The scenario-planning phase yielded 4 operational archetypes (analog oncology, automation islands, interconnected clinicians, and AI-orchestrated care) with distinct constraints, risks, and failure modes. In the simulation, the fully integrated scenario maximized capacity (1244, SD 21.4 patients per year) and halved the mean RTTI to 14.9 (SD 0.3) days, a magnitude comparable to major pathway redesign interventions. Isolated automation without data infrastructure led to reduced system performance, increasing RTTI by 26% (37.1, SD 1.3 days) and reducing throughput to 647 (SD 10.1) patients per year due to administrative governance saturation. The model illustrated a structural bottleneck migration: successful upstream AI adoption shifted binding constraints from diagnostic scanners to downstream chemotherapy infusion units, whereas missing data interoperability resulted in governance constraints. Pathway optimization analysis indicated that a coordinated strategy prioritizing early improvements in data interoperability reduced transition volatility compared to an automation-first approach. CONCLUSIONS: Integrating qualitative scenario planning with quantitative simulations enabled a systematic evaluation of oncology AI adoption strategies. As a proof of concept, it offers a replicable framework for health leaders to model future scenarios of digital transformation in times of high uncertainty. Subsequent work should expand this methodology to incorporate financial and health equity dimensions, establishing simulation-based scenario planning as an important tool in medical futures studies.
26 May 2026
Read appraisal →JMIR nursing
Health Care Professionals' Perspectives on the Use of a Wearable Device for Early Detection and Continuous Vital Signs Monitoring of Acute Respiratory Infections in Nursing Homes: Qualitative Study
BACKGROUND: The growing aging population and staff shortages are placing pressure on Dutch nursing homes (NHs). These challenges have led to an increased interest in digital health technologies. Among these are wearable devices that allow for remote continuous monitoring of vital signs. An example is the Healthdot (smartQare), a wearable electronic device that continuously monitors heart rate, respiratory rate, and physical activity. In the context of acute respiratory infections (ARIs) in NHs, where initial symptoms can go unnoticed, continuous monitoring may aid in early recognition, timely intervention, and reduce staff workloads. However, little is known about how health care professionals perceive the use of continuous vital signs monitoring devices, such as the Healthdot, for this cause in NHs. OBJECTIVE: This study aims to explore the perspectives of healthcare professionals on the use of the Healthdot for early detection and monitoring of ARIs in NHs, to inform potential future implementation. METHODS: Semistructured interviews were conducted with 20 physicians, nurses, and certified nursing assistants from 4 NHs and 1 acute geriatric community hospital located in a NH. Interview transcripts were thematically analyzed to identify themes regarding their perspectives on the use of the Healthdot for monitoring ARIs in this setting. RESULTS: Five main themes were identified that related to the appropriate use of the Healthdot for NH clients and health care professionals: alignment of Healthdot use and NH clients' treatment policies, balancing safety and freedom, impact of the Healthdot on work processes, supporting rather than replacing care, and possible use during pandemics and in the future. Additionally, several preconditions for the use of the Healthdot were identified, including its usability, a support base among care staff, adequate training and guidance, communication with NH clients and their relatives, and a clear policy regarding its use. CONCLUSIONS: Given the complexity of care in NHs, where clinical care is typically balanced against quality of life and a homelike environment, physicians generally expressed reserved attitudes toward the Healthdot, highlighting the need to consider multiple factors in its implementation. Care staff were generally positive about the device. Nevertheless, tailored assessment for each individual NH client remains essential, balancing treatment goals, safety, autonomy, and person-centered care. Additionally, clear communication and alignment between health care professionals in this setting are crucial, specifically regarding their expectations of the Healthdot's role in care processes. This study offers practical guidance that may inform future implementation efforts of continuous vital sign monitoring devices in NHs.
26 May 2026
Read appraisal →Journal of medical Internet research
Virtual Reality Interventions for Stress Reduction in the General Population: Systematic Review and Meta-Analysis of Randomized Controlled Trials
BACKGROUND: Increasing mental demands across multiple life domains underscore the importance of effective individual stress management to mitigate the adverse health consequences of chronic stress. Growing evidence suggests that virtual reality (VR) interventions constitute an effective approach to stress reduction. OBJECTIVE: This systematic review and meta-analysis aimed to examine and compare application areas of VR interventions for stress reduction in the general population and to identify potential predictors of effectiveness based on sample characteristics and intervention design. METHODS: Five databases (MEDLINE, CINAHL, CENTRAL, PsycInfo, and Web of Science) were systematically searched for randomized controlled trials investigating the effectiveness of VR interventions for stress reduction in the general population. Studies were included if they primarily focused on stress reduction, included a neutral control condition, and reported a validated measurement of perceived stress. Trials targeting mental disorders or those conducted in the context of medical procedures were excluded. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias using the Cochrane Collaboration's tool. Effects were synthesized using pooled standardized mean differences, and relevant predictors were evaluated through subgroup analyses and meta-regressions. RESULTS: A total of 55 relevant studies met the inclusion criteria, with 37 investigating single-session and 18 multisession interventions (ranging from 1 to 42 sessions over 2 days to 6 months). The meta-analysis included 39 studies with 4024 participants (sample sizes 24-409; mean ages 19.2-70.6 years). Intervention types included VR-based nature exposure (21), biophilic architectural elements (6), guided meditation (9), interactive tasks (4), and other approaches (1). On average, VR interventions significantly reduced perceived stress level (-0.55, 95% CI -0.70 to -0.40; P<.001; I2=76%; 95% prediction interval [PI] -1.32 to 0.23), anxiety (-0.88, 95% CI -1.23 to -0.54; P<.001; I2=87%; 95% PI -2.07 to 0.31), and depression (-0.34, 95% CI -0.47 to -0.21; P<.001; I2=0%; 95% PI -0.47 to -0.21), and enhanced positive emotion (-0.64, 95% CI -0.84 to -0.46; P<.001; I2=62%; 95% PI -1.28 to -0.02). In addition, the decrease in systolic blood pressure was significant (-0.20, 95% CI -0.37 to -0.04; P=.02; 95% PI -0.37 to -0.04). Univariate linear regression analyses identified multiple sessions (R²=15%, P=.02) as a significant predictor of VR-based stress reduction. Further analyses of technical and content-related characteristics revealed that a higher image refresh rate (t15=-2.36, 2-tailed; P=.04) was associated with stronger intervention effects. CONCLUSIONS: This review highlights the considerable potential of VR interventions for reducing psychological stress in the general population and extends recent evidence by quantifying the effects of a broad range of VR-based stress reduction approaches and, for the first time, comparing diverse intervention characteristics. VR represents a promising alternative, particularly in contexts where access to real-life nature or conventional relaxation methods is limited or resource-intensive.
26 May 2026
Read appraisal →Journal of medical Internet research
Association Between Wearable Device Adoption and Health-Related Lifestyle Behaviors: Retrospective Cohort Study
BACKGROUND: Wearable devices are increasingly adopted for personal health monitoring, but evidence on their long-term associations with health-related lifestyle behaviors in real-world population settings remains limited. OBJECTIVE: This study examined the longitudinal association between wearable device adoption and engagement in health-related lifestyle behaviors using a nationally representative panel dataset from South Korea. METHODS: We analyzed data from the 2016 and 2022 waves of the Korea Media Panel survey. Health-related lifestyle behaviors in the physical, social, and cultural domains were operationalized as estimated annual activity counts based on self-reported frequency measures. We used a difference-in-differences framework with generalized estimating equations to compare changes in these behaviors between new wearable adopters and nonadopters adjusting for demographic and socioeconomic characteristics. Relative changes were estimated using Poisson models with a log link, and subgroup analyses were conducted to explore variation across sociodemographic groups. As a sensitivity analysis, inverse probability of treatment weighting was additionally applied to assess the robustness of the findings to observed baseline imbalance. RESULTS: Wearable device adoption was associated with greater increases in total, physical, and cultural health-related lifestyle activities over time. In the difference-in-differences model, adopters showed greater relative increases in total activity (rate ratio [RR] 1.24, 95% CI 1.08-1.35), physical activity (RR 1.36, 95% CI 1.12-1.64), and cultural activity (RR 1.78, 95% CI 1.31-2.42) than nonadopters. Subgroup analyses showed limited evidence of consistent heterogeneity and should be interpreted cautiously. Sensitivity analyses using inverse probability of treatment weighting showed overall patterns broadly similar to those of the primary analyses. CONCLUSIONS: In this nationally representative panel study, wearable device adoption was associated with greater increases in total, physical, and cultural health-related lifestyle activities over time, whereas no clear association was observed for social activity. These findings should be interpreted as associative rather than causal given the observational design and the inability to directly assess parallel trends.
26 May 2026
Read appraisal →Journal of medical Internet research
Exploring Technological Solutions for Interoperability Between Patient Electronic Medical Records and Clinical Registries: Scoping Review
BACKGROUND: The use of electronic medical records (EMRs) and clinical registries has transformed health care delivery by improving data management, care coordination, and research capacity. However, the full potential of these technologies can only be realized through effective interoperability, thereby reducing the burden of manual data entry and enhancing the use of real-world clinical data. OBJECTIVE: This review examines technologies that enable automated data extraction and transfer, which promote interoperability between EMRs and clinical registries. METHODS: A search of PubMed, CINAHL, Embase, and Web of Science, including studies published between January 2013 and April 2025, was registered with Open Science Framework a priori and involved three key concepts: (1) "registry," (2) "electronic medical records," and (3) "interoperability." A 2-phase screen identified studies evaluating technologies that facilitate automated data extraction or interoperability. Automation was defined as fully automated, where data are extracted and transferred without human intervention, or semiautomated, where extraction or transfer is predominantly automated but may include manual validation. Only technologies supporting ongoing database integration were eligible for inclusion. Screening, data extraction, and synthesis were conducted by multiple independent reviewers. Technology experts provided extensive input and guidance throughout to ensure the accuracy and relevance of the extracted information. RESULTS: Overall, 36 studies met the inclusion criteria, representing 12 countries across 5 continents and addressing a wide range of acute and chronic health conditions. Epic was the most frequently reported EMR system, while the most common registry platforms were REDCap (Research Electronic Data Capture; Vanderbilt University), structured query language (SQL) server database, and EMR-embedded solutions. Most approaches centered around extracting data from structured formats (n=18), or a combination of both structured and unstructured formats (n=10), emphasizing the central role of structured EMR data in current automated extraction approaches. CONCLUSIONS: This review advances understanding of interoperability between EMRs and clinical registries by uniquely examining automated and sustainable solutions for data exchange, extending beyond prior work that has largely focused on technologies designed for isolated systems or study-specific data extraction. A novel contribution of this review is the synthesis of context-specific considerations derived from reported implementations, providing a comprehensive overview of how technology selection and implementation are shaped by the context in which they are deployed. While these advancements have reduced reliance on inefficient, error-prone, and resource-intensive manual processes, ongoing challenges in data standardization, seamless integration, and long-term sustainability are compounded by poor and inconsistent reporting across studies. Future efforts should follow comprehensive reporting guidelines, adhere to robust governance principles, and incorporate implementation science frameworks, to not only enable meaningful comparison and synthesis in future research, but also to ensure that technologies can be effectively, feasibly, and sustainably integrated within health care contexts, while upholding the ethical and equitable use of health care data.
26 May 2026
Read appraisal →Head and neck pathology
Digital Pathology in Head and Neck Squamous Cell Carcinoma: Translational Advances and Clinical Integration for Pathologists, Oncologists, and Surgeons
PURPOSE: Artificial intelligence (AI)-enabled digital pathology has advanced rapidly in head and neck squamous cell carcinoma (HNSCC), but its readiness for clinical implementation remains uncertain. This review evaluates the translational maturity of AI-based digital pathology applications in HNSCC and their proximity to routine clinical use. METHODS: A structured literature search of PubMed and Scopus identified studies published between January 2020 and March 2026. Studies were included if they applied AI or machine learning to histopathologic or whole-slide images in HNSCC or related oral malignancies and reported diagnostic, prognostic, biomarker, or treatment-response outcomes. Given the narrative scope of this review, representative studies aligned with key clinical domains were selected for qualitative synthesis. A six-stage translational maturity framework was used to categorize applications based on development stage, validation, and clinical integration. RESULTS: Of 444 identified records, 358 unique studies were screened, from which representative HNSCC-specific AI digital pathology studies were selected for detailed analysis. Most applications-including diagnostic classification, dysplasia grading, prognostic modeling, and therapy-response prediction-remain at early translational stages (Stage 0-1), typically limited to retrospective, single-center cohorts with minimal external validation. Biomarker quantification and HPV prediction show relatively greater maturity, with some studies approaching Stage 2, reflecting external validation and early prospective evaluation. However, calibration, decision-curve analysis, and outcome-linked endpoints are rarely reported. No HNSCC-specific AI pathology tools have demonstrated outcome-linked deployment or achieved regulatory-grade implementation, and evidence for workflow integration, clinical decision impact, and patient-centered outcomes remains limited. CONCLUSION: AI-enabled digital pathology in HNSCC remains an early-stage field. Despite promising technical performance, most applications lack the prospective validation, workflow integration, and outcome-based evidence required for clinical adoption. Progress will depend on multi-institutional prospective studies, standardized reporting of clinical utility, and demonstration that AI-assisted decisions improve patient management and outcomes.
26 May 2026
Read appraisal →Journal of robotic surgery
Extended reality and emerging artificial intelligence in orthopedic surgical training: a scoping review of educational outcomes
Immersive technologies have gained increasing relevance in orthopedic surgical education; however, the scope, outcomes, and maturity of extended reality (XR) and artificial intelligence (AI) applications remain heterogeneous. To map the educational applications, learner populations, outcome measures, and research gaps related to the use of XR and emerging AI tools in orthopedic surgical training. A scoping review was conducted following Joanna Briggs Institute methodology and reported according to PRISMA-ScR guidelines. PubMed, Scopus, and ScienceDirect were searched for English and Spanish studies published between 2015 and 2025. Fifty-four studies involving 3,066 participants were included. Virtual reality (VR) was the predominant modality (83.3%), followed by augmented reality (31.4%), while AI-based applications were infrequently reported. XR-based training was most commonly evaluated in trauma surgery, arthroscopy, and arthroplasty, primarily among medical students and residents. Most studies reported improvements in simulation-based technical performance metrics, whereas evidence on clinical outcomes, long-term skill retention, and cost-effectiveness was limited. XR-particularly VR-represents the most mature immersive technology in orthopedic surgical education. AI applications remain emergent and primarily supportive. Future research should prioritize standardized outcome measures, multicenter designs, and evaluation of long-term educational and clinical impact.
25 May 2026
Read appraisal →Scientific reports
From sleep staging to spindle detection: a case study on end-to-end automated sleep analysis
Automation of sleep analysis, including both macrostructural (sleep stages) and microstructural (e.g., sleep spindles) elements, promises to enable large-scale sleep studies and to reduce variance due to inter-rater incongruencies. While individual steps, such as sleep staging and spindle detection, have been studied separately, the feasibility of automating multi-step sleep analysis remains unclear. In this case study, we evaluate whether a fully automated analysis using validated machine learning models for sleep staging (RobustSleepNet) and subsequent spindle detection (SUMOv2) can replicate findings from an expert-based study of bipolar disorder. The automated analysis qualitatively reproduced key findings from the expert-based study, including significant differences in fast spindle densities between bipolar patients and healthy controls, accomplishing in minutes what previously took months to complete manually. While the results of the automated analysis differed quantitatively from the expert-based study, possibly due to biases between expert raters or between raters and the models, the models individually performed at or above inter-rater agreement for both sleep staging and spindle detection. Our results demonstrate that fully automated approaches have the potential to facilitate large-scale sleep research. We are providing public access to the tools used in our automated analysis by sharing our code and introducing SomnoBot, a privacy-preserving sleep analysis platform.
25 May 2026
Read appraisal →Scandinavian journal of primary health care
Responsible digital transformation in rural primary healthcare services: critical factors from a Norwegian case study
OBJECTIVE: To explore the critical factors that shape the potential for co-creating and implementing a virtual department for home-based healthcare in a rural municipality. METHODS: A qualitative case study was conducted in a rural municipality in Northern Norway. Data were collected through semi-structured interviews, focus groups, and co-creation workshops involving healthcare leaders, frontline staff, service users and relatives, and representative from the pensioners' federation. Thematic analysis was applied to identify organisational, contextual, and cultural factors influencing digital transformation. RESULTS: While the co-creation of virtual department for home-based healthcare showed potential for piloting, readiness for full implementation was limited. Six critical factors were identified: structural readiness, digital competence, technology in practice, cultural change, autonomy and care, and motivation and dedication. These factors highlight the importance of organisational culture, workforce capacity, and stakeholder engagement in shaping readiness for responsible digital transformation. CONCLUSION: Digital transformation holds potential for addressing healthcare challenges in rural municipalities, but success depends on more than technological efficacy. Effective coordination between bottom-up and top-down approaches is required to ensure responsible and sustainable transformation of primary healthcare services.
25 May 2026
Read appraisal →World journal of urology
From conventional imaging software to artificial intelligence: tools for stone volume assessment in urolithiasis. Α review by the EAU and YAU sections of endourology.
INTRODUCTION: Accurate assessment of stone burden is fundamental in urolithiasis, as it directly influences treatment selection and prognostic evaluation. Although maximum stone diameter on non-contrast computed tomography remains the most widely used parameter, it does not adequately reflect the three-dimensional complexity of urinary calculi. This review aimed to summarize the evolution of stone burden assessment from conventional imaging-based methods to software-assisted volumetry and artificial intelligence (AI)-driven tools, with emphasis on their accuracy, reproducibility, and clinical utility. METHODS: A narrative review of the literature was performed using PubMed/MEDLINE, Scopus, and Google Scholar for English-language studies published up to March 2026. Original studies, validation studies, technical reports, reviews, and guideline-related papers addressing conventional CT-based measurement, software-assisted volumetry, AI-based stone segmentation, and the clinical significance of stone volume were included. Due to heterogeneity in study design and reported outcomes, the evidence was synthesized narratively. RESULTS: Maximum stone diameter remains simple and widely available, but it incompletely represents true stone burden, particularly in larger or irregular stones. Formula-based ellipsoid calculations are practical yet show limited accuracy in complex geometries. Semi-automated CT-based segmentation software provides more reliable volumetric assessment, with excellent agreement with reference standards and reduced interobserver variability. AI-based approaches have further improved efficiency by enabling rapid and highly accurate automated stone detection and volume calculation. Across the reviewed literature, stone volume was consistently shown to be more clinically informative than linear dimensions for predicting spontaneous passage, stone-free rates after shockwave lithotripsy and ureteroscopy, and future symptomatic events during surveillance. CONCLUSIONS: Stone volume offers a more accurate and clinically meaningful estimate of stone burden than maximum stone diameter alone. The transition from formula-based methods to software-assisted and AI-driven volumetry represents an important advance in urolithiasis imaging. Wider adoption will depend on standardized imaging protocols, improved software accessibility, and validation of volume-based thresholds for routine clinical practice.
25 May 2026
Read appraisal →International journal of circumpolar health
Artificial intelligence/machine learning in Alaska Native healthcare systems: symposium perspectives.
Artificial intelligence and machine learning (AI/ML) utilisation in Alaska Native and American Indian (AN/AI) healthcare presents an opportunity to address persistent health disparities and improve the well-being of Alaska Native communities. The 2025 Artificial Intelligence in Alaska Native Health Care Systems symposium occurred in Anchorage, Alaska, with 31 individuals participating in facilitated conversations between various stakeholders and specialists at the Alaska Native Tribal Health Consortium, Southcentral Foundation, Maniilaq Association and Stanford University. The overarching goal of the symposium was to bring together partners within the Alaska Tribal Health System (ATHS) to promote: (1) AN/AI peoples' guidance in support of Tribal sovereignty for AI/ML implementation within the ATHS, (2) expanded technical capacity and (3) centralised guidance and expertise to lead future AI/ML research and implementation with community-defined benefits. During the event, small group discussions focused on the following topics: (1) common AI/ML applications, (2) concerns surrounding AI/ML, (3) current or potential policies guiding AI/ML use and (4) how to build trust in AI/ML models. Themes discussed were data democratisation, AI/ML development and use needs to occur under the principles of data sovereignty, and AI/ML quality improvement and research are needed and distinct. The symposium was an important step in considering how to use AI/ML to optimise AN/AI health and well-being through collaborative partnerships and services.
25 May 2026
Read appraisal →Rheumatology international
Cost-effectiveness of artificial intelligence interventions for musculoskeletal disorders of the spine: a systematic review
Musculoskeletal disorders (MSDs) represent a substantial global health burden, and artificial intelligence (AI)-based technologies are emerging as potential approaches for their management. However, evidence regarding the cost-effectiveness of these interventions remains limited. This systematic review synthesised the available evidence on the cost-effectiveness of AI-based technologies for managing MSDs of the spine. This systematic review was conducted and reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. PubMed, Medline, CINAHL, Cochrane Central Register of Controlled Trials, Scopus, Web of Science, and Directory of Open Access Journals (DOAJ) databases were searched. After title and abstract screening, the remaining articles underwent full-text review by two independent reviewers, who extracted economic outcome data comparing AI-based interventions versus control. A total of 347 studies were identified, with 59 duplicates removed and 251 excluded based on title and abstract. After full-text review, seven eligible studies were included in the review. These studies represented five countries (United States (n = 2), Germany (n = 2), Denmark (n = 1), Australia (n = 1) and South Korea (n = 1)). The studies adopted a healthcare perspective (n = 4), a societal perspective (n = 2) and a healthcare and societal perspective (n = 1). Time durations varied widely, from four weeks to twelve months in trials to three years or lifetime in models, with one study using a 20-month post-implementation period. The AI components of the interventions encompassed a range of technologies, including a deep learning-based diagnostic and exercise prescription platform, AI-driven chest radiograph screening, the self-BACK application, standardised Magnetic Resonance Imaging (MRI) protocols, the Back Pain Choices online decision support tool, and the Kaia back pain application. In three studies, AI-based interventions for MSDs of the spine showed consistent positive QALY gains. Across the seven studies, five interventions (71%) were found to be cost-effective or cost-saving, four (57%) were dominant, while two (29%) were not cost-effective, indicating that most AI-based and digital interventions for MSDs of the spine provide favourable economic value compared with standard care. This is the first systematic review to synthesise evidence on the cost-effectiveness of AI-based technologies for the management of MSDs of the spine. The results suggest that AI-based interventions have the potential to contribute to more efficient healthcare resource use and may support policy and decision-making. However, their impact on reducing the economic burden of MSDs of the spine and improving health outcomes is not yet conclusive, and further standardised economic evaluations are needed to strengthen the evidence base. PROSPERO registration number: https://www.crd.york.ac.uk/PROSPERO/view/CRD42025642775 .
25 May 2026
Read appraisal →Computer assisted surgery (Abingdon, England)
An automatic detection model for spread through air spaces in postoperative pathological sections based on deep learning in NSCLC
Spread through air spaces (STAS) is recognized as an aggressive pattern of invasion in lung cancer and has been associated with poorer survival outcomes. However, STAS is frequently overlooked or misdiagnosed during routine pathological diagnosis. We analyzed 129 pathological slides from 91 STAS-positive patients with non-small cell lung cancer (NSCLC). An artificial intelligence framework consisting of a tumor region segmentation algorithm and an object detection algorithm was developed. The segmentation algorithm was first used to isolate the main tumor region, followed by STAS detection using an improved object detection model. The segmentation module achieved a Jaccard similarity of 0.846. The proposed object detection algorithm demonstrated superior performance with a precision of 0.738, recall of 0.747 and average precision (AP) of 0.784 and F1 score of 0.742, outperforming other object detection methods. Both segmentation and detection results met diagnostic requirements, with higher accuracy observed in lung adenocarcinoma (LUAD) than in lung squamous cell carcinoma (LUSC). In external validation, the model achieved a precision of 0.670, recall of 0.705 and AP of 0.732 and F1 score of 0.687. Additionally, the AI-derived STAS counts showed substantial agreement with pathologists (interclass correlation coefficient = 0.703). Kaplan-Meier survival analysis revealed that the number of STAS events was significantly associated with disease-free survival (DFS) in stage I LUAD (p < .05). We propose a deep learning-based artificial intelligence framework for automated STAS detection and quantification in digital whole-slide images of NSCLC. This model holds promising potential to assist pathologists in achieving more comprehensive and accurate diagnosis of STAS.
25 May 2026
Read appraisal →PloS one
A hybrid BiLSTM and rule-based system for integrated diabetes prediction and personalized guidance
Diabetes is a common chronic disease that needs early diagnosis and proper management to avoid severe complications. While current Artificial Intelligence (AI) tools generate predictive information, they often lack an integrated element for post-diagnosis support in order to fill in this critical gap in patient self-management. This research proposes and validates a hybrid System which aims to bridge this gap. The methodology is based on a novel, fused dataset (PIMA and Type 2 Diabetes) that was carefully preprocessed following a leakage-safe protocol in order to increase generalizability. The system architecture is a combination of two different critical components: Strong Bidirectional Long Short term Memory (BiLSTM) model for prediction and rule based engine for creating personalized lifestyle recommendations. In order to validate the efficacy of the BiLSTM model, seven traditional machine learning (ML) models and standard deep learning (DL) models have been comparatively tested, in which BiLSTM model has demonstrated a better generalization and prediction performance. Rigorous 10-fold cross validation was used to validate the system, which came up with an accuracy of 84.02%, precision of 87.89%, and recall of 80.50%. This research concludes that by successfully combining the high-performance predictive engine and real-time guidance module, it is possible to develop a holistic clinically relevant tool to close the loop between diagnosis and proactive self-management.
25 May 2026
Read appraisal →Archives of women's mental health
Moderators, mediators, and components of a standalone smartphone application for postpartum depression: secondary analysis of a randomized controlled trial
PURPOSE: The purpose of this study was to identify potential moderators, mediators, and efficacious components of a standalone app intervention on postpartum depression symptoms and functional impairment. METHODS: A total of 264 women were randomized to intervention (Motherly app) or to an active control group. We analyzed depressive symptoms assessed by the Edinburgh Postnatal Depression Scale and functional impairment by the Clinical Global Impression at post-treatment as outcomes. Various potential moderators were assessed at baseline. Potential mediation variables were the level of behavioral activation (BA) measured by the Behavioral Activation for Depression Scale - Short Form and the Response-contingent positive reinforcement (RCPR) by the Reward Probability Index, at post-treatment. Intervention component analysis was evaluated using app engagement data. RESULTS: The results indicate that intervention effects on depressive symptoms were moderated by the number of children and pediatric consultations, with stronger effects observed among mothers with fewer children and higher consultation frequency. Functional impairment was moderated by time spent with the child, with greater improvements among mothers with lower caregiving demands. Mediation analyses showed BA and RCPR mediated the effect of our intervention on depressive symptoms and functional impairment. Total and BA achievements were significantly associated with reduction in depressive symptoms. CONCLUSION: Our findings suggest that our app intervention efficacy is influenced by maternal characteristics and childcare responsibilities. BA and RCPR are key mechanisms, and higher engagement with app components produce greater symptom reduction.
24 May 2026
Read appraisal →Medicine
Applying digital technologies for remote care in the real life context: A 3-year experimentation with postoperative lung cancer patients
Surgery remains the main curative treatment for lung cancer patients. However, operation has resulted in some-degree reduction in postoperative pulmonary function. The decreased pulmonary function could be rehabilitated to the preoperative level through restoring exercise, which is related to the ways and exercise time of rehabilitating. The Dignio platform is 1 digital telemedicine system and has been used maturely in Norway, however, it has not yet been widely promoted in China. In the present study, we adopted the telemedicine system, according to the formulated rehabilitation exercise program, to design a good way to recover pulmonary function for postoperative lung cancer patients, promoting early and quick recovery, and reducing the long-term postoperative complications and outpatient visits. This retrospective controlled study enrolled 250 postoperative lung cancer patients using a remote digital care platform and 250 matched controls receiving standard instructions. In the postoperative 1-week follow-up, we found that the forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), forced expiratory volume in 6 seconds, FVC25-75, peak expiratory flow had an obvious upward trend, although there were no significant differences in the measurements on postoperative day 7 compared with day 1. From the long-term data at 1 month/3 months/6 months post-operation, we found that the recovery was better to varying degrees in the experiment group using the remote care digital platform, compared with that of the control, especially the FEV1 at 3 (P = .065) and 6 months post-operation (P = .037), and forced Expiratory Flow between 25% and 75% of forced vital capacity at 3 months post-operation (P = .084). However, most pulmonary values, such as the FEV1/FVC, FVC and peak expiratory flow have no significant difference (all P > .05) in the recovery at 1 month post-operation between the experimental group and control, the second time at 3 months and the third time at 6 months after surgery. Especially in the experimental group, 2 patients with sudden pneumothorax were timely detected after discharge through the Digino platform, the patient returned to the hospital in time, and both patients were out of danger ultimately. The Digino remote care indeed has some benefit in the pulmonary function recovery in the long-term follow up, and reduces postoperative complications obviously.
24 May 2026
Read appraisal →PloS one
Views, barriers, and facilitators of people living with human immunodeficiency virus and healthcare professionals regarding the use of a mobile health application to improve HIV self-care in Malaysia
AIM: The views, barriers, and facilitators of people living with HIV (PLWH) and healthcare professionals (HCPs), regarding the use of a mobile health application (m-HA) to improve HIV self-care were explored. SUBJECT AND METHODS: Seventeen PLWH, nine doctors and two nurses who owned a smartphone and understood English or Malay were recruited from the infectious disease clinic of a tertiary hospital in Malaysia. Semi-structured in-depth interviews were conducted face-to-face using a topic guide, audio-recorded and transcribed verbatim. Data was analysed using reflexive thematic analysis. Two researchers independently coded transcripts and met to resolve discrepancies and develop themes. Data collection continued until thematic saturation was achieved. RESULTS: Three themes emerged: (1) Perceived benefits and concerns regarding m-HA usage: participants perceived m-HA could improve knowledge, but expressed concerns regarding information overload. Participants viewed m-HA as a means to enhance access to hospital services like clinic visits, medication refills, communication with HCPs, and medical records documentation. However, concerns regarding misuse, data confidentiality and security remained. (2) Perceived motivators of m-HA: participants perceived that high utilitarian and hedonic motivators increase m-HA usage. (3) Perceived influences to use m-HA: participants believed PLWH would use m-HA if introduced by their peers or HCPs. CONCLUSION: These findings provided information for developing a patient-centred m-HA to improve HIV self-care in Malaysia.
24 May 2026
Read appraisal →PloS one
Effectiveness of tele-rehabilitation using AI-guided exercise and pain neuroscience education for fibromyalgia (FIBROIA): Protocol for a randomized controlled trial
INTRODUCTION: Fibromyalgia (FM) is a chronic condition characterized by widespread pain and cognitive dysfunction, with pharmacological treatments offering limited efficacy. Although Pain Neuroscience Education (PNE) and therapeutic exercise are evidence-based interventions, accessibility and adherence remain major challenges particularly in underserved regions such as Latin America. This trial investigates the effectiveness of a 12-week tele-rehabilitation program (FIBROIA) that integrates Artificial Intelligence (AI)-guided exercise with PNE to enhance access to comprehensive multimodal care. METHODS AND ANALYSIS: This multicentre, randomized, assessor-blinded, parallel-group controlled trial will enroll fifty adults meeting the 2016 ACR criteria for FM. Participants will be randomly assigned (1:1) to either the intervention group or enhanced usual care. The intervention consists of three personalized exercise sessions per week delivered through the Rehbody AI platform, which provides real-time biomechanical feedback, along with a weekly PNE module designed to reconceptualize pain. The primary outcome is the change in pain intensity, measured using the Visual Analogue Scale (VAS), at week 13 (±7 days), immediately following completion of the 12-week program. Secondary outcomes include the Fibromyalgia Impact Questionnaire-Revised (FIQ-R), lower-limb strength assessed by the 30-Second Sit-to-Stand test, and health-related quality of life measured with the EQ-5D-3L. Statistical analyses will follow an intention-to-treat (ITT) framework. DISCUSSION: The FIBROIA protocol addresses the urgent need for scalable, evidence-based interventions in resource-limited settings. By combining AI-driven biomechanical feedback with cognitive reappraisal through PNE, this study seeks to reduce fear-avoidance behaviors and improve exercise adherence. This integrative approach aims to overcome the limitations of passive tele-rehabilitation by simulating asynchronous professional supervision, thereby ensuring both safety and technical precision in movement execution. CONCLUSIONS: If effective, this protocol will offer a robust, technology-enabled framework for remote FM management. The results could help establish a new clinical standard for accessible, patient-centered rehabilitation, bridging the gap between high-level evidence and real-world practice across diverse socioeconomic contexts. TRIAL REGISTRATION: ClinicalTrials.gov NCT06672419.
24 May 2026
Read appraisal →BMJ paediatrics open
Enhancing parental health knowledge through digital knowledge mobilisation tools: a randomised controlled trial and qualitative study
OBJECTIVE: To assess the effectiveness of blogshots (ie, image-based digital knowledge mobilisation tools) to increase knowledge and manage expectations with respect to six Choosing Wisely Canada (CWC) recommendations about common childhood conditions. DESIGN: Multimethod study including a non-pharmacological randomised controlled trial and qualitative interviews. SETTING: Online questionnaires and semistructured interviews across Canada. PARTICIPANTS: Parents or legal guardians of children under 5 years. INTERVENTION: Participants were randomly allocated to receive one of two sets of blogshots whereby a blogshot on a new CWC topic was delivered weekly over 3 weeks (Group A: pharyngitis (diagnosis), common cold, asthma; Group B: pharyngitis (treatment), bronchiolitis, otitis media). Study investigators were blinded to participant allocation. Baseline and follow-up (week 5, month 3, month 6) questionnaires were administered. Qualitative interviews contextualised intervention data. MAIN OUTCOME MEASURES: The primary outcome was change in knowledge. Secondary outcomes were change in expectations, intentions, actions over time and usability of the blogshots. RESULTS: 167 participants (n=133 (80%) mothers) were included in the final analysis (group A, n=85; group B, n=82). Baseline knowledge scores did not differ between groups. At follow-up 1, those who received the blogshot for a given topic scored significantly higher (p<0.001, d=0.30) on knowledge questionnaires, compared with those that did not. No differences were observed in knowledge scores between groups at follow-up 2 and 3. Participants expressed intentions to use the blogshots (eg, share with friends, physicians), as well as actions (eg, wait/watch approach vs seek healthcare) to manage their child's condition. Qualitative data (n=13 interviews) showed that blogshots are aesthetically pleasing and contain easy to read and understandable information. CONCLUSIONS: Blogshots can be an effective tool to disseminate child health information to parents. Due to the online dissemination, this approach could be scaled to a broader international level and applied to different health topics that are relevant across the world. TRIAL REGISTRATION NUMBER: NCT06005337.
24 May 2026
Read appraisal →Medicine
Tech-based evaluation of healthcare quality during the COVID-19 pandemic
The coronavirus disease 2019 pandemic profoundly disrupted global health systems, exacerbating preexisting inequalities in access to and quality of care. Disparities in service accessibility, continuity, coordination, and comprehensiveness highlight the need for a multidimensional, data-driven approach to healthcare evaluation. We conducted a retrospective analysis using 52,490 responses from the 2021 Global Burden of Disease COVID-19 Health Service Disruption Survey. We developed a 4-dimensional evaluation framework aligned with World Health Organization principles for people-centered care. Following variable selection, standardized preprocessing, and manual labeling, we trained and assessed 6 machine learning (ML) algorithms. We further optimized the 2 best-performing models - support vector machine (SVM) and random forest (RF) - using interpolation-based data augmentation, cross-validated hyperparameter tuning, and particle swarm optimization. SVM showed the highest baseline performance (accuracy 0.93), whereas RF achieved competitive macro-F1 scores. Interpolation-based data augmentation improved the generalizability of the SVM model, increasing its accuracy to 0.96. The RF model benefited most from particle swarm optimization-based hyperparameter optimization. Model predictions revealed persistent inequalities in healthcare service quality: respondents with lower income, lower educational attainment, and rural residence were disproportionately classified as receiving lower-quality services, whereas urban residence and higher income were associated with higher-quality care. Our scalable ML framework successfully assessed healthcare service quality during the pandemic, with optimized SVM and RF models demonstrating robust performance on high-dimensional, imbalanced survey data. Post-pandemic recovery strategies must prioritize socioeconomically disadvantaged populations. Furthermore, integrating ML tools can enhance real-time quality monitoring within health systems.
24 May 2026
Read appraisal →BMJ health & care informatics
Beyond the digital health tsunami: reaffirming biomedical and health informatics as the scientific foundation of digital healthcare transformation
Digital health (DH) has emerged as the dominant terminology for technology-enabled healthcare, reshaping institutional discourse, educational programmes and policy agendas. DH is often portrayed as a novel discipline that could supersede Biomedical and Health Informatics (BMHI), creating conceptual ambiguity and the risk of weakening scientific rigour in the design, evaluation and governance of digital interventions. This paper clarifies BMHI's disciplinary identity, characterises DH as a practice-oriented and implementation-oriented domain, and establishes the epistemic relationship between the two in order to inform academic strategy, curricula and professional identity. A comparative conceptual analysis examined scope, theoretical foundations, methodological approaches, educational frameworks, professional structures and stakeholder ecosystems in both domains, informed by recent debates on degree programmes and workforce training. We found that BMHI is a mature scientific discipline with explicit theoretical foundations encompassing data representation, knowledge modelling, clinical reasoning, decision support, interoperability, sociotechnical analysis, evaluation science, safety, ethics and governance. DH, by contrast, represents an implementation-oriented translational domain that operationalises BMHI through products, services and organisational transformation (eg, telehealth, mobile applications, artificial intelligence-enabled clinical services). While DH has amplified visibility and accelerated adoption, it lacks autonomous theoretical foundations and depends on BMHI for scientific validity, safety and sustainability. Three evolutionary scenarios are examined: DH absorbing BMHI (epistemically incoherent), convergence as parallel disciplines (conceptually flawed), and reintegration of DH within BMHI as its applied layer (scientifically coherent and strategically sustainable) but requiring disciplinary communication, curricular alignment and institutional leadership explicitly positioning DH as a translational layer of BMHI.
24 May 2026
Read appraisal →Journal of robotic surgery
AI-based automated bleeding monitoring in conventional and robot-assisted laparoscopic surgery: a systematic review
Artificial intelligence has emerged as a promising approach for improving the detection and management of intraoperative bleeding during conventional and robotic-assisted laparoscopic surgery, where delayed recognition of hemorrhage can lead to increased morbidity and procedural complexity. This review synthesizes current evidence on the use of artificial intelligence for intraoperative bleeding monitoring, with a particular focus on performance, feasibility, and clinical integration. A systematic review was conducted in accordance with PRISMA 2020 guidelines. Comprehensive searches of PubMed, Scopus, Web of Science, IEEE Xplore, Embase, and grey literature identified studies published between 2016 and 2025 that applied artificial intelligence to bleeding prediction, detection, localization, tracking, and quantitative blood-loss estimation during conventional and robotic-assisted laparoscopic surgery. Data relating to study design, model architectures, evaluation metrics, latency, and integration feasibility were extracted and summarized narratively. Across the included studies, artificial intelligence models demonstrated high detection accuracy in predominantly single-centre, retrospective, or simulation-based settings, with several approaches reporting promising real-time feasibility under controlled experimental conditions. Emerging work also explored bleeding source tracking, blood-loss estimation, and early integration into surgical workflows. However, most studies relied on small, single-center datasets and retrospective validation, limiting generalizability and clinical translation. Overall, artificial intelligence-based bleeding monitoring in conventional and robotic-assisted laparoscopic surgery shows increasing technical maturity and potential, though largely unvalidated in prospective clinical settings. Future research should prioritize large, multi-institutional datasets, prospective clinical evaluation, and optimized low-latency deployment within real surgical workflows to support safe and effective intraoperative use.
24 May 2026
Read appraisal →Studies in health technology and informatics
Interoperability Skills for Digital Health: A Reference Model from the XiA Project
As healthcare systems advance through digital transformation, achieving interoperability across Health Information Systems (HIS) relies on skilled professionals who enable effective integration and data exchange. Under the scope of the Xpanding Innovative Alliance (XiA) project, this article introduces a Reference Model for Interoperability Skills that defines the core skills needed by professionals to support interoperable digital health ecosystems. Through a systematic literature review, the model provides a structured view of competencies across professional profiles, grounded in international best practices. The proposed model serves as a strategic tool for workforce development, curriculum design, and organizational capability assessment. By promoting a shared understanding of interoperability, it supports the creation of a skilled, adaptable, and collaborative workforce capable of advancing integrated, data-driven healthcare across Europe.
24 May 2026
Read appraisal →Studies in health technology and informatics
Leveraging Large Language Models with Retrieval-Augmented Generation for Semantic Mapping of Clinical Data Lakes to SNOMED CT
Mapping local clinical concepts to standardized terminologies such as SNOMED CT is essential for semantic interoperability and large-scale research, but manual mapping is labor-intensive and difficult to scale. We report a preliminary evaluation of a hybrid approach combining retrieval-augmented generation (RAG) and a large language model (LLM) to support the mapping of hospital datalake concepts to SNOMED CT. Using a dataset of 2,768 concepts annotated by the SIMED across five semantic categories (organisms, healthcare locations, laboratory samples, allergies, and clinical assessments), the pipeline consistently outperformed retrieval-only baselines. For organism concepts, strict accuracy reached 92.8% (weighted 98.0%, top-3 96.7%), while allergy and assessment concepts achieved 84.7% and 72.1% strict accuracy, respectively. Healthcare location and laboratory sample concepts remained more challenging, with strict accuracies of 57.5% and 57.1%, and weighted accuracy varied strongly depending on frequency distributions. Overall, strict accuracy was 72.8%, weighted accuracy 76.1%, and top-3 accuracy 83.1%. These findings suggest that combining RAG with LLM-based reasoning can reduce expert workload while maintaining accuracy, representing a step toward scalable, AI-assisted semantic interoperability in healthcare. The approach relies on a retrieval-augmented generation pipeline using embedding-based candidate retrieval followed by LLM-based disambiguation (GPT-4.1-mini), and is evaluated against a retrieval-only baseline. By improving semantic consistency in clinical data lakes, it indirectly supports interoperable access to patient-level health data.
24 May 2026
Read appraisal →Studies in health technology and informatics
A Federated Benchmark for Clinical Natural Language Processing (FedDRAGON)
We introduce the FedDRAGON challenge, a federated learning benchmark for clinical natural language processing. The challenge includes 12 information extraction tasks where data is extracted from clinical reports from 4 Dutch care centers. Baseline results show that the performance of the federated models surpass single-center performance and approaches that of centralized models. Benchmark, code, and pre-trained LLMs are publicly available.
24 May 2026
Read appraisal →Studies in health technology and informatics
Large Language Models for Automating Conformance to Health-Data Standards: The Interoperability Case of HL7 FHIR and OMOP
Interoperability standards enable different healthcare systems to exchange electronic health information seamlessly, and support large-scale analytics needed for current Secondary uses of healthcare data. Transforming diverse clinical inputs into standards-compliant, analyzable data remains labor-intensive and error-prone. Moreover, the learning curve for their proper exploitation can be steep due to extensive documentation, specific technical requirements, and the need for both semantic and technical integration, making them not fully accessible to practitioners in the healthcare domain unless they undergo extensive training. Our study investigated whether large language models can automate conformance while preserving syntactic validity, semantic correctness, and reproducibility, thereby simplifying the adoption of these standards. Using diabetes measurements as a probe domain, we validated extraction fidelity, coding accuracy against controlled vocabularies, and conformance to the domain's main standards. Our prototype emits FHIR Observations and OMOP MEASUREMENT rows.
24 May 2026
Read appraisal →Studies in health technology and informatics
De-Identification of Free-Text Medical Records Using Large Language Models
This study evaluates large language models (LLMs) for automated de-identification of free-text medical records. Using 150 synthetically generated, personal health information (PHI)-enriched doctor's letters (23.2 PHIs per letter), we compared the full LLaMA-3.1-8B model (BF16, GPU) with a quantized variant (Q8, CPU) under zero-shot and few-shot prompting. Few-shot prompting improved performance from a macro F1 of 0.989 to 0.996 (recall = 0.994); the quantized Q8 model achieved a macro F1 of 0.992 (recall = 0.987) while enabling fully local, GPU-free inference. Although LLMs perform at a level comparable to human annotation, false negatives remain a concern for highly sensitive PHI, such as names. While human annotators typically require 13.4 seconds per PHI, the LLMs de-identified complete letters within seconds, demonstrating an efficiency and scalability advantage alongside GDPR-compliant, high-precision de-identification.
24 May 2026
Read appraisal →Studies in health technology and informatics
Anonymization of Electronic Health Records by the Use of Dimensionality Reduction Techniques
Electronic health records (EHR) mostly consist of highly sensitive data, whose sharing is highly regulated due to privacy and security concerns. To address privacy protections, de-identification and anonymization techniques offer a more secure way of data transmission to ensure patient's privacy. Principal components analysis (PCA) has emerged as an anonymization technique. The applicability of more diverse and complex dimensionality reduction (DR) techniques such as Factor Analysis of Mixed Data (FAMD), or Autoencoder (AE), remains largely unexplored. The objective of this study is an investigation into the potential of FAMD and AE as anonymizers for EHR. The goal of this study is to apply advanced DR methods on three EHR datasets of varying sizes. Subsequently, a supervised prediction task is performed on the anonymized EHR using an artificial neural network (ANN), and the performance of the prediction task is compared to the performance of the original, non-anonymized EHR. The findings indicate that the use of FAMD and AE in the anonymization of EHR results in a comparable performance to that of the original, non-anonymized EHR in a downstream prediction task. This study thus indicates the potential of advanced DR as an anonymization technique for EHR, thereby underscoring the necessity for further investigation.
24 May 2026
Read appraisal →Studies in health technology and informatics
Accessibility of Digital Health and Welfare Services Among Immigrants in Finland
Digital health and welfare services support the delivery of the services. However, the health benefits are mixed at the population level, and further research is needed to better understand the driving factors of healthcare inequalities, particularly among vulnerable subgroups, e.g., immigrants. This qualitative study aimed to understand equality in service accessibility among immigrants, especially from the informational, experiential, and institutional perspectives. Five focus group interviews were conducted for immigrants. Data was analysed with deductive content analysis. Barriers for informational accessibility included linguistic challenges and inadequate digital literacy (DL); for experiential accessibility, usability challenges and disrespectful phone services; for institutional accessibility, challenges with electronic authentication identification for personalized digital services. Facilitators for the use of digital health and welfare services included linguistic improvements of services and support for DL. Further research, including interventions with participatory design and linguistic and digital support, is needed to improve accessibility of digital services for immigrants.
24 May 2026
Read appraisal →Studies in health technology and informatics
Mapping Existing Evidence on Physicians' and Patients' Experiences with GenAI in Clinical Communication and Documentation: A Rapid Review
GenAI tools like chatbots and ambient scribes are increasingly used in clinical documentation, raising ethical and safety concerns. This rapid review maps existing evidence on experiences and attitudes of physicians and patients toward using GenAI in clinical communication and documentation. Structured searches were conducted in PubMed and Web of Science. Studies were screened using PICO criteria. Data were extracted and summarized descriptively, with high-order thematic analysis of research aims and emerging recommendations. 41 studies were included: 75.6% focused on physicians, 9.8% examined patient perspectives exclusively, and 14.6% included both. Some of the themes identified were usability, exploration of perceptions and expectations, acceptability, and ethical implications. Recommendations emphasized the need for diverse cohorts, real-world validation, and ethical considerations. Current literature was skewed toward physician perspectives. Broader, meaningful inclusion of patients is needed to support equitable and trustworthy GenAI integration in healthcare.
24 May 2026
Read appraisal →Studies in health technology and informatics
Towards Clinical NLP in a Low-Resource Language: Structuring Estonian Clinical Text
Healthcare sectors generate large volumes of unstructured text, yet mature clinical NLP is lacking for low-resource languages. This project targets Estonian, a morphologically rich, low-resource language, and outlines an end-to-end pipeline that converts free text into interoperable, structured data. A design-science approach guides development of multilingual speech-to-text for clinical dictation, clinical NLP for entity extraction and context handling, terminology binding to the Estonian SNOMED CT extension, and export as HL7 FHIR resources with mappings to EHR fields. Expected benefits include reduced documentation time through dictation with automatic structuring, improved data quality and consistency via controlled terminology, and faster retrieval of clinically relevant information to support decision-making. Pilot evaluations with healthcare partners will assess accuracy, usability, and workflow impact.
24 May 2026
Read appraisal →Studies in health technology and informatics
Build and Query Indexes of Clinical Documents with Easy-to-Reuse Pipelines
Electronic Health Records are a central source of healthcare data, containing structured data alongside unstructured clinical texts. The latter capture detailed reasoning, observations, treatment plans and clinical evolutions, which are crucial for phenotyping, and real-world evidence generation. Natural language processing enables the extraction, thus the subsequent use, of these crucial elements; however, these extractions remain one-off, study-specific efforts. This is detrimental as the extracted elements could be valuable for future research. We present medkit Seshat, an open-source Python pipeline that: (1) ingests free text, (2) recognizes relevant entities, (3) normalizes them with OMOP vocabularies, (4) builds an index that can either be searched by concept or by document. In addition, we share a flexible web UI to illustrate the interest of built indexes in terms of search, text analysis and export. Seshat aims at facilitating the reuse and adaptation of this prototypical pipeline to various purposes, with the main objective of enabling the secondary use of results of phenotyping campaigns.
24 May 2026
Read appraisal →Studies in health technology and informatics
Electronic Health Record System Support for Physician-Patient Communication: National Cross-Sectional Survey on Outpatient Physicians' Experiences
The impact of digital patient engagement platforms (DPEPs) on interactions between physicians and patients has been widely studied from patients' perspectives. However, research into physicians' viewpoints remains scarce. This study aimed to identify electronic health record (EHR) characteristics that support physician-patient collaboration from the perspective of physicians working in outpatient care. A national cross-sectional survey of physicians' experiences with EHRs was conducted in Finland in 2021 where EHR coverage had reached 100% already in 2010. We selected the responses of 1983 physicians working in public sector outpatient clinics (primary and specialized care). Linear regression analysis was used to examine the relationship between eight EHR characteristics and perceived support for physician-patient collaboration and information exchange. Of these, rapid system responsiveness, ease of utilizing patient-provided data, support for adherence to treatment recommendations, assistance in preventing medication errors, and comprehensive and reliable patient data were found to be associated with support. The model accounted for 27% of the variance in the dependent variable. In addition to enhancing care outcomes, clinical decision support and easy access to patient data can aid physicians in interactions with patients. Further investigations should continue to analyze how these identified features could better support collaboration with patients - beyond DPEPs and visible interactions during appointments. Considering the rapid evolution of DPEPs, future studies should also take into account the depth of the DPEP-EHR integration.
24 May 2026
Read appraisal →Studies in health technology and informatics
Digital GO: A Clinical Decision-Support Dashboard for Gynecologic Oncology Tumor Board
Multidisciplinary Tumor Board (MTB) meetings are essential for coordinated oncologic decision-making but are often hindered by the complexity and fragmentation of patient data. This study presents the development of an interactive digital dashboard designed to streamline data access and visualization during ovarian cancer MTB sessions. The system leverages structured knowledge extracted from unstructured clinical reports using in-house large language model (LLM) pipelines. These outputs are harmonized within a cloud environment through dedicated processing workflows and mapped into a domain ontology, to ensure interoperability. The resulting dashboard, implemented using a dedicated functional module, enables both cohort-level exploration and patient-specific visualization, including longitudinal event timelines. Preliminary results demonstrate the feasibility of integrating LLM-based data extraction with a scalable visualization framework, offering a step toward the digital transformation of multidisciplinary oncology workflows.
24 May 2026
Read appraisal →Studies in health technology and informatics
Frameworks for Digital Health Engagement: A Scoping Review Protocol
Digital health technologies (DHTs) are increasingly integral to healthcare delivery, yet sustained engagement remains inconsistent across users and settings. This scoping review aims to map frameworks and instruments that conceptualise digital health engagement across adoption and sustained use. Searches were conducted in Scopus, Web of Science, ACM Digital Library, and PsycINFO. The review will clarify conceptual coverage and identify gaps in digital engagement.
24 May 2026
Read appraisal →Studies in health technology and informatics
Can NLP Detect Loneliness in Electronic Health Records? A Proof-of-Concept Study
Loneliness is clinically important but under-documented in electronic health records (EHRs), posing challenges for secondary use and computational phenotyping. This study evaluated whether natural language processing (NLP) methods can detect and classify loneliness severity from clinical notes. Patients with a loneliness survey (mild, moderate, severe) were identified, and notes within six months prior to the survey were retrieved. An expert-expanded lexicon was applied, and transformer models (RoBERTa, ClinicalBERT, Longformer) were fine-tuned for loneliness severity classification. Large language model-based summarization of social and psychiatric history was also tested as an alternative input representation. Performance was evaluated using accuracy, weighted-F1, and per-class F1. All models achieved modest accuracy (0.3 to 0.7), and struggled to identify severe loneliness, reflecting sparse and inconsistent documentation even among surveyed patients. While summarization marginally improved accuracy, gains primarily reflected mild predictions. Manual review of 100 social worker notes from severely lonely patients found explicit mentions of loneliness in only two cases, confirming that relevant documentation is exceedingly rare. These findings demonstrate that model performance is constrained by the sparse and inconsistent documentation of loneliness in EHRs, rather than by deficiencies in the modeling approach itself.
24 May 2026
Read appraisal →Studies in health technology and informatics
MAP-CARE: Enhancing Cross-Lingual Medical Intervention Terms
Traditional keyword-based or single-language systems are not able to align data from surgical and interventional procedures, especially from non-English healthcare systems. This study aims to develop a pipeline for cross-lingual retrieval and integration of medical procedures data. Methods: MAP-CARE is a novel framework that leverages Large Language Models (LLMs) for translating and transforming medical procedures into a unified multilingual embedding space. Semantic embeddings are used to enhance retrieval accuracy and interoperability across languages and healthcare systems. Results: MAP-CARE demonstrated high accuracy in the translation and mapping of clinical terms. Its cross-language translation performance proved robust, achieving up to 90% accuracy in translating procedure classification codes across English, German, French, and Italian-when considering the correct term among the top five retrieved. Conclusion / Discussion: MAP-CARE offers a flexible, scalable, and robust solution for the multilingual and cross-system integration of medical procedural data.
24 May 2026
Read appraisal →Studies in health technology and informatics
Logic Models on Health Information Technology: First Insights from a Cross-Disciplinary Scoping Review
BACKGROUND: Health information technology (HIT) interventions are complex, yet theory-based approaches such as logic models or Theory of Change (ToC) remain rarely applied in medical informatics, although they could clarify assumptions, link activities to outcomes, and support design and evaluation. OBJECTIVES: This scoping review examined how logic models and ToC have been used in HIT research across disciplines. METHODS: Following the PRISMA-ScR guidelines, databases were searched up to August 2025. Eligible studies explicitly applied logic models or ToC to HIT. Data were extracted on HIT type, purpose of using logic models, and reported benefits and challenges. RESULTS: Sixty-nine studies met inclusion criteria, most published after 2020. A majority of studies developed logic models for patient-facing HIT such as mHealth or telehealth. Logic models were mainly used to support HIT design or evaluation and were mostly newly developed. Reported benefits of using logic models included conceptual clarity and better stakeholder communication; challenges involved resource demands and limited generalisability. CONCLUSIONS: Logic models and ToC are increasingly used for HIT but remain inconsistently applied and seldom reused. Standardised reporting and repositories could strengthen theory-informed evaluation in medical informatics.
24 May 2026
Read appraisal →Studies in health technology and informatics
Usability of Mobile Health Apps from the Patient Perspective: A Systematic Review
Mobile Health (mHealth) apps are widely used to support care, but patient-focused usability evidence remains fragmented. We conducted a systematic review (January 2012-September 2025) of PubMed, IEEE Xplore, and Scopus to identify studies assessing usability from the patient perspective using standardized questionnaires or structured interviews. Of 490 records retrieved, 146 studies met the inclusion criteria. Fifteen disease categories were represented, most commonly mental health, neurology, metabolic/endocrine, and oncology. Overall, 24 distinct usability assessment methods were reported, and most studies relied on a single method. Among standardized instruments, the System Usability Scale (SUS) was most common (50 studies, 34.3%); the mean SUS score across these studies was 76.37, above the acceptability benchmark of 68. Overall, mHealth apps were generally rated as usable by patients, but future work should adopt mHealth-specific standardized measures, enroll larger samples, and report methods transparently to enable benchmarking and inform better app design.
24 May 2026
Read appraisal →Studies in health technology and informatics
Large Language Models for Health Knowledge Modelling in Data Interoperability: A Scoping Review of Methods, Standards, and Applications
Achieving interoperability in healthcare requires semantic alignment of data with standards such as SNOMED CT, LOINC, and FHIR. Manual ontology mapping is slow and inconsistent. Evolving Large Language Models (LLMs) can automate knowledge modelling and data harmonisation. This review explores how LLMs are applied to enhance or automate health knowledge modelling for data interoperability. Following PRISMA-ScR guidance, PubMed and Compendex were searched. Studies applying LLMs to ontology alignment, terminology mapping, schema integration, or knowledge-graph construction were included. From 166 records, 20 studies met the inclusion criteria. Most used GPT-4 or BERT-derived models with retrieval-augmented or embedding-based methods. Reported gains included automation, improved mapping precision, and scalability across standards such as FHIR, OMOP, and UMLS. LLMs show strong potential for automating semantic interoperability in health informatics. Standardised evaluation benchmarks and explainable frameworks are essential for trustworthy adoption.
24 May 2026
Read appraisal →Studies in health technology and informatics
Long Covid and Digital Health in Switzerland: Potentials and Limitations
Post-COVID-19 (or Long Covid) presents a complex and persistent health challenge affecting a growing number of individuals worldwide. Digital health interventions (DHIs) offer potential support for symptom monitoring, self-management, and information access. However, their availability, quality, and sustainability remain uncertain, particularly in smaller healthcare markets such as Switzerland. This study provides a descriptive assessment of ten representative DHIs for Long Covid available from Switzerland. Each tool was evaluated using a 19-item framework derived from validated instruments. Three independent reviewers tested all interventions on iOS and Android devices, with consensus ratings across five quality categories. Results showed strong usability (average 6.3/8) and practical usefulness (5/8), but consistent weaknesses in accessibility (3.7/8) and data protection (4.6/8). Total scores ranged from 14.7 to 26.3 out of 38. Several Swiss-based apps were discontinued during the study period, highlighting market fragility. Findings underscore the need for inclusive design, transparent data practices, and institutional support if DHIs are to complement Long Covid care sustainably.
24 May 2026
Read appraisal →Studies in health technology and informatics
Hybrid AI Decision Support in Healthcare: Insights into Usability and Trust
Decision Support Systems (DSS) increasingly integrate Artificial Intelligence (AI) to enhance clinical reasoning, yet adoption depends on professionals' trust and usability. This study explored how hybrid AI decision support, combining rule-based guideline logic with generative reasoning, can be designed and evaluated in practice. Using a human-centred design approach within preventive child healthcare, a prototype integrating Large Language Models (LLMs) into an existing guideline-based DSS was developed and tested. Usability and trust were evaluated with nine healthcare professionals through task observations, usability scales, and interviews. The system showed good usability and moderate trust; highest scores were for traceability, workflow integration, and autonomy, while confidence indicators and contextual reasoning needed improvement. The study demonstrates the feasibility and acceptability of hybrid AI DSS in child healthcare and provides initial design requirements for usable, trustworthy, and human-centred AI integration across clinical domains.
24 May 2026
Read appraisal →Studies in health technology and informatics
Digital Health Tool Usability and Redesign: Preliminary Scoping Review Findings
Few usability or user experience studies report how insights resulted in improved digital health tool (DHT) design. Therefore, this paper reports on preliminary findings of a scoping review that examines this topic. A systematic search in five databases led to the inclusion of 102 studies that met the inclusion criteria, namely those reporting both usability evaluation and redesign outcomes. Categorisation of these studies identified four user groups: patients, healthcare professionals, public health users, and mixed groups. Many DTHs were web- (36%) or mobile-based (32%) applications, and most often newly developed (68%).
24 May 2026
Read appraisal →Studies in health technology and informatics
Training Future Health and Information Technologies Professionals on the European Health Data Space.
The European Health Data Space (EHDS) furthers the EU's digital health transformation by enabling secure access to electronic health data by facilitating environments for health data sharing. The topic of EHDS is relatively new and remains underrepresented in most academic curricula in both medical and informatics fields. This study evaluates the impact of a structured educational intervention to improve awareness, understanding, and preparedness among future health and information technology professionals to engage with EHDS-related systems and policies. Conducted at the University of Oulu, the intervention included training sessions, practical examples, and interactive discussions on topics such as the primary and secondary uses of health data, legal frameworks, and technical infrastructure. The survey's results showed significant gains in students' conceptual understanding of EHDS, particularly in distinguishing between primary and secondary uses of health data. We found that students with a European background reported slightly higher familiarity with EHDS-related rights and frameworks. The findings underscore the importance of embedding EHDS education in academic programs to build a digitally competent, informed workforce capable of supporting EHDS implementation. This intervention contributes to the portfolio of learning materials, developed by the SUSA project to integrate digital health policy and governance into multidisciplinary higher education curricula across the EU.
24 May 2026
Read appraisal →Studies in health technology and informatics
Advancing Knowledge in Evaluating the Clinical Impact of Large Language Models for Clinical Text Summarization: A Narrative Review
Large language models (LLMs) are increasingly explored for clinical text summarization from electronic health records (EHRs), where they could reduce documentation burden. While promising, errors in generated summaries may propagate misinformation, misrepresent clinical facts, and ultimately pose risks to patient safety. This review examined how the clinical impact of LLM-based summarization has been evaluated across four dimensions: utility, failure, patient safety risks, and bias. Literature was retrieved from PubMed between June 2024 and September 2025. Of the 144 retrieved studies, 32 studies were included in the analysis. Failure analysis was the most frequently reported (n=16, 50%), focusing on inaccuracies, omissions, and hallucinations, though definitions and methods varied widely. Utility analysis (n=8, 25%) examined workflow efficiency, readability, and understandability. Bias analysis (n=7, 22%) considered gender, race, and stigmatizing language. Only one study explicitly conducted patient safety risk analysis (n=1, 3%), using risk matrices. Findings indicate that evaluation frameworks are gradually moving beyond technical performance metrics; however, methodologies remain heterogeneous and lack standardization. Future research should establish consistent error taxonomies, structured risk assessment frameworks, and systematic bias detection methods to enable the safe clinical deployment of LLM-based summarization systems.
24 May 2026
Read appraisal →Studies in health technology and informatics
Electronic Patient Records, Real-Time Clinical Documentation and Burden: An Umbrella Review
This umbrella review synthesises evidence from reviews on clinicians' documentation burden (DB) associated with electronic patient records (EPRs) in hospital and primary care settings. Guided by Donabedian's model (structure, process, outcomes), it maps causes of burden, barriers to real-time documentation, and technological solutions. Following the Joanna Briggs Institute (JBI) methodology, searches across six databases identified 2,739 records, with 166 under full-text review. Findings of this review will inform strategies to optimise real-time EPR use while minimising DB.
24 May 2026
Read appraisal →JMIR research protocols
Protocol for Monitoring Postpartum Hypertension Outcomes via a Smartphone App (MOMitor): Ecological Momentary Assessment
BACKGROUND: Postpartum hypertension defined as elevated blood pressure after childbirth, affects approximately 20% of women after delivery. Digital health interventions that include remote monitoring therefore, present an important opportunity to facilitate regular blood pressure assessment in this high-risk population. OBJECTIVE: The primary objective is to assess within- and between-person changes in hypertension status over the first 6 weeks postpartum using a smartphone-based ecological momentary assessment protocol. The secondary objective is to characterize the trajectory of hypertension indicators across the 6-week period and to compare hypertension screening results from week one and week six within participants. METHODS: This study uses a time-series design using data from prospectively enrolled patients within a large health system. Patients eligible for recruitment were women aged 18 years or older who spoke English, owned a smartphone, had a liveborn neonate, and were willing to download an app and respond to survey questions within the app. Throughout the study period, all participants received hypertension screening items that asked whether their blood pressure was greater than 140/90 mm Hg and whether they felt dizzy or lightheaded, short of breath, or had a severe headache. A subset of participants were additionally asked to report their blood pressure readings. We estimate within- and between-participant effects using a generalized linear mixed model for a positive hypertension screen during weeks one through six. The model includes both fixed and random effects, controlling for age, race, marital status, education, insurance status, mode of delivery, total number of pregnancies, and history of diabetes mellitus. RESULTS: The study was funded July 2021 and data collection was initiated in November 2021. Data collection for this study, per protocol, was completed in June 2025, though other participants continue to be enrolled in the ongoing parent study. Data analysis is expected to be completed by Spring 2026 with publication planned for Summer 2026. CONCLUSIONS: The study findings will provide insight into trajectories of hypertension outcomes in postpartum women and the utility for smartphone app-based remote blood pressure monitoring among postpartum women whose blood pressure was regularly tracked. Findings will provide evidence to support whether widespread implementation of this digital health surveillance approach to improve outcomes in maternal health, especially in the postpartum period, is clinically warranted.
23 May 2026
Read appraisal →JMIR research protocols
Effect of a Personalized Mobile Health Intervention Using Artificial Intelligence (the WARIFA App) Versus a Nonpersonalized Intervention on User-Defined Objectives, Healthy Lifestyles, and Management of Type 1 Diabetes (T1D): Protocol for a Randomized Controlled Trial
BACKGROUND: Noncommunicable diseases are the leading cause of death worldwide. Cardiovascular and respiratory diseases, cancer, and type 2 diabetes share common risk factors that can be addressed: physical activity, a healthy diet, and avoiding smoking and alcohol. The Watching the Risk Factors (WARIFA) mobile health app was created for general health awareness and to support users in adopting healthier behaviors, as well as to support type 1 diabetes (T1D) self-management. OBJECTIVE: This study aims to evaluate the effectiveness of the WARIFA app with personalized artificial intelligence (AI)-driven messages, compared to a nonpersonalized version, in promoting health-related behavior change among the general population and individuals with T1D. METHODS: A total of 108 European participants, including individuals with T1D, were to be randomized (computer-generated sequence, double-blind, 1:1 ratio) to an intervention or control group. In the intervention group, participants used the WARIFA app with personalized messages and the use of AI. This applied to certain functionalities, such as providing recommendations on healthy dietary habits based on food logging and offering advice and encouragement through daily step tracking. It also provided risk predictions for cardiovascular and respiratory diseases, cancer, and type 2 diabetes. Participants with T1D were offered glucose predictions based on previous measurements. In the control group, participants used a WARIFA app without personalized messages or AI. Both WARIFA app versions offered access to air quality and UV index information for the geographical area, as well as displaying physical activity in the form of daily steps and sleep hours, as well as glucose results for participants with T1D. Both groups were provided with an activity monitor and used the WARIFA app for 8-12 weeks. The primary outcome is a self-defined goal, chosen from a set of proposed objectives at baseline and assessed at the end of the study using a Likert scale (1 to 10 points, 0 being no achievement at all and 10 being full achievement of the objective). Secondary outcomes include: engagement with the app, changes in lifestyle behavior, body composition, lipid profile, glycated hemoglobin (T1D only), hypoglycemic events (T1D only), and health-related quality of life, as well as acquired knowledge, self-efficacy, and usability. RESULTS: The clinical trial took place between January and June 2025. A total of 88 participants were finally recruited. The data are being analyzed, and the results are expected to be published in 2026. CONCLUSIONS: There is evidence that improving lifestyle behavior can prevent noncommunicable diseases. In this study, we aim to evaluate the effectiveness of the WARIFA app to improve lifestyle behaviors and T1D management. TRIAL REGISTRATION: ClinicalTrials.gov NCT06918444; https://clinicaltrials.gov/study/NCT06918444. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/84510.
23 May 2026
Read appraisal →Journal of medical Internet research
Reporting of Telehealth Implementation in Cystic Fibrosis: Scoping Review Using a Novel Theory-Based Evaluation Lens
BACKGROUND: Many inductive reviews exploring telehealth and its application in health care have identified missing or inconsistently reported implementation data, calling for a standardized approach to telehealth research. OBJECTIVE: Using cystic fibrosis (CF) as a case exemplar, this study evaluated the adherence of telehealth research to standardized reporting frameworks through a theory-based evaluation lens to assess implementation reporting quality and identify knowledge gaps and strengths across the literature. METHODS: We conducted an updated systematic review of the PubMed, Scopus, and Web of Science databases using a novel deductive approach to identify relevant scientific papers available in English and focusing on the delivery of telehealth interventions to CF populations as part of or alongside routine CF care. Two relevant reporting checklists were identified in the Equator Network database (Guidelines and Checklist for the Reporting on Digital Health Implementations [iCHECK-DH] and Template for Intervention Description and Replication for Telehealth [TIDiER-telehealth]) to extract data from the papers. Each checklist category was described as being "fully reported" (score=2), "partially reported" (score=1), and "did not report" (score=0) for each paper. An overall score was calculated for adherence to the checklists. RESULTS: In total, 98 studies published between 2006 and May 2025 were included in this review, with the majority appearing during the COVID-19 pandemic (2021-2022). Most studies were conducted in a single country, predominantly the United States, Australia, and the United Kingdom, and were published in medical journals. Telehealth was variably described, with video call-based models in combination with remote monitoring being most common. The median score was 22/40 (range 11-29, 55.0% adherent) for iCHECK and 15/24 (range 6-23, 62.5% adherent) for TIDiER, demonstrating moderate overall reporting quality. For iCHECK, ≥50% of studies fully reported 6/20 categories, partially reported 9/20 categories, and did not report 3/20 categories. For TIDiER, ≥50% of studies fully reported 4/12 categories, partially reported 6/12 categories, and did not report 1/12 categories, indicating persistent gaps in intervention description despite improved partial reporting. CONCLUSIONS: Key areas, such as justification for telehealth, target populations, and outcomes, are well documented, offering valuable insights into the rationale for and outcomes associated with telehealth. However, implementation processes remain underreported, partly due to the more recent adoption of frameworks like iCHECK and TIDiER. The clinical implications of the current evidence limit the implementation of telehealth in terms of the ability to assess feasibility and readiness for adoption; understand financial implications and plan sustainably; ensure patient safety, data protection, and equity; interpret outcome data in context; and share, replicate, or scale evidence-based models of care. Strengthening the commitment to standardized telehealth reporting will ultimately support clinical decision-making and improve the effective and equitable integration of telehealth into care.
23 May 2026
Read appraisal →Journal of medical Internet research
Effectiveness of a Blended Intervention to Promote Physical Activity Among Office Employees: Randomized Controlled Trial
BACKGROUND: Regular moderate-to-vigorous physical activity (MVPA) reduces the risk of noncommunicable diseases, yet one-third of adults globally fail to meet MVPA recommendations. Office employees, among the least active groups, face heightened risks due to prolonged sedentary behavior and barriers like lack of time, fatigue, and low motivation. Although scalable, web-based interventions frequently face challenges, such as low engagement, high attrition, and limited personalization. Blended interventions, combining digital tools with interactive components, show promise but lack robust evidence among office employees. OBJECTIVE: This study aimed to evaluate the effectiveness of a theory- and evidence-based blended intervention in increasing MVPA among office employees. METHODS: This 24-week, 3-arm randomized controlled trial included 141 office employees, randomized equally into blended (web-based+electronic workshops [e-workshops]), web-based only, and control groups. The intervention was developed using the intervention mapping framework, ensuring a systematic, evidence- and theory-based design tailored to the needs of the target population. All participants accessed a study website; the control group used the "Library" module with general MVPA information, while intervention groups also accessed a tailored, theory-driven "Intervention" module. The blended group additionally attended 3 live e-workshops (weeks 2, 4, and 8). MVPA was objectively measured using hip-worn accelerometers at baseline (T1), postintervention (12 wk, T2), and follow-up (24 wk, T3). Retention, engagement, and acceptability were assessed. Analyses followed the intention-to-treat principle, and generalized linear mixed models were used to evaluate between-group differences at T2 and T3, adjusted for baseline MVPA, sex, and ActiGraph LEAP (Ametris) wear time. RESULTS: A total of 141 participants (mean age 32.1, SD 9.2 y; 75/141, 53.2% female) were randomized evenly across groups with comparable baseline characteristics, except for sex (P=.002). The blended group showed significantly greater MVPA increases than the control at T2 (β coefficient=0.247, 95% CI 0.013-0.480; P=.04) and T3 (β coefficient=0.373, 95% CI 0.139-0.607; P=.002), and greater increases than the web-based at T2 (β coefficient=0.287, 95% CI 0.062-0.512; P=.01) and T3 (β coefficient=0.368, 95% CI 0.138-0.597; P=.002). Sensitivity analyses confirmed the robustness of these findings. Retention and engagement rates were 83% and 56%, respectively. Participants reported high acceptability across all domains. CONCLUSIONS: A blended intervention integrating tailored web content with interactive e-workshops significantly increased MVPA at T2 and T3 compared with both web-based and control groups. This success stems from the rigorous intervention mapping framework, which systematically identified effective behavior change and habit formation theories, while the blended approach addressed key limitations of traditional and web-based methods. These findings underscore the potential of evidence- and theory-based blended approaches to address common barriers to MVPA and promote sustainable MVPA in workplace settings, offering promising opportunities for broader scalability and personalization.
23 May 2026
Read appraisal →JMIR human factors
Development and Psychometric Validation of a Usability Instrument Based on ISO 25010 for Electronic Health Record Systems in Peruvian Health Care Settings: Methodological Study
BACKGROUND: The usability of electronic health records influences patient safety, clinical efficiency, and the sustainable digital transformation of health care. While tools such as the System Usability Scale exist, few are based on structured software quality models or align with local regulatory frameworks, particularly in low- and middle-income countries. OBJECTIVE: This study aims to develop and psychometrically validate an instrument to assess electronic health record usability, based on the ISO 25010 (International Organization for Standardization) quality model, and in accordance with the Peruvian Technical Health Standard (Norma Técnica de Salud [NTS]). METHODS: A 3-stage methodological study was conducted following COSMIN (Consensus-based Standards for the Selection of Health Measurement Instruments) guidelines. First, a 23-item instrument was developed, grouped into 7 usability dimensions based on ISO 25010. Second, content validity was assessed using a modified Delphi method with 10 experts, using Aiken's V coefficient. Third, structural validity and reliability were evaluated in 115 participants (physicians, nurses, and administrative staff) using confirmatory factor analysis and Cronbach α. Additionally, the module's functional compliance with the NTS was verified. RESULTS: The instrument demonstrated excellent psychometric properties among 115 participants (53 physicians, 48 nurses, and 14 administrative staff); high content validity (Aiken's V=0.95; 95% CI 0.84-0.98), excellent internal consistency (α=.968), and optimal factor fit (Comparative Fit Index=1.000; Tucker-Lewis Index=1.001). Overall usability was perceived as very high by 94 of 115 users (81.7%), and high by 21 of 115 users (18.3%), with no perceptions at lower levels. The module met 28 of 35 requirements (80%) of the Peruvian Technical Health Standard. Administrative staff reported higher satisfaction (13/14, 93% rated it as very high) compared with clinical professionals (81/101, 80% rated it as very high), particularly in the dimensions of error protection and operability. CONCLUSIONS: This study provides the first fully psychometrically validated instrument in Spanish based on ISO 25010, System Usability Scale, and other theoretical foundations for evaluating the usability of information systems. The results highlight the importance of considering both international standards and local regulatory requirements (NTS) in the design of digital health systems. The instrument is applicable in Spanish-speaking contexts and can serve as a reference for future usability evaluations in the region as well as in low- and middle-income countries.
23 May 2026
Read appraisal →Journal of medical Internet research
Measuring eHealth Literacy in the European Economic Area, Switzerland, and the United Kingdom: Scoping Review
BACKGROUND: Digital tools continue to evolve and have the potential to improve health care delivery. However, they are associated with challenges, including accessibility issues and health misinformation. Individuals need eHealth literacy (eHL) to reliably use these tools, and providers require appropriate eHL measurement approaches to offer targeted solutions. For around 2 decades, researchers have been operationalizing and measuring eHL. OBJECTIVE: This paper aims to provide an up-to-date overview of how eHL has been assessed in recent years in the European Economic Area, Switzerland, and the United Kingdom and which methodological limitations need to be considered. METHODS: A scoping review was conducted. Records were searched via CINAHL, PubMed, and Google Scholar on January 31, 2025, and January 28, 2026. Peer-reviewed empirical papers published in German or English since 2020 that measured eHL in the European Economic Area, Switzerland, or the United Kingdom were included. The synthesis covered the publication trend, eHL measurement approaches and associated limitations reported in the included papers, eHL measurement frequency, countries and languages, and samples. RESULTS: In the final analysis, 132 papers published between 2020 and 2025 were included. The number of publications per year showed an overall upward trend (2020: 11/132, 8.33%; 2025: 35/132, 26.52%). Nine self-report eHL measurement instruments were used, the eHealth Literacy Scale being the most frequent (94/132, 71.21%). All included papers (132/132, 100%) reported data collection via surveys, and digital surveys were common (71/132, 53.79%). Reported limitations included potential self-report biases (37/132, 28.03%), selection biases due to the data collection modes (26/132, 19.70%), and limitations specific to 5 eHL measurement instruments (18/132, 13.64%). Most included papers (121/132, 91.67%) reported eHL results from a single measurement per participant. Data were collected in 22 countries within the target regions. The distribution of eHL measurement instruments varied considerably between countries. Data were collected in 22 languages within the target regions. Patients or individuals with health problems were a frequent target group (64/132, 48.48%). Most papers (104/132, 78.79%) described studies covering broad adult age ranges. Sample sizes ranged from ≤50 (7/132, 5.30%) to >2000 participants (8/132, 6.06%). CONCLUSIONS: To our knowledge, this is the first scoping review synthesizing eHL measurement limitations reported in empirical papers from the European Economic Area, Switzerland, and the United Kingdom. The identified limitations reported in the included papers potentially lead to biased results. Therefore, health care providers and researchers should take various factors into account when selecting eHL measurement approaches, such as eHL measurement purpose, target population, and data collection setting. Future research should address these constraints by adapting and developing new or revised eHL measurement instruments, including translated and culturally adapted versions. Policymakers should encourage health care providers to conduct methodologically well-founded eHL measurements as a basis for targeted solutions.
23 May 2026
Read appraisal →JMIR human factors
Support, Monitoring, and Reminder Technology for Mild Dementia (SMART4MD) for People With Mild Cognitive Impairment and Their Informal Caregivers: Cost-Effectiveness Analysis
BACKGROUND: Support, Monitoring, and Reminder Technology for Mild Dementia (SMART4MD), a customized tablet app, was developed to improve or maintain the quality of life of people with mild cognitive impairment (MCI) and their informal caregivers. OBJECTIVE: This study conducts an 18-month economic evaluation of the SMART4MD app, in addition to standard care, compared with standard care alone in Sweden and Spain, from a health care provider perspective. METHODS: In a pragmatic randomized controlled trial, people with MCI and their informal caregivers were randomized to the intervention and control groups. Health care costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs) were measured in 345 Swedish people with MCI and their informal caregivers, and in 347 Spanish people with MCI. RESULTS: The analysis showed higher incremental costs and lower QALYs for Swedish people with MCI than for controls, whereas higher incremental costs and higher QALYs were observed for Spanish people with MCI. The intervention was not found to be cost-effective for Swedish informal caregivers, with an ICER of €78,000/QALY (€1=US $1.16). CONCLUSIONS: The differing findings regarding cost-effectiveness for people with MCI in Sweden and Spain highlight the need for further research with extended follow-up, ideally involving a larger sample size and conducted across different national contexts. TRIAL REGISTRATION: ClinicalTrials.gov NCT03325699; https://clinicaltrials.gov/ct2/show/NCT03325699.
23 May 2026
Read appraisal →JMIR human factors
Emotion-Adaptive Large Language Model-Driven Clinical Decision Support: User Evaluation of the Empathic Clinical Decision Support System Framework for Trust and Explainability
BACKGROUND: The increasing prevalence of cannabis use has motivated researchers to develop computational behavioral models that predict usage patterns and related health impacts in naturalistic environments. However, the opaque nature of many artificial intelligence (AI) systems limits users' ability to interpret outputs and undermines trust. Existing explainable artificial intelligence techniques often remain overly technical and do not account for the confusion or frustration clinicians may experience when interpreting complex model explanations. OBJECTIVE: We propose and evaluate an empathic clinical decision support system (empathic-CDSS) that integrates large language models (LLMs) with real-time emotion recognition and explainability modules. The goal is to provide transparent, adaptive, and emotionally attuned explanations that enhance interpretability, user confidence, and trust in AI-assisted clinical decision-making. METHODS: Our empathic-CDSS integrates explainable artificial intelligence, causal inference, and affective computing within an interactive LLM-driven framework. Users' affective states were inferred using the circumplex model of affect, which characterizes emotions along 2 dimensions: valence (the degree of pleasure or displeasure) and arousal (the level of physiological activation or energy). Based on the captured emotional signals, the system dynamically adjusts the tone, structure, style, and complexity of its explanations generated by a fine-tuned LLM, enabling personalized and plain-language explanations. A total of 33 participants with diverse medical and technical backgrounds engaged with the system through guided evaluation tasks and postsession assessments to evaluate 6 dimensions of user-centered questions: usability, personalization and relevance of insights, clarity and comprehensibility, system benefits, satisfaction, and trust and reliability. RESULTS: Our empathic-CDSS effectively generated personalized and transparent explanations that revealed the causal reasoning behind model predictions while enhancing users' emotional engagement and trust in the system's decision logic. Continuous, affect-based feedback enabled the system to dynamically adapt explanation delivery to individual user needs. Participants reported significantly improved usability, clarity, satisfaction, and trust compared with a baseline clinical decision support system without emotion adaptation. Improvements were also observed across additional evaluation dimensions, including personalization and perceived system benefits, supporting the feasibility and added value of integrating empathy-aware communication into AI-driven clinical decision support. CONCLUSIONS: This study introduces a transparent, trustworthy, and emotionally adaptive framework for AI-assisted prediction and clinical decision support. By uniting causal reasoning, affective sensing, and LLM-based natural-language explanations, the empathic-CDSS offers a novel direction for developing emotionally intelligent and user-centered AI systems, with potential applications in behavioral monitoring and personalized interventions, including cannabis use and related health domains.
23 May 2026
Read appraisal →Journal of medical Internet research
Human-AI Interaction in Kidney Transplant Decision Support Systems: Qualitative Study of Patient and Support Person Expectations
BACKGROUND: Artificial intelligence (AI) is increasingly applied in medicine, including clinical decision-making. AI-based decision support systems (DSS) can enhance early risk detection and treatment optimization. However, the perspectives of patients and their support persons on AI-assisted DSS in clinical care, particularly regarding shared decision-making (SDM), remain underexplored. OBJECTIVE: This study investigates the expectations, informational needs, and perceptions of patients who underwent kidney transplantation and their support persons regarding AI-assisted DSS and its influence on SDM in posttransplant care. METHODS: In a longitudinal qualitative study, 36 semistructured interviews were conducted with patients who underwent kidney transplantation and their support persons at a German kidney transplant center. Participants were asked about their views on AI's role in follow-up care, its impact on communication, trust, and decision-making, as well as their informational needs regarding AI-assisted DSS. Interviews were transcribed, pseudonymized, and analyzed using framework analysis. RESULTS: Participants recognized AI's potential to support clinicians by identifying risks of transplant loss, rejection, and infection, and by providing data-driven treatment recommendations. However, they emphasized that final decisions should remain with physicians. A majority of participants (n=28, 78%) expressed concern that AI might depersonalize care and diminish physician-patient communication due to a lack of "human touch." Participants demonstrated limited understanding of AI-based DSS functionality and highlighted the need for simple, accessible educational materials (eg, leaflets) explaining AI operations. While most doubted AI could replicate human empathy, some acknowledged that AI might be perceived as more attentive than time-pressured physicians, offering consistent monitoring and support. Participants consistently stressed that AI should augment, not replace, clinical decision-making. CONCLUSIONS: Patients who underwent kidney transplantation and support persons endorse the integration of AI in follow-up care when it enhances clinical decision-making without supplanting the physician's role. Acceptance and trust depend on transparency, accountability, and preserving the "human touch" in care. The development of educational tools to communicate AI functions and limitations is crucial to empower patients and support persons in SDM processes and to ensure AI complements, rather than undermines, patient-centered care.
23 May 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
Acceptability of Using Large Language Models to Support Smoking Cessation Attempts: a Qualitative Study Among African Americans Who Smoke
INTRODUCTION: African Americans face significant disparities in tobacco-related diseases and access to smoking cessation resources. Digital health interventions using large language models (LLMs) show promise in increasing engagement and successful quit attempts. However, impressions regarding acceptability of LLM-based interventions among African Americans are unknown and evidence suggests such interventions require tailoring for underserved communities. Our goal was to evaluate perceptions of LLM-based interventions among African Americans to inform development of an LLM-based chatbot to support quit attempts. METHODS: We conducted remote focus groups with adult African Americans residing in Minnesota who smoke cigarettes to determine acceptability of LLM-based health interventions and inform the functionality of an LLM-based chatbot supporting quit attempts. Discussions followed a structured guide and were recorded to facilitate analysis. Transcripts were coded independently by two analysts with a shared codebook. RESULTS: 21 adults participated in focus group discussions. 57.1% of participants were female and the mean age was 45.6 (SD, 10.5). Three themes united participants' feedback: (1) participants limited familiarity with LLM and related AI technology, (2) willingness to use an LLM-based app to support quit attempts, and (3) perceptions of features that impact the acceptability of an LLM-based chatbot to support quit attempts. CONCLUSION: African Americans have complex perceptions and expectations regarding the use of LLMs to deliver health interventions. While many recognize the potential benefits, significant concerns and knowledge gaps remain. To build trust and acceptability, intervention developers should prioritize community engagement and ethical guidelines to ensure that LLM-driven solutions are inclusive and effective for multiple communities. IMPLICATIONS: Large language models and related AI technologies have shown promise for improving access and engagement when deployed through digital health interventions. Focus groups with African Americans who smoke combustible cigarettes revealed factors impacting the acceptability in using LLM-based interventions to support quit attempts. Efforts to build digital health interventions should prioritize community engagement to reduce apprehensions and improve alignment with community needs and values.
23 May 2026
Read appraisal →PloS one
Audit data governance for disability-inclusive public services: A systematic review and integrative S-A-C framework
This study consolidates interdisciplinary evidence on how audit data governance shapes accountability and service equity in data-intensive public services, with specific sensitivity to disability-inclusive delivery. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020, we conducted a protocol-driven systematic literature review of peer-reviewed studies and selected institutional and standards sources published between 2010 and 2025. We searched Scopus and complemented database retrieval with targeted searches of audit, digital government, accessibility, and data-governance portals. We applied a theory-led narrative synthesis to accommodate heterogeneity in designs, domains, and outcome reporting. Synthesizing 125 records, we find that governance effects are produced through three interacting dimensions-service delivery for people with disabilities (S), audit data governance (A), and data-driven culture (C)-that jointly condition transparency, contestability, and distributive impacts. Service delivery arrangements shape where data are generated, which encounters are recorded, and whether access needs, reasonable accommodations, and complaints become organizationally legible. Audit data governance conditions the credibility and usability of audit trails through provenance, metadata, retention, audit logging, and role-appropriate oversight access, shaping both oversight feasibility and privacy risk. Data-driven culture conditions how evidence is interpreted and acted upon, including data literacy, the standing of experiential evidence, and whether audit signals support learning or only compliance. Across the corpus, recurring tensions emerge between optimization and inclusion; transparency and privacy; standardization and reasonable accommodation; automated decisioning and meaningful contestability; and compliance-oriented auditing and learning-oriented governance. Drawing these mechanisms together, we propose an integrative framework linking S, A, and C to explain how auditability and disability inclusion are co-produced across the digital service lifecycle. The framework identifies leverage points for governance reform and evaluation and outlines implications for future empirical testing and for practitioners seeking trustworthy, oversight-ready, inclusive digital public services. A review protocol was prepared before the review; the review was not registered.
23 May 2026
Read appraisal →ACS sensors
In Situ Formation of Mechanical Interlocking Skin and Electronics Interfaces through Dual-Phase Transition of Conductive Materials
The increasing demand for daily health monitoring has accelerated the development of wearable devices. However, conventional wearables, typically based on rigid materials and external fixation methods, suffer from interfacial instability during dynamic motion and heavy sweating, leading to signal distortion and device delamination. Skin-like wearable electronics and in situ fabrication strategies have emerged to improve conformability and signal stability. However, challenges remain in achieving a stable skin-device interface and reliable performance under dynamic conditions using simplified fabrication methods. Here, we present a flexible multiparameter monitoring device that integrates with the skin through a skin-electronics interface undergoing a dual-phase transition from hydrogel to liquid and subsequently to a solid state via in situ heating and photonic crosslinking. This transition forms mechanically interlocked structures that ensure robust adhesion and stable skin contact even under humid and high-acceleration conditions. Rapid curing enabled by an integrated heating layer and optical waveguide reduces the fabrication time to 1 min, allowing efficient on-demand deployment. The device achieves stable monitoring of ECG, PPG, acceleration, and temperature signals and supports Bluetooth data transmission under accelerations up to 11 g, as well as during swimming and showering. Furthermore, machine-learning-based motion recognition and individual identification models achieve 100% accuracy. This work advances in situ-fabricated wearable electronics by improving fabrication efficiency, interfacial robustness, and environmental adaptability, providing a promising platform for portable physiological monitoring.
23 May 2026
Read appraisal →PloS one
Learning semantic similarity from sentence pairs using hybrid features centric approach and explainable siamese neural networks.
Semantic embeddings play an important role in modern natural language processing because they help models understand meaning beyond individual words. Accurate text similarity is essential for many applications such as search, automated scoring, summarization, and question answering. However, existing methods based on Term Frequency-Inverse Document Frequency (TF-IDF) or simple lexical overlap often fail when sentences differ in length, structure, or word choice. These methods are less in performance, especially when working with short or medium-length sentences where meaning is expressed in different ways. This study explores sentence-level similarity using a Siamese BiLSTM model that learns deep semantic patterns and relationships between two sentences. The model captures contextual meaning, word interactions, and paraphrastic variations more effectively than traditional approaches. Experimental results show that the proposed model achieves the highest performance among machine-learning regressors, with lower errors and improved stability. Compared to TF-IDF, cosine similarity, and feature-based regressors, the Siamese model provides more accurate judgments of semantic closeness with RMSE of 0.16 and MAE of 0.107. Feature-level analysis using TF-IDF, Jaccard similarity, and embedding distances further supports these findings. Explainable AI techniques (SHAP, LIME) confirm model transparency by highlighting meaningful semantic cues and distributing attention across important linguistic features.
23 May 2026
Read appraisal →Journal of neurology
A machine learning-derived speech index as a biomarker for Huntington's disease severity
BACKGROUND: The development of disease-modifying therapies for Huntington's disease (HD) necessitates sensitive, scalable, and objective biomarkers for patient stratification and tracking. Current clinical scales are rater-dependent and time-consuming, while neuroimaging is costly and inaccessible. We aim to develop and validate a novel Speech Index, derived from automated acoustic analysis, to stratify HD stages and predict the severity of disease. METHODS: We recruited 141 HTT gene carriers (37 premanifest, 104 manifest HD) and 69 healthy controls. Participants read a standardized passage, and the recordings were processed to extract 28 speech features. A composite Speech Index was constructed using Bootstrap LASSO Regression for feature selection. Its performance was validated against the HD stages, clinical scale scores and the volume of the caudate and putamen from MRI. RESULTS: The Speech Index significantly increased across HD stages (p < 0.001). The Index showed strong correlations with clinical measures (cUHDRS: ρ = -0.67; TMS: ρ = 0.57; SDMT: ρ = -0.63; all p < 0.001) and neuroimaging biomarkers (caudate: ρ = -0.55; putamen: ρ = -0.62; all p < 0.001). Generalized additive models confirmed the Index's high predictive value for these outcomes (pseudo-R2 from 0.430 to 0.596). CONCLUSION: We developed a fully automated, interpretable Speech Index that serves as a valid digital biomarker for HD severity. It holds promise for remote monitoring, clinical trial enrichment, and objective assessment of therapeutic efficacy.
23 May 2026
Read appraisal →Journal of medical systems
Advances in Technology-Assisted Visual Rehabilitation Therapies: Scoping Review
Visual impairment (VI) is a growing condition associated with aging, neurological diseases, and chronic eye conditions. In 2023, more than 2.2 billion people experienced VI, and at least 1 billion could have been prevented or remain untreated. Visual rehabilitation (VR) is essential for optimizing residual vision and promoting functional independence; however, only a minority have access to these services. In recent years, the development of technologies such as artificial intelligence (AI), mobile applications, and virtual reality (VRt) has transformed VR, offering accessible and personalized alternatives. This scoping review aimed to map and categorize the available evidence on technology-assisted visual rehabilitation therapies, identifying the types of technologies used, their applications, and reported clinical and functional outcomes. A scoping review was conducted following the Joanna Briggs Institute (JBI) methodology and PRISMA-ScR guidelines to map the literature published between 2015 and 2025 on technology-assisted VR therapies in adults with VI worldwide. Twenty-five studies, mainly from developed countries, were included. Interventions included videoconferencing, computerized systems, portable devices, VRt, artificial vision (AV), and smart sensors. Overall, digital technologies were shown to improve visual function, autonomy, and quality of life, with high patient satisfaction and adherence. Telerehabilitation (TR), understood as the remote delivery of visual rehabilitation services through videoconferencing and digital monitoring platforms, has emerged as a viable and cost-effective strategy. In contrast, immersive virtual reality technologies (VRt), head-mounted display systems, and multisensory integration devices have demonstrated potential to stimulate neuroplasticity and enhance functional visual outcomes.Telerehabilitation platforms, immersive virtual reality systems, portable electronic vision enhancement devices, artificial vision technologies, and sensor-based monitoring tools are emerging as promising interventions. These technologies have demonstrated improvements in visual acuity, reading performance, visual field sensitivity, patient adherence, and functional autonomy, although most studies remain small-scale and geographically concentrated in high-income settings.
23 May 2026
Read appraisal →Journal of medical Internet research
Toward Participatory Precision Health With Co-Designed Recommendations: Systematic Review of Just-in-Time Adaptive Interventions in Adolescents and Young Adults
BACKGROUND: The transition from adolescence to young adulthood (age 10-25 years) constitutes a sensitive developmental period marked by rapid biological, psychological, and social change, during which preventive health interventions can shape long-term outcomes. Mobile health tools offer accessible opportunities for tailored support for this population, but often adapt poorly to dynamic contexts, resulting in inconsistent engagement and effects. Just-in-time adaptive interventions (JITAIs), which tailor support in real time using ongoing data, are increasingly explored as precision health strategies. However, how these mechanisms are designed, implemented, and evaluated for adolescents and young adults (AYAs) has not been systematically reviewed. OBJECTIVE: This review aimed to synthesize the evidence on JITAIs developed for AYAs, examine how their adaptive mechanisms have been designed to support specific health goals and changing AYA contexts, and assess methodological reporting quality to inform future precision health intervention development. METHODS: We conducted a systematic review in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and SWiM (Synthesis Without Meta-Analysis) reporting guidelines. Twelve databases were searched for peer-reviewed studies published from 2013 to 2025. Eligible studies focused on participants aged 10 to 25 years and reported real-time adaptive mobile health interventions consistent with JITAI design principles. Two reviewers independently conducted screening, data extraction, and methodological quality appraisal using the Joanna Briggs Institute checklists. AYA coauthors contributed to all phases. Due to substantial heterogeneity in study populations, intervention content, adaptive mechanisms, comparators, and outcome measurements, findings were synthesized narratively, and no meta-analysis was conducted. RESULTS: A total of 61 unique interventions were included. JITAIs for AYAs addressed substance use (n=24, 39.3%), mental health (n=23, 37.7%), and physical health or chronic conditions (n=14, 23%). JITAI tailoring mechanisms relied predominantly on self-reported behavioral data. Decision rules were typically symptom threshold-based, and decision points were commonly daily or event-triggered. Methodological concerns with reporting on intervention administration, participant selection, and outcome measurement reliability were pervasive across all studies, limiting the interpretability of observed effects and cross-study comparisons. Ethical considerations, including researcher positioning and reflexivity, alongside the depth of reporting around participatory AYA engagement in design and implementation, were also inconsistent. CONCLUSIONS: This review contributes a novel perspective to AYA digital health by moving beyond intervention outcomes to examine how core adaptive mechanisms are operationalized for AYAs across multiple health domains, while also integrating AYA perspectives into the interpretation of findings and recommendations. Unlike prior reviews focused primarily on adults or specific conditions, it identifies broader contextual, methodological, and ethical considerations relevant to AYA precision health. These findings highlight the need for more transparent, contextually responsive, and youth-centered adaptive interventions, alongside more rigorous designs for evaluating adaptive intervention components in daily life contexts.
23 May 2026
Read appraisal →ACS sensors
Recent Advances in Electrospun Nanofiber-Based Wearable Sweat Sensors
Electrospun nanofibers, owing to their high porosity, flexibility, straightforward fabrication, and tunable physicochemical properties, have been widely employed in wearable sensors for gas detection, temperature monitoring, pulse measurement, gesture recognition, and other applications. In recent years, research on employing electrospun nanofibers in wearable sweat sensors has grown substantially. This paper provides the first comprehensive review of the research progress in electrospun nanofiber-based wearable sweat sensors, focusing on structural design, device fabrication, and potential applications. The review begins with a concise introduction to the fundamental aspects of electrospun nanofibers, including their formation principles, processing routes, material selection, and morphological characteristics. It then delves into the key components involved in constructing wearable sweat sensors based on electrospun nanofibers, such as reagent immobilization matrices, signal-enhancing materials, flexible substrates, and sweat collection components. A detailed analysis of the functional mechanisms of nanofibers in these contexts is provided, accompanied by an assessment of their advantages and limitations. Furthermore, recent applications of electrospun nanofiber-based wearable sweat sensors are systematically reviewed. Finally, the review discusses the challenges and potential future applications of electrospun nanofibers in wearable sweat sensors. This comprehensive overview aims to keep researchers informed of the latest advancements in the field and to stimulate further innovative developments.
23 May 2026
Read appraisal →ACS sensors
Artificial Intelligence-Driven Wearable Sensors for Cardiovascular Health Monitoring
Wearable sensing technologies hold great promise for continuous and non-invasive monitoring of cardiovascular health, offering new avenues for early detection and effective management of cardiovascular diseases. Despite these advances, several critical challenges remain, including inefficiencies in sensor design, the degradation of calibration model accuracy over time, and difficulties in processing complex physiological signals under real-world conditions. The integration of artificial intelligence (AI) into wearable cardiovascular monitoring systems presents transformative solutions to these limitations by improving system performance, adaptability, and clinical applicability. In this review, we provide an overview of current wearable technologies for cardiovascular monitoring, including their mechanisms, characteristics, and limitations. An in-depth discussion is subsequently presented on AI-driven wearable cardiovascular monitoring, encompassing five critical facets: sensor design, sensor calibration, signal processing, pattern recognition, and disease management. Furthermore, we analyze the challenges associated with the widespread use of AI-based wearable systems and provide insights into potential strategies for addressing these challenges. This review is expected to serve as a roadmap for future research and development in the rapidly evolving field of intelligent cardiovascular health monitoring.
23 May 2026
Read appraisal →Journal of medical Internet research
Effect of Integrated Internet-Based Acceptance and Commitment Therapy and Behavioral Activation Among Ethnic Minority Young Adults With Alcohol Use Disorder in Hong Kong: Pilot Randomized Controlled Trial
BACKGROUND: Alcohol use disorder (AUD) profoundly affected ethnic minority young adults due to unique stressors, such as acculturation challenges and discrimination. Our prior studies found that experiential avoidance behavior and discrimination significantly contributed to AUD. These findings warranted third-wave interventions, such as acceptance and commitment therapy (ACT). Nonetheless, barriers to engagement with value-based activities may affect ACT efficacy. Behavioral activation (BA) may be an alternative for this shortcoming. Integrating these interventions has shown promise. However, no studies evaluated its effectiveness in this group. Therefore, evaluating the preliminary effectiveness and feasibility of this intervention was needed. OBJECTIVE: This study aimed to evaluate the feasibility measures. The secondary objective aimed to examine its effectiveness on cumulative abstinence duration, drinking days, drinks per drinking day, heavy drinking days, alcohol abstinence self-efficacy, readiness to change, psychological flexibility (Acceptance and Action Questionnaire-version II), and everyday discrimination score. METHODS: A pilot parallel randomized controlled trial was conducted. Forty young ethnic minorities who can speak English and have AUD based on DSM-5 (Diagnostic and Statistical Manual of Mental Disorders [Fifth Edition]) criteria were recruited and randomly assigned to either Acceptance and Commitment Therapy with Behavioral Activation (ACT-BA; n=20) or Treatment-as-Usual (TAU; n=20) using a computer-generated random number; assessors were blinded to group assignment. Outcomes were collected at baseline (T0) and postintervention (T1). The analysis included descriptive statistics, independent samples t test, chi-square test, and generalized estimating equations with multiple imputations. To further supplement findings, a qualitative interview was conducted. RESULTS: A total of 148 ethnic minority young adults were screened, with an eligibility rate of 38.5% (57/148). Of these, 85.9% (49/57) consented, and among them, 81.6% (40/49) were randomized to either the ACT-BA or TAU. The retention rate was 82.5% (33/40), of which 75% (30/40) completed the postintervention assessment (ACT-BA: 16/20, 80%; TAU: 14/20, 70%). The adherence rate was 81.7% (4.9/6 sessions), and participants reported no adverse effects. Finally, 40 participants (20 for each group) were analyzed. The intervention group showed a promising improvement in drinking days (B=-4.12, 95% CI -8.10 to -0.13; P=.04, d=-0.57), drinks per drinking day (B=-1.56, 95% CI -3.06 to -0.07; P=.04, d=-1.89), alcohol abstinence self-efficacy (B=11.95, 95% CI 0.10-23.81; P=.048, d=0.81), and Acceptance and Action Questionnaire-version II (B=-6.41, 95% CI -12.77 to -0.06; P=.04, d=-0.65). CONCLUSIONS: This study, unlike existing evidence, presents an innovative integration of ACT and BA delivered via an internet-based self-help format. The findings contribute to the field by providing preliminary evidence that this integrated intervention is feasible and promising for AUD. The main implication in the real world is to conduct a fully powered randomized controlled trial to further examine its effectiveness with longer follow-up to serve as a stand-alone treatment option for ethnic minorities.
22 May 2026
Read appraisal →JMIR human factors
Telemedicine Service Experience Questionnaire for Chinese Outpatients: Development and Validation Study
BACKGROUND: Telemedicine has rapidly expanded; however, standardized, telemedicine-specific patient-reported experience measures tailored to outpatient workflows are limited in many settings. OBJECTIVE: This study aimed to develop and psychometrically validate the Telemedicine Service Experience Questionnaire (TSEQ) for Chinese outpatients using telemedicine services. METHODS: We conducted a web-based survey among outpatients who completed a telemedicine consultation at Peking Union Medical College Hospital between July 1, 2021, to August 31, 2021, and who had used telemedicine services, using an adapted Chinese Patient Experience Questionnaire that encompasses 15 questions across 4 dimensions, to investigate patients' telemedicine consultation experiences. Item generation was informed by a literature review, workflow mapping, and expert review. We evaluated the factor structure using exploratory factor analysis and confirmatory factor analysis on the full sample with cross-validation. Reliability was assessed using Cronbach α and item-total correlations. RESULTS: In total, 3338 participants completed the survey (mean age 45.3, SD 17.8 y; n=2182, 65.4% female participants; n=1827, 54.8% with college education or above). The exploratory factor analysis of the final 14-item scale resulted in 4 factors. After scrutinizing the content, these factors were labeled "Service Efficiency," "Post-treatment," "Information Guidance," and "Humanistic Care," and they demonstrated good internal consistency (Cronbach α values of 0.876, 0.840, 0.962, and 0.876, respectively). Moreover, as the average variance extracted values were greater than 0.5 and the composite reliability values were greater than 0.7, the TSEQ scale has high convergent validity. Our findings suggest that the psychometric properties of the 14-item TSEQ are valid and reliable for assessing telemedicine service experience among Chinese outpatients. CONCLUSIONS: The TSEQ demonstrates a stable multidomain structure with satisfactory reliability and validity for evaluating outpatient telemedicine service experience in China. The instrument can support routine quality monitoring and guide targeted workflow improvements. Future studies should validate the TSEQ in multisite and postpandemic samples and examine measurement invariance across key subgroups.
22 May 2026
Read appraisal →JMIR formative research
Patients' mHealth Apps Usage and Data Privacy, Security, and Confidentiality Concerns: Exploratory Study
BACKGROUND: The Technology Adoption Model (TAM) offers a potential framework for elucidating the relationships between data privacy or security concerns and behavioral intention, perceived usefulness (PU), and perceived ease of use (PEOU) of mobile health (mHealth) apps, particularly for patients' self-care management. In Saudi Arabia, limited information is available on these pertinent research areas despite the government's relentless efforts to bolster the use of mHealth apps. OBJECTIVE: This study applies the TAM and the psychosociocultural framework to explore the influence of patients' data privacy and security concerns on the PU, PEOU, and behavioral intention to use mHealth apps for self-care management in Saudi Arabia. METHODS: A cross-sectional study was conducted by recruiting patients using mHealth apps for self-care from various provinces in Saudi Arabia. Research instruments were developed based on the components of 2 theories: the psychosociocultural framework and TAM, which were then piloted, validated, and distributed to participants via Google Forms. Linear regression models were performed to test the hypothesized relationships. RESULTS: Overall, 567 patients using mHealth apps participated in the study. Slightly more than one-third (217/567, 38.2%; range 35.6%-41.4%) of the participants expressed a high level of concern regarding data privacy, confidentiality, and security, with significant predictors being female gender, higher educational qualifications, and younger age groups (<46 years). About 18% to 25% of the variance in PU, PEOU, and behavioral intention to use mHealth apps was explained by the tested factors. Patients were more likely to have higher PU following a unit decrease in data confidentiality (β=.31; P=.01) and security concerns (β=.47; P=.01). The PEOU of mHealth apps increased as users demonstrated less concern regarding data privacy (β=.18; P=.001), confidentiality (β=.24; P<.001), and security (β=.43; P=.02). Likewise, behavioral intention to use mHealth apps also increased significantly following a reduction in respondents' concerns toward data privacy (β=.18; P=.02), confidentiality (β=.24; P=.03), and security issues (β=.36; P=.01). CONCLUSIONS: Specific demographic factors and concerns regarding data security and privacy influence patients' PU, PEOU, and behavioral intention to use mHealth apps for self-care management. Targeting the age-, education-, and gender-based differences regarding the usage of mHealth apps. Health care providers and policymakers may consider age-, education-, and gender-based differences when developing strategies to improve the adoption of mHealth apps among the Saudi patient population.
22 May 2026
Read appraisal →JMIR research protocols
Mobile Health Technology for Personalized Tobacco Cessation Support in Laos (Project Support Laos): Protocol for a Randomized Controlled Trial
BACKGROUND: Tobacco use remains the leading cause of preventable morbidity and mortality in Lao People's Democratic Republic (Lao PDR). Despite the prevalence of cigarette smoking in Lao PDR (51% in men and 7% in women), no national tobacco treatment programs are available. Therefore, the development and evaluation of sustainable tobacco cessation interventions suitable for widespread adoption in Lao PDR are pressing public health needs. OBJECTIVE: This project aims to adapt our theoretically and empirically based mobile health (mHealth) technology to help people quit smoking cigarettes in Lao PDR. METHODS: Our mHealth approach includes a fully automated, interactive, personalized, smartphone-delivered intervention for behavioral treatment. This project includes 2 main phases. In the first phase, we use formative research methods to adapt our intervention content to the sociocultural context, language, and communication styles of Laotians. In the second phase, we conduct a randomized controlled trial to evaluate the efficacy of our mHealth intervention. In the trial, adult smokers are recruited through 2 national hospitals: Setthathirath Hospital in Vientiane and Champasak Hospital in Champasak Province. Participants (n=500) are randomized to either the standard care (SC; n=250) or automated treatment (AT; n=250) group. SC consists of brief advice to quit smoking delivered by research staff, self-help written materials, and a 2-week supply of nicotine replacement therapy (transdermal patches). AT consists of all SC components plus a fully automated smartphone-based treatment program that involves interactive and personalized proactive messages, images, or videos. The primary health outcome of the trial is biochemically confirmed self-reported 7-day point prevalence abstinence 12 months post study enrollment. Secondary outcomes include abstinence at 3 and 6 months post enrollment. RESULTS: This study was approved by the ethical review boards of the respective domestic and international institutions. Data collection for the formative phase occurred from January 2022 to May 2023, and data analyses are ongoing. Data collection for the trial phase is ongoing and is expected to be completed by the end of 2026. CONCLUSIONS: If the proposed project is successful, it has the potential to transform health care services for tobacco treatment throughout Lao PDR and, ultimately, to significantly reduce tobacco-induced morbidity and mortality in the country. The AT's potential for widespread adoption and sustainability is enhanced by the direct involvement of Lao governmental stakeholders at multiple national institutes. Furthermore, the US-Lao collaborative work and capacity-building activities in this project will contribute to creating a knowledge base for mHealth research applications and advancing mHealth research capacity in Lao PDR.
22 May 2026
Read appraisal →JMIR formative research
Conceptualizing Acceptance and Knowledge as Process Variables in Internet-Delivered and Therapist-Supported Cognitive Behavioral Therapy and Acceptance and Commitment Therapy in Primary Care for Insomnia: Pilot Feasibility and Process-Oriented Randomized Controlled Trial
BACKGROUND: Internet-based interventions for insomnia show promise, but understanding the process variables, such as knowledge acquisition and psychological acceptance, is crucial for enhancing digital adherence and clinical effectiveness. OBJECTIVE: This study aimed to evaluate the feasibility, adherence, and preliminary clinical signals of 2 therapist-assisted interventions-internet-delivered cognitive behavioral therapy (iCBT) and internet-delivered acceptance and commitment therapy (iACT)-for insomnia in a primary care setting. METHODS: This was a pilot randomized controlled trial. Adults seeking help for insomnia (n=18) were recruited via primary care and randomized to either a 5-module iCBT or iACT program delivered via a secure digital platform with weekly therapist feedback. Blinding of participants and therapists was not possible due to the nature of the interventions. Primary outcomes included the Insomnia Severity Index; secondary outcomes included the 9-item Patient Health Questionnaire, 7-item Generalized Anxiety Disorder, and WHO Disability Assessment Schedule. A novel sleep knowledge test was used as a process variable. The data were analyzed using split-plot analyses of variance (intention-to-treat or last observation carried forward and complete case analysis) and nonparametric Friedman and Kruskal-Wallis tests. RESULTS: A total of 18 participants were randomized (iCBT: n=9; iACT: n=9). High attrition was observed, with only 33.3% (n=3) of iCBT and 55.6% (n=5) of iACT participants completing all modules. The iACT group demonstrated a significant within-group reduction in insomnia severity (P=.01, Friedman test), whereas iCBT results were nonsignificant (P=.10, Friedman test). No significant between-group differences were found for any clinical or process variables. Participants rated both treatments as credible (Credibility/Expectancy Questionnaire scores remained stable), though qualitative feedback indicated a need for more flexible, less burdensome content. CONCLUSIONS: This pilot study demonstrates that while internet-delivered insomnia treatments are feasible and credible in primary care, high attrition remains a significant barrier. Preliminary signals suggest that iACT may be a viable alternative to iCBT, potentially offering better adherence. Larger, fully powered pilot randomized controlled trials (estimated N=404) with refined recruitment and automated retention strategies are required to determine definitive comparative efficacy and the mediating role of sleep knowledge and acceptance.
22 May 2026
Read appraisal →JMIR infodemiology
Japanese-Language AI Agent System for Human Papillomavirus Vaccine Infoveillance and Public Communication: Development and Feasibility Evaluation
BACKGROUND: Human papillomavirus (HPV) vaccine hesitancy remains a significant public health challenge in Japan, where proactive vaccination recommendations were suspended between 2013 and 2021. The resulting information gap between medical institutions and vaccine-hesitant populations is exacerbated by misinformation on social media platforms. Traditional public health communication strategies cannot address individual queries while simultaneously monitoring population-level discourse. OBJECTIVE: This study aimed to develop and conduct a feasibility evaluation of a dual-purpose artificial intelligence agent system that delivers verified HPV vaccine information to the public through a conversational interface while generating infoveillance reports for medical institutions based on user interactions and social media discourse. METHODS: We implemented a system with 3 components: a vector database integrating 139,803 documents, including academic papers, Japanese government sources, news media, and social media posts; a retrieval-augmented generation chatbot using a ReAct agent architecture with iterative multitool orchestration across 5 specialized knowledge sources; and an automated report generation system with modules for news analysis, research synthesis, social media sentiment analysis, including stance classification and topic modeling, and user interaction pattern identification. System performance was assessed using both automated and manual evaluation protocols on a scale from 0 to 5. RESULTS: The entire system functioned as expected. For single-turn evaluation, the chatbot achieved mean scores of 4.83 (SD 0.67; 95% CI 4.71-4.93) for relevance, 4.89 (SD 0.53; 95% CI 4.79-4.97) for routing, 4.50 (SD 1.29; 95% CI 4.27-4.70) for reference quality, 4.90 (SD 0.62; 95% CI 4.78-4.99) for correctness, and 4.88 (SD 0.54; 95% CI 4.78-4.96) for professional identity, with an overall mean of 4.80. Multiturn evaluation yielded higher mean scores: 4.94 for context memory (SD 0.25; 95% CI 4.84-5.00) and an overall mean of 4.98, with topic centering and identity achieving 5.00. The report generation system achieved high scores across all sections: 4.83 for completeness (SD 0.37; 95% CI 4.73-4.94), 4.88 for correctness (SD 0.33; 95% CI 4.77-4.96), and 4.12 for helpfulness (SD 0.48; 95% CI 3.98-4.27). Reference validity achieved perfect scores (5.00) across all periods, with citation correctness averaging 4.21 (SD 0.58; 95% CI 3.96-4.46). CONCLUSIONS: This feasibility study demonstrated that an integrated artificial intelligence agent system can support both public HPV vaccine communication and social media infoveillance in a Japanese-language context. Prospective deployment with real users is needed to assess actual public health impact.
22 May 2026
Read appraisal →JMIR medical informatics
A Practical Approach to Assessing the Completeness of Electronic Health Records for Medical Research: Data Quality Study
BACKGROUND: Data quality is the degree to which data are fit for their intended purpose and is described using quality dimensions. The increased use of medical data in clinical research and medical artificial intelligence development has rendered data quality assessment essential. Despite existing data quality definitions, frameworks, and tools, data quality assessment in real-world settings faces multiple challenges. This stems from a lack of understanding of how to assess real-world data quality and interpret the results. Therefore, practical approaches to data quality assessment are needed that are appropriate for diverse data environments, intended uses, quality dimensions, and requirements. OBJECTIVE: This study proposes a practical approach for assessing the completeness of electronic health records (EHRs) for medical research. This approach integrates structural completeness, rule-based assessment, and descriptive analyses of completeness and data diversity to clarify how data quality can be measured and meaningfully interpreted in practice. METHODS: The completeness of a large-scale EHR dataset from Gachon University Gil Medical Center was evaluated covering January 2005 to December 2023. Completeness was assessed using a three-part approach comprising (1) structural completeness assessment, (2) rule-based assessment, and (3) descriptive analyses of completeness and data diversity. Assessments were conducted using clinical data quality assessment tools. This practical approach was used to assess EHR completeness for medical research from 1,798,153 patient records. RESULTS: In the structural assessment, 12.8% (5/39) of the data tables were unavailable, indicating limited capturing of clinician free-text data. The rule-based assessment identified substantial missingness in vocabulary fields (38/124, 30.6%) and missing or special characteristic values in relation to observations (3,643,581/15,313,287, 23.8%), measurements (25,583,622/642,623,715, 4%), care sites (28/1715, 1.6%), and deaths (117/34,330, 0.3%). Descriptive analyses demonstrated a balanced gender distribution (886,489/1,798,153, 49.3% male and 911,664/1,798,153, 50.7% female) and a predominantly Korean racial distribution (1,739,628/1,798,153, 96.7%). Collectively, these findings illustrate the completeness quality of a multiperspective completeness assessment for medical research. CONCLUSIONS: This study demonstrates how data quality dimensions can be measured in practice through a real-world completeness assessment. This practical approach enables evaluation of EHR completeness and provides insights into data quality. Its findings have implications for researchers conducting data quality assessments and applying quality dimensions in medical research.
22 May 2026
Read appraisal →Journal of medical Internet research
Efficacy of Internet-Based Self-Help Interventions for Irritable Bowel Syndrome: Systematic Review and Meta-Analysis of Randomized Controlled Trials
BACKGROUND: Irritable bowel syndrome (IBS) is a common functional gastrointestinal disorder that reduces quality of life and causes a heavy medical burden. Internet-based self-help interventions are flexible and scalable, showing potential for IBS symptom improvement, but relevant evidence is fragmented and lacks systematic review. OBJECTIVE: This systematic review aimed to comprehensively evaluate the effects of internet-based self-help interventions on symptom severity, quality of life, and visceral sensitivity, as well as comorbid depressive and anxiety symptoms, in individuals with IBS. METHODS: Only randomized controlled trials evaluating internet-based self-help interventions for individuals with IBS were included. A literature search was conducted across PubMed, Embase, Web of Science, CINAHL Complete, PsycINFO, the Cochrane Library, and 4 Chinese databases on June 25, 2025, with an updated search on March 9, 2026. Risk of bias was assessed using the Cochrane Risk of Bias 2.0 tool. Meta-analyses were performed with the Hartung-Knapp-Sidik-Jonkman-adjusted random-effects model. Effect sizes were reported as standardized mean differences (SMDs) with 95% CIs, and evidence certainty was evaluated using GRADE (Grading of Recommendations Assessment, Development, and Evaluation) criteria. RESULTS: A total of 17 randomized controlled trials from 7 countries were included, involving 2289 participants (predominantly female). Compared with control groups, internet-based self-help interventions were associated with a statistically significant improvement in IBS symptom severity (SMD -0.52, 95% CI -0.78 to -0.26, 95% prediction interval [PI] -1.46 to 0.42), quality of life (SMD 0.57, 95% CI 0.23 to 0.90, 95% PI -0.41 to 1.54), visceral sensitivity (SMD -0.55, 95% CI -0.89 to -0.21, 95% PI -1.43 to 0.33), and depressive symptoms (SMD -0.14, 95% CI -0.27 to -0.01, 95% PI -0.27 to -0.01). However, no statistically significant improvement was observed in anxiety symptoms (SMD -0.03, 95% CI -0.34 to 0.27, 95% PI -0.78 to 0.71). The certainty of evidence was rated as moderate to very low for all outcomes. CONCLUSIONS: This review synthesizes the latest evidence on internet-based self-help interventions for individuals with IBS disease-specific and comorbid psychological symptoms. It stands out by encompassing a diverse range of such interventions and incorporating visceral sensitivity as a key outcome. In doing so, it establishes a more comprehensive multi-outcome evidence base for IBS digital interventions, advancing the field by clarifying the potential of these interventions as viable alternatives to conventional treatments. For real-world practice, these findings can inform targeted strategies for primary care and telemedicine platforms, especially in resource-limited regions. However, this review is limited by moderate bias risk, high heterogeneity, and moderate to very low GRADE evidence certainty. A wide 95% PI suggests that effect variability is linked to contextual and population factors, so findings should be interpreted cautiously. Future research should prioritize technical support, patients' digital health literacy, and standardized intervention protocols to further validate clinical utility.
22 May 2026
Read appraisal →Journal of medical Internet research
Large Language Models in Colorectal Cancer Care and Clinical Decision Support: Systematic Review
BACKGROUND: Colorectal cancer (CRC) is a leading cause of cancer morbidity and mortality worldwide. The complexity of guideline-concordant care and unstructured clinical data has driven demand for decision-support tools. Large language models (LLMs) show promise for processing clinical data and patient-provider communication, yet evidence is fragmented, and a CRC-specific synthesis across the full care continuum is lacking. OBJECTIVE: This systematic review evaluates the current applications, performance determinants, and clinical implications of LLMs across the continuum of CRC care. METHODS: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), we searched 6 databases (PubMed, Embase, Web of Science, Scopus, CINAHL, Cochrane) through April 1, 2026. Eligible studies were peer-reviewed original investigations of LLMs on CRC tasks with extractable outcomes; reviews, editorials, and abstracts were excluded. Two reviewers assessed quality with QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2), PROBAST (prediction model risk of bias assessment tool), and ROBINS-I (Risk of Bias in Nonrandomized Studies - of Interventions). Data on model types, applications, prompts, input/output formats, and outcomes were analyzed descriptively, with narrative synthesis per synthesis without meta-analysis (SWiM) guidelines. RESULTS: Of 8880 records, 37 studies met inclusion criteria (2023-2026), mostly from China and the United States, with GPT series most frequently evaluated. Overall risk of bias was low in 10/37 studies (27.0%), moderate in 14/37 (37.8%), unclear in 7/37 (18.9%), and high or serious in 6/37 (16.2%). Problematic domains included outcome measurement, intervention classification, patient selection, and lack of blinded assessment. LLMs showed utility in automating data extraction from clinical texts, supporting patient education, aiding diagnosis, and assisting clinical decision-making, with emerging visual interpretation and multimodal capacities. Domain-specific and multimodal models showed advantages over general-purpose models in certain tasks. Performance was significantly influenced by prompt design, from zero-shot queries to fine-tuning. Despite efficiency and outcome benefits, challenges persist regarding methodological quality, data privacy, and generalizability. CONCLUSIONS: This review provides an integrative framework synthesizing evidence across study designs and LLM categories in CRC care. Unlike prior reviews addressing gastroenterology broadly or limited to one design, it covers the full CRC continuum and, for the first time, comparatively evaluates general-purpose, domain-specific, and multimodal LLMs, clarifying how prompt engineering and heterogeneous metrics shape outcomes. Although findings support LLMs' clinical potential, results must be interpreted cautiously, given low overall evidence quality. Most studies lacked safeguards against bias-blinded assessment, confounder adjustment, or prospective multicenter validation. Substantial heterogeneity across tasks, LLM types, prompts, reference standards, and outcomes means reported advantages cannot be generalized. Future work should prioritize real-world integration via prospective multicenter validation, robust privacy frameworks, and rigorous human oversight. Amid rising global CRC burden and health care disparities, this review informs clinical translation, equitable scaling, and policy on LLM deployment.
22 May 2026
Read appraisal →BMJ open
Patient preferences and willingness-to-pay for AI-enabled blended type 2 diabetes care by digital experience and socioeconomic status: a discrete-choice experiment in China
OBJECTIVES: To elicit stated preferences and willingness-to-pay (WTP) for artificial intelligence (AI)-enabled blended care in type 2 diabetes mellitus (T2DM), and to examine preference heterogeneity by digital experience and socioeconomic status (SES). DESIGN: Cross-sectional discrete choice experiment (DCE). SETTING: 12 community health centres in Jiaozuo and Puyang, Henan Province, China. Data were collected between June and August 2025. PARTICIPANTS: 423 adults diagnosed with T2DM for at least 6 months, recruited using consecutive convenience sampling from routine follow-up appointments. Of 769 participants who completed the survey, 346 were excluded following prespecified data quality criteria (retention rate: 55.0%). OUTCOME MEASURES: Outcome measures included preference weights and WTP (in Chinese Yuan, ¥) for five DCE attributes: monthly subscription fee, recommendation source, feedback modality, in-person follow-up frequency and expert oversight, estimated using mixed logit models. Simulated uptake probabilities for tailored service packages across four user profiles were computed. RESULTS: Among 423 participants, 80.6% had never used AI tools. Price was the dominant driver of choice (62.7% relative attribute importance). Profound preference heterogeneity emerged across subgroups: rural residents (n=78) were highly price-sensitive but preferred physician endorsement (WTP ¥17.58 (US$2.55), 95% CI ¥5.98 to ¥29.17); female participants (n=224) valued guideline recommendations (WTP ¥18.45 (US$2.67), 95% CI ¥7.81 to ¥29.40); and diabetes app users (n=34) were the least price-sensitive but showed a negative preference for AI instant feedback, instead preferring human dietitian feedback. Expert oversight carried a consistent negative WTP across all profiles. Targeting tailored service bundles to intended subgroups increased uptake by 8-16 percentage points compared with non-targeted bundles. CONCLUSIONS: A 'digital experience paradox' exists whereby digitally experienced users view human interaction as a premium service, while underserved groups rely on specific trust markers such as physician endorsement. To avoid widening the digital divide, AI-enabled blended diabetes care must move beyond standardised models towards configurable, equity-driven service pathways.
22 May 2026
Read appraisal →The Cochrane database of systematic reviews
Artificial intelligence for detection and staging of Alzheimer's disease using retinal images
This is a protocol for a Cochrane Review (diagnostic). The objectives are as follows: To determine the diagnostic accuracy of AI algorithm-based retinal image reading for detecting referral-requiring Alzheimer's disease in adults who have behavioral symptoms or cognitive decline in the primary care setting. Secondary objectives Where data are available, we will investigate heterogeneity by incorporating covariates in a metaregression.
22 May 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Soft, skin-interfaced electronics enable cannula-free wireless monitoring of sleep respiration.
Sleep-related breathing disorders are prevalent yet frequently underdiagnosed, in part due to limitations of conventional respiratory monitoring technologies. Standard nasal cannulas introduce airflow resistance, discomfort, and poor long-term adherence, constraining at-home and longitudinal assessment. Here, we report a soft, skin-interfaced nasal patch that enables cannula-free, wireless monitoring of respiratory activity during sleep. The device is constructed from ultrathin, elastomeric materials that conform to the nasal surface, coupling respiratory-induced tissue deformation to a strain-sensing element. The mechanics of the skin-device interface and the elastomeric response govern the sensitivity and linearity of signal transduction, enabling quantitative capture of breathing dynamics. An integrated wireless platform transmits deformation signals directly to mobile devices, eliminating the need for external tubing or tethered modules. Modular fabrication permits replacement of the strain sensor and skin-contact interface without compromising mechanical performance. Mechanical characterization under physiologically relevant deformation demonstrates high repeatability and low hysteresis, while in vivo studies confirm that the patch accurately reproduces respiratory waveforms and correlates closely with gold-standard nasal cannula measurements. By integrating soft materials mechanics, wearable strain sensing, and wireless electronics, this system provides a minimally obtrusive platform for continuous respiratory monitoring. The class of technologies presented in this work establishes design principles for skin-interfaced devices, in which elastomeric mechanics, strain transduction, and wireless integration combine to enable quantitative, unobtrusive physiological monitoring in clinical and home environments.
22 May 2026
Read appraisal →Journal of medical Internet research
Receipt of Medicines Information From the Internet and Other Information Sources Among Adult Medicine Users in Developed Economies, 2010-2025: Systematic Review
BACKGROUND: The internet, social media, and digital health tools have transformed access and receipt of medicines information (MI), complementing or replacing traditional sources, such as physicians, pharmacists, and package leaflets. OBJECTIVE: This study aimed to examine the prevalence and patterns of adult medicine users' receipt of MI from electronic sources (eg, internet and social media) compared with traditional sources, and to assess trends in the receipt of MI in developed economies since 2010. METHODS: A systematic search was conducted in CINAHL, the Cochrane Library, ProQuest, Scopus (including Embase and MEDLINE), and Web of Science databases for peer-reviewed studies conducted and published between January 1, 2010, and December 31, 2025. Studies including adults (≥18 years) in developed economies were eligible. The review followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Methodological quality and risk of bias were assessed using the Mixed Methods Appraisal Tool. Due to substantial heterogeneity, findings were synthesized narratively without meta-analysis. RESULTS: Twenty-six studies from 10 countries, involving 19,891 medicine users, were included. Most studies were cross-sectional surveys (n=19), with only one being a national long-term trend study. Physicians, pharmacists, and package leaflets consistently remained the most common MI sources, irrespective of patient group, country, or study design. No clear temporal trends in MI receipt were observed. Variations in the frequency of MI receipt were evident across research methods, medicine user groups, and types of medicines. Individuals with greater familiarity with the internet were more likely to receive MI from electronic sources, such as websites and social media. In most studies, MI obtained from social media was not distinguished from other internet-based sources. Half of the studies focused on heterogeneous medicine user groups (n=14), while the remainder examined specific medicine user groups (n=12). Although the methodological quality of the studies was generally acceptable, only 3 studies reported the use of an explicit theoretical framework. CONCLUSIONS: Traditional MI sources remain central for adult medicine users despite the growing role of electronic platforms. While the receipt of MI from electronic sources appears more common among internet-experienced users, no significant temporal trends in using these sources were identified. Further research is needed to better distinguish between different digital MI sources, including artificial intelligence-based MI sources, and to explore their evolving roles, implications for health-related decision-making, and associations with user characteristics and health contexts in the digital era. This review is innovative in systematically comparing traditional and electronic MI sources across developed economies and in highlighting methodological and conceptual gaps in existing research. By synthesizing cross-national evidence and identifying the need for clearer differentiation of digital MI subtypes, the study contributes to the field and informs the development of more targeted, reliable, and user-centered MI strategies in real-world health care settings.
22 May 2026
Read appraisal →Journal of medical Internet research
Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration
BACKGROUND: Explainable artificial intelligence (xAI) is increasingly used in medical imaging to enhance transparency, clinical interpretability, and trust in artificial intelligence (AI)-assisted diagnostics, particularly in oncology. Evidence on how explainability is implemented, validated, and reported in cancer imaging remains fragmented. OBJECTIVE: This scoping review aimed to systematically map research applying xAI methods to radiologic cancer imaging, summarize methodological and clinical trends, and identify persistent gaps in validation and integration. METHODS: We conducted a structured search of PubMed and Scopus (final search executed on October 20, 2025), covering studies published from 2017 to December 2024. Eligible peer-reviewed articles using machine learning or deep learning were analyzed with a focus on xAI components. Data from 371 studies were extracted into predefined categories covering cancer type, imaging modality, AI model, xAI method, terminology, validation, code availability, and decision support system integration. RESULTS: Studies focused primarily on breast (112/371, 30.2%), lung (87/371, 23.5%), and brain (56/371, 15.1%) cancers, with prostate, thyroid, and liver cancers also represented. The primary imaging modalities were computed tomography (139/371, 37.5%) and magnetic resonance imaging (104/371, 28%). Deep learning was used in 70.1% (260/371) of studies, classical machine learning in 18.1% (67/371), hybrid pipeline methods for 10% (37/371), and emerging concept-, prototype-, or causal-based approaches accounted for 1.9% (7/371) of studies. Post hoc xAI methods were dominant (305/371, 82.2%), with visualization (163/371, 53.4%), and feature relevance (111/371, 36.4%) as the most common subcategories. Hybrid post hoc or inherent approaches comprised 12.1% (45/371) and intrinsically interpretable methods 5.7% (21/371). Data sources were mostly public (149/371, 40.2%) or mixed (100/371, 26.9%); 22.9% (85/371) used private institutional datasets, and 7.8% (29/371) did not report data sources. Among validated studies, expert or user-based validation was most common (104/193, 53.9%), followed by mixed methods (74/193, 38.3%), while quantitative metrics (10/193, 5.2%) and clinical knowledge-based (8/193, 4.1%) validation remained rare. Only 17.5% (65/371) of studies provided code and 12.1% (45/371) reported decision support system integration, with few achieving actual clinical deployment. CONCLUSIONS: This scoping review maps xAI implementation across multiple cancer imaging modalities, revealing methodological inconsistency and insufficient validation. Most research emphasizes visualization over quantitative interpretability, and few models are reproducible or clinically implemented. These findings provide an evidence base for researchers, clinicians, and regulators to prioritize standardization of xAI reporting, quantitative validation, and user-centered frameworks to advance trustworthy AI in oncology imaging.
22 May 2026
Read appraisal →European journal of pediatrics
The effect of virtual reality glasses used during inhaler treatment on anxiety, fear and vital signs in children: a randomized controlled trial
UNLABELLED: This study aimed to determine the effect of virtual reality (VR) glasses used during inhaler treatment on anxiety, fear and vital signs in children. The study had an experimental, randomized controlled design. Participants were assigned to groups using a lottery method based on simple randomization. The random sequence was generated by physically mixing up 30 red (experimental) and 30 white (control) cards. All the data were collected and finalized by two trained researchers (Yasemin Özyer Güvener, a nurse researcher, and Şeyma Şimşirgil Kara, a pediatrician). Participants were recruited using a convenience sampling method from among individuals who met the inclusion criteria. In terms of baseline measurements, the experimental group had significantly higher mean pulse rate, fear scores, and anxiety scores (130.23 ± 14.64, 2.77 ± 1.25, and 4.60 ± 2.04, respectively) compared to the control group (118.83 ± 17.10, 2.07 ± 1.26, and 2.80 ± 2.07, respectively) (p = 0.007, p = 0.035, and p = 0.001, respectively). For post-intervention measurements, the experimental group had significantly lower mean fear and anxiety scores (0.443 ± 0.16 and 0.703 ± 0.22, respectively) compared to the control group (2.39 ± 0.16 and 3.197 ± 0.22, respectively) (p < 0.001 and p = 0.001, respectively). CONCLUSION: It was determined that VR glasses reduced anxiety and fear, increased oxygen saturation, and reduced respiratory and pulse rates. They can thus be used as an alternative method to reduce fear and anxiety levels and balance vital signs during inhalation therapy in children. WHAT IS KNOWN: • Virtual reality (VR) is an effective non-pharmacological method for reducing pain, anxiety, and fear in pediatric patients during various medical procedures. • VR applications create a sense of presence in a virtual world, limiting the impact of anxiety and fear in clinical settings. WHAT IS NEW: • Using VR glasses during the administration of inhaled medication significantly reduces anxiety and fear levels in children compared to traditional methods. • While evidence for VR use during inhalation therapy was previously limited, this study demonstrates its effectiveness as a promising alternative to improve treatment compliance and physiological responses in pediatric patients.
21 May 2026
Read appraisal →JMIR formative research
Comparing Stakeholders' Perspectives on Parkinson Disease Management and Digital Technologies: Exploratory International Survey
BACKGROUND: Parkinson disease (PD) is a progressive neurodegenerative disorder that poses complex challenges for persons with PD, informal caregivers, and health care professionals. With growing interest in digital and predictive artificial intelligence (AI) tools for disease management, understanding the needs and digital readiness of these stakeholder groups is crucial. OBJECTIVE: This work aims to (1) identify digital practices for PD management among persons with PD, at-risk individuals, caregivers, and health care professionals; (2) compare these practices across groups; (3) explore stakeholder desires for AI-based tools; and (4) assess alignments and gaps to inform tailored AI solutions. METHODS: An anonymous cross-sectional online survey of an exploratory nature was distributed (from December 2024 to October 2025) in 5 languages and completed by 255 respondents. Descriptive statistics summarized responses to 41 questions, including stakeholder-specific items. χ2 tests were performed to examine stakeholder differences in desired AI features. RESULTS: Interest in predictive AI was high across stakeholder groups. Symptom tracking was the most desired feature (selected by more than 76% of the respondents), and personalized treatment recommendations came second for both persons with PD and health care professionals; however, stakeholder priorities diverged in other areas. Health care professionals rated improving patient and informal caregiver engagement as significantly more important than persons with PD did, χ21 (n=205)=34.78, P<.001, and Cramer V=0.41. Despite considerable interest, the reported use of digital tools was limited, as most persons with PD did not use symptom-tracking apps or wearables, nor were they currently monitoring their condition, although many expressed intentions to begin. CONCLUSIONS: While predictive AI tools were viewed positively across groups, there were significant gaps in stakeholder preferences, highlighting the importance of tailored, context-aware design. Early diagnosis was not prioritized by persons with PD or health care professionals, likely reflecting the complexity of diagnosing PD in the absence of disease-modifying therapies. Coupled with the emphasis placed on preventive lifestyle guidance by persons with PD and those at risk, this highlights the importance of actionability in AI-based monitoring and prediction. Such actionability may also enhance perceived relevance and uptake, given that reported interest in digital health tools and self-tracking exceeded actual use. These findings offer early-stage insight to guide the development of future AI-based solutions for PD.
21 May 2026
Read appraisal →JMIR mental health
Automated Safety Testing and Reporting Application for Conversational Safety Monitoring of Generative AI Tools for Mental Health: Development and Validation Study
BACKGROUND: Artificial intelligence (AI)-based conversational tools are rapidly expanding within mental health care as a means of increasing access and scalability. At the same time, these systems introduce distinct safety risks arising from both user disclosures (eg, self-harm ideation) and inappropriate or inadequate AI responses. OBJECTIVE: This study aimed to develop and evaluate the Automated Safety Testing and Reporting Application (ASTRA), an external system intended to identify clinically relevant risk behaviors across entire AI-mediated mental health conversations. METHODS: ASTRA was tested on a dataset of 100 synthetic therapeutic conversations written by licensed clinicians to reflect risk behaviors and harmful responses between users and AI tools. Conversations varied in length and included both subtle and overt risk behavior examples across 8 predefined categories. Human coder consensus ratings served as the reference standard. ASTRA's classifications were evaluated across 2 prompt iterations using standard diagnostic performance metrics and agreement statistics. RESULTS: ASTRA demonstrated consistently high concordance with expert human ratings across all categories. Accuracy exceeded 0.90 for all risk behavior categories examined, with specificity uniformly high and sensitivity varying by category (range 0.55-1.00). Agreement beyond chance was substantial to almost perfect between ASTRA and human raters (κ=0.65-1.00). Detection of user self-harm indicators was particularly accurate, even in conversations where risk was expressed subtly. CONCLUSIONS: In this initial validation study, ASTRA reliably identified multiple forms of mental health-related risk behaviors at the conversation level. These findings support the feasibility of independent safety monitoring systems as a complement to AI tools used in mental health contexts and underscore the need for further evaluation using larger and real-world datasets.
21 May 2026
Read appraisal →Journal of medical Internet research
A Digital Toolkit for Weight Loss Maintenance in European Adults (NoHoW): 2×2 Factorial Randomized Controlled Trial
BACKGROUND: Digital approaches to weight management have the potential to produce cost-effective and scalable weight management solutions. Effective behavior change interventions typically promote self-regulation of energy balance behaviors, which may be enhanced by incorporating emotion regulation strategies. OBJECTIVE: This study aimed to evaluate the effectiveness of a digital behavior change toolkit for weight loss maintenance in European adults who had achieved ≥5% weight loss in the previous 12 months. We hypothesized that a combined intervention targeting self-regulation or motivation and emotion regulation would be more effective than either component alone, and that each would outperform an active control. METHODS: The Navigating to a Healthier Weight (NoHoW) trial was a 2×2 factorial randomized, single-blind, controlled trial involving 1627 adults who had achieved ≥5% weight loss in the previous 12 months (initial BMI ≥25 kg/m2) across 3 European centers (the United Kingdom, Denmark, and Portugal). The trial evaluated a digital toolkit for weight management subsequent to an initial ≥5% weight loss in the prior 12 months. Participants were assigned using adaptive stratified sampling to one of four groups: (1) self-regulation or motivation (n=403), (2) emotion regulation (n=416), (3) combined motivation and emotion (n=408), or (4) active control (generic content, regular self-weighing, and Fitbit use, n=400). The primary outcome was weight change from baseline to 12 months. Prespecified secondary outcomes included cardiometabolic markers. Linear models adjusted for recruitment center, sex, age group, BMI group, and pretrial weight loss. Subgroup analyses were conducted by sex. RESULTS: At 12 months, 76% (364/1627) of participants remained in the study. In the primary ITT analysis in all participants, none of the intervention arms (motivation, emotion, or combined) differed significantly from the active control for weight change at 12 months. Completer and per-protocol analyses produced similar patterns and did not change the overall interpretation. In the per-protocol sample, men regained 0.14 kg, and women regained 0.54 kg of their pretrial weight loss. Subgroup analyses indicated a small effect of the motivation intervention in men, but this was not clinically meaningful and did not alter the primary null findings. Nearly half of ITT participants regained weight, and no significant intervention effects were observed for cardiometabolic secondary outcomes. CONCLUSIONS: The NoHoW trial was the first large-scale, multicountry 2×2 factorial randomized controlled trial to evaluate a digital-only toolkit based on self-regulation or motivation and emotion regulation techniques for weight loss maintenance. NoHoW found no evidence in the primary ITT analysis that digital interventions targeting self-regulation or emotion regulation improved weight loss maintenance compared with the active control. A small subgroup effect in men should be interpreted cautiously and does not change this conclusion. The trial provides evidence on both the limitations and potential of digital behavior change interventions for long-term weight outcomes. Future digital interventions may benefit from enhanced engagement and tailored content to improve long-term weight outcomes.
21 May 2026
Read appraisal →Journal of medical Internet research
Alignment Between Cardiologists and AI-Driven Diagnostic Systems: Mixed Methods Study
BACKGROUND: The clinical value of artificial intelligence (AI)-based diagnostic systems depends not only on their accuracy but also on how well their outputs integrate with clinicians' judgments in practice. Critical knowledge gaps remain regarding diagnostic concordance between AI and clinicians in stress echocardiography interpretation, patient characteristics predicting discordance, and how cardiologists respond when AI recommendations conflict with their clinical diagnoses. OBJECTIVE: This study examined the diagnostic alignment between an AI-driven stress echocardiography system (EchoGo Pro [EGP]) and cardiologists' diagnoses of coronary artery disease (CAD), identified predictors of concordance and AI scan rejection, and explored cardiologists' decision-making strategies when disagreements arise. METHODS: We conducted mixed methods research. The quantitative study analyzed concordance between EGP and cardiologists using data from 854 participants with suspected CAD in the multicenter PROTEUS randomized controlled trial. Logistic regression identified predictors of agreement, disagreement, and scan rejection, adjusting for age, sex, smoking status, BMI, and cardiovascular risk factors (hypertension, hypercholesterolemia, diabetes, family history of CAD, and prior CAD events). To gain deeper insight into discordance, we conducted a qualitative study analyzing survey responses from 61 UK consultant cardiologists recruited via Qualtrics, exploring their perceptions of AI tools, the risks of following discordant AI recommendations, and their typical responses to AI-clinician disagreement. RESULTS: EGP and cardiologists agreed in 60% (512/854) of the cases, but agreement was significantly lower among patients with hypertension (OR 0.58, 95% CI 0.38-0.89; P=.01), diabetes (OR 0.56, 95% CI 0.35-0.90; P=.02), and pre-existing CAD (OR 0.48, 95% CI 0.30-0.77; P=.002). EGP rejected 26.1% (222/854) of the scans due to insufficient image quality, with rejection significantly more common in male patients (β=0.35; P=.03) and those with a family history of CAD. If a positive CAD diagnosis was assigned when either cardiologists or EGP identified CAD, the proportion of positive cases increased from 17.9% (153/854) to 22.1% (189/854), potentially identifying additional at-risk patients. Survey respondents (50/60, 85% male; 26/57, 46% aged 40-49 years; 39/61, 64% White) required 65% to 69% confidence in their initial diagnosis to justify disregarding contradictory AI recommendations. The survey findings revealed cardiologists treated AI recommendations as advisory rather than definitive. When facing discordance, they retained confidence in their judgment and sought corroboration through additional testing, data review, or second opinions rather than deferring to AI. Paradoxically, cardiologists with higher confidence in AI tools required greater confidence in their own diagnosis to disregard AI recommendations (β=7.73; P=.02). Cardiologists attributed discordance primarily to AI's inability to incorporate patient history, comorbidities, and broader clinical context. CONCLUSIONS: EGP shows promise as an adjunctive tool but struggles with multimorbid patients and exhibits high, uneven rejection rates. Cardiologists use AI to prompt scrutiny, not replace judgment. Future systems need to integrate wider patient data with imaging and minimize bias through representative training to avoid exacerbating inequities.
21 May 2026
Read appraisal →JMIR formative research
Development, Feasibility, Acceptability, and Usability of an Artificial Intelligence-Powered Chatbot (Suzy) to Support Patients in Substance Use Disorder Recovery: Multiphase Study
BACKGROUND: Substance use disorder (SUD) remains a major public health crisis in the United States, with significant challenges in treatment access, retention, and workforce capacity. SUD care teams, including addiction medicine physicians and peer recovery coaches (PRCs), support patients receiving SUD treatment but face heavy workloads and burnout. Artificial intelligence (AI) innovations, particularly large language model (LLM)-based chatbots, may extend PRC support and provide patients with on-demand recovery support between clinic visits and PRC contacts. However, evidence on their development, feasibility, acceptability, and usability in addiction services remains limited. OBJECTIVE: This study describes the development, feasibility, acceptability, and usability of an AI-powered health coaching chatbot (Suzy) designed to support patients in SUD recovery. METHODS: A total of 2 clinicians, 5 researchers, and 2 technology developers led a small, multiphase pilot study. In the formative phase, they conducted focus groups and qualitative in-depth interviews with 12 health care professionals and 8 patients with substance use histories to specify chatbot functions and develop a rule-based chatbot. In phase 2, they conducted usability testing of the rule-based chatbot with 8 patients who reported substance use and completed standardized tasks, surveys, and qualitative interviews. Measures included the System Usability Scale (SUS), Net Promoter Score (NPS), and Single Ease of Use Question (SEQ). In phase 3, they developed an LLM-based chatbot co-designed and fine-tuned with PRCs and other SUD experts. RESULTS: Rule-based chatbot functions included craving management, appointment reminders, resource referrals, care team contacts, and goal setting. Usability task testing supported feasibility. In this small pilot sample, quantitative and qualitative feedback indicated acceptability and usability, with an average SUS score of 93 (benchmark 68), an NPS of 63 (benchmark 35), and a mean SEQ score of 6.5/7. Patients valued Suzy's approachable, nonjudgmental language and features that promoted accountability, self-monitoring, and 24/7 availability, while emphasizing that chatbots should supplement but not replace human support. The LLM-based chatbot development emphasized information accuracy, safety escalation protocols to mitigate risks of inappropriate chatbot responses, human-in-the-loop features, and expanded conversational flexibility and personal tailoring. CONCLUSIONS: In this pilot study, a rule-based chatbot designed to support SUD care demonstrated feasibility, usability, and acceptability. LLM-based chatbot development required more robust safety and emergency reporting features, while offering more patient-responsive conversational functions. By providing on-demand coaching, referrals, and reminders, Suzy may extend the reach of care teams, alleviate provider burden, and enhance patient engagement. Additional work is needed to understand how to best integrate Suzy into patients' recovery journeys to ensure human support remains accessible and prioritized. LLM evaluation was based on expert testing and safety review. Clinical effectiveness, including the impact on substance use, was not evaluated. Next steps include evaluating the LLM chatbot in real-world settings with larger samples and assessing its efficacy in reducing substance use.
21 May 2026
Read appraisal →JMIR medical education
A Competency Framework for Medical AI Education: Mixed Methods Study
BACKGROUND: Although artificial intelligence (AI) is increasingly adopted in health care, clinicians face barriers, including insufficient understanding, limited trust, and challenges in interpreting AI outputs. Existing frameworks, such as the United Nations Educational, Scientific and Cultural Organization (UNESCO) AI competency framework, lack clinical specificity. Additionally, there remains limited evidence on framework-based training programs for medical professionals. OBJECTIVE: This study aimed to (1) develop a medical AI competency framework and (2) apply the framework to design an AI training program and pilot 1 module. METHODS: We conducted a mixed methods study comprising 2 phases. In Study 1, we developed a medical AI competency framework by integrating the UNESCO AI framework with the Miller pyramid model. The framework was refined through expert input from 24 stakeholders (6 hospital administrators, 8 medical professionals, and 10 university instructors). Expert responses were analyzed using deductive content analysis based on predefined codebooks. In study 2, we designed an AI training program based on the framework, and evaluated it using a 2-round Delphi process. Nine educators with expertise in instructional design, medical education, and AI participated in the Delphi study. Consensus in round 1 was defined as an IQR≤1, agreement score proportion >75%, and full-score frequency >49.23%; in round 2, consensus was defined as agreement score proportion >80%. A pilot workshop with 28 participants and 4 instructors assessed the feasibility of 1 module using self-reported measures of satisfaction, engagement, and confidence. RESULTS: A 6D 4-level medical AI competency framework was developed. Among the 24 experts, 19 (79.17%) mentioned competencies related to AI foundations, and 23 (95.83%) mentioned competencies related to application skills. The framework was translated into a 5-module training program, covering patient-centered and ethical AI, privacy and security, medical AI applications, bias and health equity, and generative AI in health care. Each module included 5 elements: content, learning goals, teaching activities, learning resources, and assessment. The Delphi process achieved complete consensus across all 25 elements of the training program. The pilot workshop indicated high participant satisfaction (mean 4.00, SD 0.52), good engagement (mean 3.80, SD 0.71-mean 4.05, SD 0.51), and moderate self-reported confidence (mean 3.63, SD 0.53). These findings suggest that the module was feasible, although outcomes should be interpreted cautiously given the self-reported and short-term nature of the evaluation. CONCLUSIONS: The framework provides a structured reference for AI training program design in medical education. The workshop findings provide preliminary support for the feasibility of the program's technical module, while also highlighting the need for broader and longer-term evaluations. Future work should expand the framework and training program to new regions and delivery formats (eg, semester-long courses and continuing medical education) and evaluate their long-term impact.
21 May 2026
Read appraisal →Brain topography
fNIRS-STCT: A Novel Hybrid Spatial and Temporal CNN and Transformer Network for fNIRS Signal Classification
Functional near-infrared spectroscopy (fNIRS) is a non-invasive brain imaging method that detects neural activity by monitoring blood oxygen levels and circulation changes in the brain. Some current fNIRS classification models are simply adapted from deep learning models such as Transformer and fail to effectively leverage both temporal and spatial information from fNIRS. In this paper, we develop a hybrid deep learning model for fNIRS classification, fusing spatial convolutional neural network (CNN), a temporal CNN, and Transformer, named as fNIRS-STCT. Primarily, the architecture employs dual large convolutional kernels - one for temporal feature extraction and another for spatial feature extraction. Second, spatial and temporal CNNs are respectively integrated with Transformer by feeding CNN-extracted features as input to Transformer and then concatenating CNN and Transformer features. Third, spatial and temporal features are fused by element-wise addition. Finally, we exploit cross-entropy as the loss function, which integrates both online label smoothing and flooding strategies to avoid overfitting. The model's effectiveness is demonstrated through testing on two public fNIRS datasets. Across subject-dependent, subject-semi-dependent, and subject-independent evaluation schemes, fNIRS-STCT model consistently outperforms other compared models for all given performance metrics. Specifically, on the unilateral finger- and foot-tapping dataset, our model achieves accuracies of 83.29%, 81.73%, and 79.60% across three evaluation schemes, surpassing the second-best model by margins of 3%, 2%, and 1%, respectively. The findings underscore fNIRS-STCT's effectiveness in cross-subject mental state and brain activity interpretation.
21 May 2026
Read appraisal →Biomedical microdevices
Automated blood group classification using a digital microfluidics chip and vision transformer-based image analysis
Identifying blood groups accurately is critical for safe medical practices, especially in emergencies, surgeries, and prenatal care. Conventional methods often depend on visual inspection of agglutination reactions, which can be error-prone, particularly when using small sample volumes. In this work, we introduce an efficient and intelligent system for blood type detection that combines digital microfluidics with advanced deep learning and antigen-based decision logic. A Vision Transformer (ViT) model was trained to recognize agglutination patterns in droplet-based blood images, followed by an automated mapping of standard ABO/Rh rules to assign the corresponding blood group based on antigen presence. The proposed system achieved 100% accuracy specifically in detecting antigen-antibody agglutination reactions, with perfect scores across precision, recall, specificity, and F1-score. Our method reduces the need for large reagent volumes and minimizes testing cost, while also improving reliability. In future work, our aim is to embed this system into a compact, microfluidics paper-based device, enabling low-cost, AI-assisted blood typing in portable point of care settings.
21 May 2026
Read appraisal →Scientific reports
Use of machine learning and voice for multiclass classification of Parkinson's disease, chronic obstructive pulmonary disease, and healthy controls.
Parkinson's disease (PD) and chronic obstructive pulmonary disease (COPD) are prevalent conditions with substantial impact on quality of life and health care systems. Both disorders affect voice production through different physiological mechanisms, yet neither condition has a widely adopted objective biomarker for routine clinical use. Voice analysis has emerged as a non-invasive digital biomarker candidate, but existing studies have largely focused on binary classification within a single disorder or language. This study aimed to evaluate whether an unified multiclass machine learning (ML) framework applied to sustained vowel "a" phonation can discriminate between PD, COPD, and healthy controls (HC) across linguistically distinct cohorts. Sustained vowel recordings were analyzed from Swedish speaking individuals with COPD and HC, and English-speaking individuals with PD and HC, collected under comparable mobile recording conditions. Acoustic features included baseline voice measures and Mel Frequency Cepstral Coefficients. A soft voting ML framework integrating support vector machine, random forest, CatBoost, and light gradient boosting classifiers was trained using nested cross validation with hyperparameter optimization. Data were partitioned at the participant level into a development cohort and an independent test cohort. Model performance was evaluated using accuracy, macro averaged precision, recall, F1 score, receiver operating characteristic analysis, and confusion matrices. Model interpretability was assessed using Shapley additive explanations and vowel space analysis. The final soft voting classifier achieved robust multiclass discrimination on the participant disjoint independent test set, with an overall accuracy of 0.842 and a macro averaged F1 score of 0.839. Classification performance differed across groups, with the highest performance observed for PD, intermediate performance for HC, and lower performance for COPD. Misclassifications occurred primarily between HC and COPD, while confusion between PD and COPD was minimal. Feature attribution analysis revealed class dependent relevance patterns, and vowel space analysis demonstrated subtle but consistent group level differences. These findings demonstrate the feasibility of using an explainable soft voting machine learning framework applied to sustained vowel phonation to distinguish between neurologically and respiratory driven voice impairments across linguistic contexts. The study supports voice as a promising digital biomarker modality for multiclass clinical discrimination using mobile recordings.
21 May 2026
Read appraisal →Journal of clinical pathology
AI triage of duodenal biopsies improves workflow
AIMS: To develop, deploy and evaluate artificial intelligence (AI) for triaging duodenal biopsies within a National Health Service (NHS) histopathology laboratory, with the aim of improving reporting turnaround times for clinically significant diagnoses. METHODS: The pathway was developed in the UK in an NHS laboratory. Rule-based automation software was used to find all newly scanned duodenal biopsy slides. Those with case numbers ending in an odd number were exported for AI triage and if they had significant AI predicted abnormalities they were prioritised for reporting. The cases with even numbers followed the routine reporting pathway. RESULTS: 313 cases (517 duodenal slides) were processed by the routine pathway, and 329 cases (533 duodenal slides) were processed by the AI triage pathway. AI processing took about 70 s per slide. The AI classifier had a sensitivity and positive predictive value (PPV) as follows: normal small bowel: 99.6%, 95.7%; coeliac disease: 86.7%, 100%; gastric heterotopia: 84.6%, 95.7%; adenoma: 88.9%, 88.9%; adenocarcinoma: 50.0%, 100%. In the AI triage workstream coeliac disease, and non-neoplastic abnormalities as a group, were reported quicker than in the standard workstream (6 days vs 10 days and 7 days vs 10 days, respectively, both p<0.005), but neoplastic lesions were not reported quicker. The cost of deployment and operation was reasonable. CONCLUSIONS: An NHS histopathology laboratory successfully developed and implemented an AI-based triage system for duodenal biopsies, achieving high diagnostic accuracy and significantly improving turnaround times for coeliac disease and non-neoplastic abnormalities as a group. This study demonstrates the feasibility and clinical value of locally developed AI tools within routine diagnostic practice.
21 May 2026
Read appraisal →JMIR formative research
Metaverse-Based Virtual Reality for Remote Anatomy Education: Pilot Randomized Controlled Trial
BACKGROUND: Traditional anatomy teaching relies on cadaveric dissection and 2D resources, which often require in-person attendance and may limit spatial understanding. Virtual reality (VR) provides an immersive, remote alternative that supports 3D visualization from home. Recent evidence suggests that while VR may yield comparable factual knowledge gains to 2D methods, its primary value lies in enhancing learner engagement, motivation, and perceived educational value. OBJECTIVE: This pilot randomized controlled trial compares remote synchronized VR with didactic animated anatomy lectures for the teaching of tracheostomy anatomy. METHODS: Participants were recruited via convenience sampling through the VRiMS (Virtual Reality in Medicine and Surgery) Surgical Society network. All participants first attended a synchronous 20-minute online lecture delivered by a consultant surgeon. They were then individually randomized to one of 2 groups using a computer-generated sequence. Allocation was concealed until the intervention; however, participants and researchers were unblinded. The intervention group completed a 10-minute metaverse-based VR session, delivered by a consultant surgeon via 3D Organon's Medverse platform on the PICO 4 Ultra headset. The control group completed a 10-minute prerecorded 2D animated lecture, accessed on their personal device. Participants then swapped to the other modality. Data were collected via Google Forms at 3 intervals (baseline, postintervention, and postcrossover) to assess confidence, spatial understanding, and knowledge (10-item multiple-choice questions). The analysis of nonparametric data utilized Wilcoxon signed-rank tests for within-group changes and Mann-Whitney U tests for between-group differences. RESULTS: Twenty-four medical students from 11 United Kingdom and Irish medical schools participated. Adherence was 100%, with all participants completing their assigned 10-minute intervention and all assessment points. Ninety-two percent of participants (n=22) reported no prior tracheostomy anatomy teaching. Additionally, 83% (n=20) had no prior remote-synchronized VR anatomy teaching experience. Anatomical confidence improved significantly in the VR group compared with animation (mean change 1.58, SD 1.00 vs mean 0.50, SD 0.80, P=.01). Knowledge scores improved significantly in both groups (VR: mean 1.75, SD 1.54, P=.007; animation: mean 2.83, SD 1.70, P=.003), with no significant postintervention difference between groups (P=.46). VR participants reported significantly superior spatial understanding across all measured domains (all P≤.009). These included depth perception (3.75 vs 2.58, P=.009), appreciation of anatomy from different viewpoints (4.25 vs 2.33, P=.001), mental reconstruction from varying angles (3.83 vs 2.08, P=.002), and spatial depth supporting anatomical understanding (4.08 vs 2.08, P=.001). Following the completion of both modalities, participants rated VR as more engaging (mean 4.54, SD 0.78) and more educationally effective (mean 4.29, SD 0.95) than animation. CONCLUSIONS: Remote VR teaching is feasible and engaging and enhances spatial understanding compared to animation. While knowledge gains were comparable between modalities, VR improved learner confidence and perceived 3D comprehension. Hence, VR may represent a scalable adjunct or alternative to traditional anatomy teaching.
20 May 2026
Read appraisal →JMIR medical informatics
Extracting Social Determinants of Health From Electronic Health Records: Development and Comparison of Rule-Based and Large Language Model Methods
BACKGROUND: Social determinants of health (SDoH) are critical drivers of health outcomes but are often underdocumented in structured electronic health record (EHR) data. Instead, SDoH are more commonly recorded in unstructured clinical notes, and unlocking this information could have far-reaching implications for advancing population health research and informing clinical decision-making. OBJECTIVE: This study develops and systematically evaluates cost-efficient methods for extracting SDoH information from unstructured clinical text using rule-based natural language processing (NLP) and large language model (LLM)-based approaches. METHODS: We constructed a gold-standard annotated corpus comprising clinical text segments from 171 patients in the Mass General Brigham Research Patient Data Registry, covering 7 SDoH domain categories and 23 subcategories. A rule-based system (RBS) was developed and evaluated alongside 7 OpenAI GPT models (GPT-4o, 4.1, 4.1-mini, o4-mini, GPT-5, GPT-5-mini, and o3) under zero-shot and few-shot settings using multiple prompting strategies. We additionally implemented late-fusion ensemble approaches that combined outputs from rule- and LLM-based methods. Performance was assessed using precision, recall, and F1-score, alongside qualitative error analysis. RESULTS: The RBS achieved high precision for SDoH domain categories (0.96) but substantially lower recall (0.68). GPT-based models consistently outperformed the RBS in overall recall and F1-scores. The best domain-level performance was observed for GPT-5 and GPT-5-mini in few-shot settings (F1-score=0.89), while o4-mini achieved the highest subcategory-level performance (F1-score=0.88). A late-fusion ensemble integrating RBS and GPT outputs further improved domain-level performance (F1-score=0.92), with balanced precision (0.93) and recall (0.90), but did not improve subcategory-level performance. CONCLUSIONS: Recent GPT models with advanced reasoning capabilities, including the newly released mini models (eg, o4-mini and GPT-5-mini), demonstrated strong performance for SDoH extraction without task-specific fine-tuning and consistently outperformed the rule-based NLP system. Integrating rule- and LLM-based methods via late fusion further enhanced domain-level extraction performance. Our results demonstrate a cost-efficient framework for the accurate identification of SDoH from clinical text, facilitating downstream population health research and clinical informatics applications.
20 May 2026
Read appraisal →Sexually transmitted infections
Complex role of digital health literacy in awareness and use of digital sexually transmitted and blood-borne infections testing: a structural equation modelling analysis of the 2022 GetCheckedOnline survey
BACKGROUND: Although digital health literacy (DHL) is recognised as a determinant of access to digital sexually transmitted and blood-borne infection (STBBI) testing, empirical evidence about its contribution to access disparities remains limited. We applied multidimensional DHL measures to examine inequities in awareness and use of GetCheckedOnline, British Columbia's (BC) publicly funded digital STBBI testing service. METHODS: We analysed data from GetCheckedOnline's 2022 community survey of English-speaking BC residents aged ≥16 years who were sexually active in the past year. Outcomes were awareness and use of GetCheckedOnline (yes/no). DHL was measured using latent factors from the eHealth Literacy Scale: Information Navigation, Resource Appraisal and Confidence in Use. Structural equation modelling (SEM) was used to estimate associations and mediation pathways between DHL, sociodemographic characteristics and service outcomes. Model fit was assessed using standard SEM indices. RESULTS: Among 1657 respondents (mean age 33 years, SD 11.77), Information Navigation was positively associated with awareness (β=0.162, p<0.001) and use (β=0.063, p=0.020) of GetCheckedOnline. Confidence in Use was positively associated with awareness (β=0.206, p=0.014) and use (β=0.115, p=0.020). In contrast, Resource Appraisal was negatively associated with awareness (β=-0.263, p=0.006) and use (β=-0.150, p=0.010). DHL factors mediated the effects of age, income, education and digital access on both outcomes. CONCLUSIONS: DHL operates as a multidimensional and socially patterned determinant of access to digital STBBI testing services. While information navigation and confidence in use facilitate access, higher resource appraisal may reduce use, potentially reflecting concerns about service fit, privacy or trust. Findings highlight the need for digital interventions that are not only accessible but also contextually relevant, trusted and responsive to the needs of diverse users.
20 May 2026
Read appraisal →PloS one
Deep Learning outperforms physicians in myopathy and neuropathy classification based on Needle Electromyography Signal
Needle electromyography (nEMG) is a valuable tool for diagnosing patients with neuromuscular diseases. However, it is labor-intensive and is prone to diagnostic inaccuracies stemming from human biases. To address these challenges, we validated an nEMG diagnosis-aiding system with minimal preprocessing using deep learning model to classify patients into three categories: normal, myopathy, and neuropathy. Using 376 nEMG signals from 57 patients from a tertiary university hospital database through nested k-fold cross validation, deep learning model surpassed the classification performance of six electromyographers. The median patient classification accuracy, precision, sensitivity, and specificity of the deep learning model was 0.70, 0.70, 0.70, and 0.85, respectively, whereas those of the physicians were 0.55, 0.60, 0.54, and 0.78, respectively. Model interpretability and failure analysis showed that the deep learning model classifies based on relevant signal features. Despite higher accuracy of DL model, the number of unanimously misclassified cases were higher in the DL model than physicians. Our study validates deep learning is a fast, accurate, and practical application to aid physicians in diagnosing patients using nEMG signals.
20 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Parkinson disease severity detection based On OPtFuzNet with fused features
BACKGROUND: Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting millions of people worldwide. It severely impairs patients' mobility. For effective treatment strategies, it is essential to determine the severity of the disease at an early stage. METHODS: In this study, an optimized feature-fusion framework is developed to detect the severity of Parkinson's disease using gait data via an Optimized Fuzzy Neural Network (OPtFuzNet). This feature-fusion framework can be categorized into three stages: pre-processing, feature extraction, and classification. In the pre-processing stage, a median filter is employed to reduce noise present in the gait images. During feature extraction, local and global features of the gait images are captured using SAE and IDCN, respectively. Subsequently, these features are fused and fed into the OPtFuzNet classifier, where parameter optimization is performed using IGWO-Lévy. RESULTS: The proposed model is evaluated on the GAIT-IT and GAIT-IST benchmark datasets using standard metrics, achieving superior performance with accuracies of 98.08% and 98.12%, respectively. Furthermore, a feature importance analysis is conducted to identify the most influential gait characteristics that contribute to determining the severity of the impairment, thereby enhancing the clinical interpretability of the model.The proposed model is evaluated on the GAIT-IT and GAIT-IST benchmark datasets using standard metrics, achieving superior performance with accuracies of 98.08% and 98.12%, respectively. Furthermore, a feature importance analysis is conducted to identify the most influential gait characteristics that contribute to determining the severity of the impairment, thereby enhancing the clinical interpretability of the model. CONCLUSION: These results validate the efficacy of the proposed feature fusion and optimization strategy in identifying discriminative gait patterns associated with the severity of Parkinson's disease. However, as the datasets are based on simulated gait patterns rather than real clinical data, further validation on real-world datasets is necessary for practical applicability.
20 May 2026
Read appraisal →Journal of medical Internet research
Co-Lifecycle Governance for Learning Medical AI: A Hybrid Convergence Framework for Adaptive Regulatory Oversight
Artificial intelligence (AI) in health care is increasingly defined not by static algorithms but by adaptive intelligence-systems that evolve over time through interactions with data, clinicians, and clinical environments. This adaptive capacity creates a structural mismatch with regulatory frameworks built for technologies whose behavior remains static. As AI models drift, recalibrate, or degrade in real-world contexts, they dissolve the linear boundaries between design, deployment, and clinical interpretation. These temporal, epistemic, and organizational frictions expose responsibility gaps that cannot be resolved through incremental modifications to legacy oversight structures. Regulators across major jurisdictions are beginning to respond to these challenges, though with differing orientations. The United States advances mechanisms for predictable adaptation, including Predetermined Change Control Plans, real-world evidence frameworks, and life cycle-oriented quality management reforms. The European Union emphasizes precautionary, rights-based governance through the European Union Artificial Intelligence Act (AI Act) and modernized liability rules. South Korea, operating within a hyperconnected digital health ecosystem, has introduced the Digital Medical Products Act (DMPA), one of the world's first comprehensive statutory frameworks for learning medical AI. Despite philosophical differences, these regulatory trajectories converge on a shared insight: learning AI systems cannot be governed by static rules or episodic evaluation. This viewpoint proposes Co-Lifecycle Governance as a conceptual framework to synchronize regulatory oversight with adaptive intelligence. Rather than treating oversight as a discrete event, Co-Lifecycle Governance frames regulation as a continuous, synchronized process grounded in 4 pillars: continuous validation, agile change management, proactive performance surveillance, and distributed accountability. Each pillar functions as a structural antidote to the responsibility frictions that arise when AI systems evolve faster than expectations surrounding them. Together, these pillars provide a governance grammar capable of supporting safe, iterative model improvement while maintaining system-level trust. Drawing from the strengths of US predictability, European Union accountability, and Korean scalability, this paper outlines a hybrid convergence pathway that synthesizes predictability, accountability, and operational feasibility. Learning AI will not wait for governance to catch up; oversight must evolve in lockstep with adaptive intelligence. Co-Lifecycle Governance offers a foundation for regulatory systems that not only regulate learning AI but also learn with it-at the speed at which adaptive intelligence actually changes.
20 May 2026
Read appraisal →JMIR formative research
Detection of Posttraumatic Stress Disorder With Rest-Activity Data: Machine Learning Approach Using Wearable and Self-Report Data
BACKGROUND: Growing evidence suggests that disruptions in rest-activity rhythms may serve as relevant markers of posttraumatic stress disorder (PTSD). Despite the emergence of machine learning methods applied to actigraphy and self-report data, few studies have used these approaches to identify individuals with clinically diagnosed PTSD. Prior work has focused on predicting probable PTSD based on self-report measures, yet discrepancies exist between clinical diagnoses and probable PTSD derived from self-reports. OBJECTIVE: This study explored whether wrist actigraphy and sleep logs could be used to accurately predict clinician-rated PTSD diagnosis and probable diagnosis of PTSD based on established self-report cutoffs (PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [PCL-5] ≥31 and ≥38) among trauma-exposed service members and veterans. We also explored which features were most strongly predictive of each outcome and whether models were able to predict PTSD diagnosis even when accounting for other mental health disorders. METHODS: Wrist actigraphy data and daily sleep logs were collected over 1 week from trauma-exposed male service members and veterans (N=36; mean age 41, SD 5.3 y). Candidate features were identified using univariate feature selection. Extreme gradient boosting models were trained using leave-one-subject-out cross-validation to predict the diagnosis of PTSD and probable diagnosis of PTSD based on 2 self-report cutoffs (PCL-5≥31 and ≥38). Performance metrics were then calculated at the person level. Linear regression was used to assess the discriminant validity of model-predicted scores and each PTSD outcome specifically, relative to other mental health diagnoses. RESULTS: Machine learning models predicting PTSD diagnosis and probable PTSD based on the PCL-5≥31 threshold demonstrated satisfactory performance in this sample. The diagnosis model achieved an area under the curve (AUC) of 0.83 (95% CI 0.61-1.00), with high accuracy (88%) and specificity (96%) and moderate sensitivity (63%). The PCL-5≥31 model yielded comparable performance (AUC=0.84, 95% CI 0.71-0.98) with balanced sensitivity (73%) and specificity (82%). For both models, a combination of subjective and objective features was the most impactful. These models were able to predict PTSD even when accounting for non-PTSD mental health diagnoses, as model-predicted scores were significantly associated with 2 outcomes: clinician-rated PTSD (B=0.19; P=.002) and probable PTSD based on a PCL-5≥31 cutoff (B=0.24; P=.003). In contrast, the model predicting probable PTSD based on the PCL-5≥38 threshold performed poorly (AUC=0.47, 95% CI 0.24-0.69), with a nonsignificant relationship between model-predicted scores and the outcome (B<0.01; P=.89). CONCLUSIONS: Both subjective and objective rest-activity features may improve the prediction of PTSD. Further research is needed to validate these findings and explore the use of integrating wearable sensor data and subjective information to support PTSD assessment.
20 May 2026
Read appraisal →The British journal of dermatology
Artificial intelligence-based smartphone application for skin cancer detection: a prospective diagnostic accuracy study
BACKGROUND: Smartphone applications ('apps') that use artificial intelligence (AI) for skin cancer diagnosis may help with early detection. However, prospective studies assessing real-world performance are scarce. OBJECTIVES: To evaluate in an independent prospective study the diagnostic accuracy of a widely used skin cancer detection app and the effect of photographic conditions in a user-representative cohort of patients consulting for lesions of concern. METHODS: Between 1 February 2021 and 30 June 2023, participants presenting at an early-access consultation with a lesion of concern were consecutively enrolled. Following dermatological assessment, each lesion was photographed using the app. The app's convolutional neural network (CNN) generates a binary risk output and advice. A teledermatology review was performed in a subset of cases. The diagnostic accuracy of the CNN combined with teledermatology review was compared with final clinical or histopathological diagnosis. Performance was tested under different photographic conditions (angle, lighting, user) and with different smartphone models. The trial was registered with ClinicalTrials.gov (NCT05246163). RESULTS: A total of 1458 participants with 1904 lesions of concern were included. Of these, 185 (9.7%) were skin cancers, with 32 melanomas. Image capture was unsuccessful in 16.6% of lesions (n = 317/1904), despite optimal conditions. For successfully captured lesions, the CNN achieved a sensitivity of 82.5% and specificity of 76.8% for skin cancer detection. In-app teledermatology review was available for 65.7% (n = 1042/1587) of images. Combined CNN and teledermatology review resulted in increased specificity (86.8%; P < 0.001), with a sensitivity of 75.3%. Overall sensitivity in melanocytic lesions was lower than in nonmelanocytic lesions (71.0% vs. 76.3%; P < 0.001), whereas specificity was higher (90.0% vs. 84.9%; P < 0.001). A substudy demonstrated low image-capture success in user hands (28.9%) and variation in diagnostic performance between smartphone models. CONCLUSIONS: This independent, prospective study evaluated diagnostic performance of a widely used skin cancer detection app in a user-representative cohort. The findings highlight the importance of independent clinical validation of AI-based healthcare tools in real-world settings. Skin cancer is one of the most common cancers that affects people worldwide. Finding cancer early is important, especially for melanoma, a serious form of skin cancer. There is potential for smartphone apps to use AI to help people identify skin spots that may need medical attention. In this study, we evaluated a widely used app called SkinVision. People presenting with a worrisome spot at a dermatology appointment were invited to take part in this study. Altogether, 1458 people were included. The skin area of concern was assessed with the app. Using AI, the app produced a risk score. It also gave a final score. The final score was the AI risk score that may be combined with dermatology advice delivered remotely (‘teledermatology’). We compared the app scores with the doctor’s final diagnosis. We also studied the impact of things such as photo angle, lighting, type of smartphone and the person who took the photo. We found that over 16% of worrisome skin areas could not be captured by the app under ideal conditions. If participants were asked to take the photo themselves, this increased to over 70%. The app correctly identified skin cancers in 83% of cases but gave false alarms in 23%. The in-app teledermatology review reduced the number of false alarms, but this increased the number of missed skin cancers. Finally, we found important differences between different smartphones. Our findings highlight the importance of trialling apps like this with real patients in real-life setting before they are fully introduced to the public.
20 May 2026
Read appraisal →PloS one
A computational text analysis of recent African digital health strategies and their attention to vulnerable populations
BACKGROUND: Digital health offers opportunities to transform healthcare systems and improve access to care, particularly in low- and middle-income countries. However, there is concern that national digital health strategies may inadequately address the needs of vulnerable populations, risking the reinforcement of existing health inequalities. OBJECTIVE: This study investigates the extent to which recent African national digital health strategies consider and prioritize vulnerable populations particularly at risk of digital exclusion (women, children and the elderly). METHODS: We conducted a computational text analysis of 13 recent national digital health strategy documents from African countries to identify major topical priorities within the strategies and to systematically assess the frequency and context of references to vulnerable populations. RESULTS: Most strategies focus on broad goals such as health system strengthening, infrastructure development, and stakeholder coordination. While there are mentions of women and children, these references are often indirect and not accompanied by concrete plans or dedicated actions. References to the elderly are especially rare. Few strategies include specific measures to ensure digital inclusion or equity for these vulnerable populations. CONCLUSIONS: Recent African digital health strategies currently prioritize systemic and infrastructural development, but the needs of vulnerable populations are often overlooked or superficially addressed. To prevent the digital divide from widening, national strategies should embed explicit equity targets, actionable plans, and mechanisms for meaningful inclusion and participation of all population groups.
20 May 2026
Read appraisal →PloS one
Implicit bias in safety-aligned large language models: A multi-faceted evaluation of clinical decision-making and health equity
BACKGROUND: Large language models are increasingly integrated into healthcare for clinical decision support and patient communication. Although these models can pass explicit social bias tests, they may retain implicit biases-latent associations between social groups and attributes-that could influence medical judgment. OBJECTIVE: To systematically evaluate the presence, magnitude, and behavioral impact of implicit biases in large language models within the medical domain across six high-stakes categories: gender, race, socioeconomic status, health conditions, religion, and healthcare systems. DESIGN: A descriptive cross-sectional study using a multi-faceted evaluation framework. SETTING(S): Computational analysis of 10 mainstream global large language models, including proprietary models (ChatGPT-4o, Gemini-2.0-Flash) and open-source models (DeepSeek-V3, Qwen3). METHODS: We constructed 24 medical bias datasets across six categories. Bias was assessed using three methods: (1) the Large Language Model Word Association Test, a prompt-based method for revealing implicit biases; (2) the Large Language Model Relative Decision Test, a strategy for detecting subtle discrimination in situational decision-making; (3) Paired-Prompt Analysis, used to examine whether implicit associations predict discriminatory decisions. RESULTS: All 10 models exhibited systematic implicit biases (Mean IAT Bias > 0) across all categories, with the strongest biases observed in Race (Mean = 0.61) and Socioeconomic Status (Mean = 0.56). Advanced reasoning capabilities (Chain-of-Thought) did not significantly reduce bias magnitude. Crucially, stronger implicit associations significantly predicted discriminatory choices in downstream medical decision tasks (p < 0.001). CONCLUSION: Current safety alignment techniques fail to eliminate implicit biases in large language models within the medical domain. These latent associations translate into biased decision-making, posing risks for health equity. Future development must prioritize representational debiasing over superficial alignment. Furthermore, healthcare professionals must embrace a stance of "AI vigilance": they should critically evaluate algorithmic outputs as fallible "second opinions" rather than objective truths, thereby ensuring that human judgment remains the ultimate safeguard for equitable patient care.
20 May 2026
Read appraisal →JMIR medical informatics
The Impact of Electronic Health Records on Family Physicians During Simulated Virtual Encounters: Exploratory Mixed Methods Study
BACKGROUND: This exploratory study investigated the impact of computer use on physician performance during clinical simulations. Standardized patient (SP) scenarios used in family practice certification examinations were adapted to include the use of the electronic health record (EHR). OBJECTIVE: The goal was to compare the impact of EHR use during simulated virtual patient encounters on resident physicians' and staff physicians' patient-centeredness (PC) and overall clinical performance, as well as to measure the cognitive load (CL) imposed by EHR use. METHODS: Sixteen participants each completed 2 video telemedicine simulations with SPs. One simulation case included limited past medical history for the SP in the EHR, while the other did not. Participants were instructed to completely document the encounter using the EHR. Participants' self-perceived CL was measured using the raw National Aeronautics and Space Administration Task Load Index (NASA-TLX). Video recordings were analyzed for participant PC and overall clinical performance. In addition to interacting with the EHR, multiple participants also conducted internet searches. The proportion of time that participants spent interacting with the computer, either using the EHR or searching the internet, was calculated. Inductive qualitative coding of a subset of video recordings (18 of 32 encounters) was performed, with a focus on signs of stress/CL. All videos were assessed for usability problems. RESULTS: Staff physicians (n=6) scored higher on PC compared to resident physicians (n=10) for both cases, though differences were not statistically significant after correction for multiple comparisons (family-wise error rate). Physicians' overall CL, as measured by the raw NASA-TLX, was not significantly correlated with computer use. Exploratory qualitative data analysis found both verbal and nonverbal signs of stress/CL due to computer use while interacting with the SPs. The proportion of time displaying nonverbal signs of stress/CL was calculated for a subset of participants (6 resident physicians and 3 staff physicians). Participant interpretations of instructions to completely document the encounter using the EHR varied widely. It is likely that participants' usual style of documenting, either primarily during or after patient encounters, impacted their use of the EHR while SPs were present. CONCLUSIONS: Use of the computer during video telemedicine appointments may negatively impact physician PC and overall clinical performance. Exploratory qualitative coding identified both verbal and nonverbal signs of stress/CL when participants interacted with the computer and the patient simultaneously. Increased clinical experience helped to mitigate the negative impact of computer use. If the use of the EHR is included in physician certification examinations, clear instructions regarding which tasks must be completed in the EHR during interactions with SPs should be provided.
20 May 2026
Read appraisal →Journal of medical Internet research
Understanding Remission of Long-Term Conditions Through Electronic Health Records: Scoping Review
BACKGROUND: Multiple long-term conditions (MLTCs) require complex and prolonged treatment regimens. Remission in long-term conditions (LTCs) is important for understanding disease progression and evaluating treatment effectiveness. Electronic health records (EHRs) are increasingly used to monitor clinical outcomes, but how remission is defined within EHRs remains unclear. OBJECTIVE: This study aimed to summarize and collate the previous literature on how remission of LTCs has been defined in EHRs. METHODS: Systematic electronic searches were performed on OVID MEDLINE, Embase, CINAHL EBSCO, the Cochrane Library, and the Bielefeld Academic Search Engine for eligible studies published from inception to November 27, 2025. Quantitative studies, published in any language, on adult populations, and using EHRs to assess remission of LTCs, were eligible for inclusion. Studies that did not clearly define remission and studies on cancer remission were excluded. Data were extracted from each eligible study using a structured table. Risk of bias was not assessed, in line with scoping review methodology. A narrative approach was taken to summarize and present data from the included studies. The number and characteristics of studies were described, both overall and by condition. Findings were discussed with clinicians and data experts to ensure applicability in clinical practice. RESULTS: Ninety-one studies were included. Sample sizes ranged from 12 to 72.9 million adults. Studies were conducted in 18 countries, with the majority being from the United States. The majority of included studies used a cohort study design. Studies assessed how remission was defined in 12 LTCs, including inflammatory bowel disease (41/91, 45.1%), type 2 diabetes (n=15, 16.5%), depression (n=15, 16.5%), alcohol or drug misuse (n=8, 8.8%), asthma (n=3, 3.3%), multiple sclerosis (n=3, 3.3%), epilepsy (n=1, 1.1%), anemia (n=1, 1.1%), chronic kidney disease (n=1, 1.1%), autoimmune pancreatitis (n=1, 1.1%), hypertension (n=1, 1.1%), heart failure (n=1, 1.1%), and MLTC (n=1, 1.1%). Remission was typically defined using a combination of clinical codes (n=7, 7.7%), validated rating scales (n=56, 61.5%), biochemical markers (n=29, 31.9%), absence of symptoms (n=10, 11%), absence of condition-specific events (eg, hospital admissions; n=4, 4.4%), and cessation of pharmacological treatments (n=26, 28.6%). There was substantial variation in the criteria and duration of follow-up used to define remission across studies. CONCLUSIONS: This review demonstrates that remission of LTCs can be identified and operationalized within EHRs, although remission criteria varied across studies. The review extends the literature on remission in EHRs by combining evidence synthesis and consultation with clinical and data experts to propose standardized comprehensive definitions to reliably define and implement remission of multiple LTCs in EHR-based research. This will allow cross-study comparisons and present an opportunity to advance understanding of disease trajectories and improve evaluation and monitoring of patient outcomes. Further research may apply, compare, and evaluate standardized definitions across different data sources to assess generalizability and further improve our understanding of remission of LTCs.
20 May 2026
Read appraisal →BMJ quality & safety
Patient safety measures for virtual consultations in primary care: a systematic review
OBJECTIVES: With the growing adoption of virtual consultations in primary care, the need for tailored metrics to evaluate their safety became increasingly urgent. This systematic review seeks to identify and review existing safety measures that could be used for safety evaluation of virtual consultations in primary care. METHODS: This has been conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and followed a published protocol. A systematic literature search was performed in Ovid MEDLINE/PubMed, Embase and Cochrane Library databases from 2014 to 2024. Studies comparing virtual consultations with face-to-face consultations in the primary care setting were included. An inductive thematic analysis was performed to systematically extract and group the safety measures into overarching themes, with a narrative synthesis to summarise the results. RESULTS: A total of 47 studies (31 experimental and 16 observational studies) were included (n=2 223 697 patients). All studies assessed the safety of virtual versus face-to-face consultations via one or both of the following domains: (1) factors that influence the safety of virtual consultations and (2) tangible outcomes of virtual care safety. The former were categorised into provider-related, patient-related and system-related factors. Tangible outcomes were evident through three subthemes-adverse events, health outcomes and patient perception of safety. CONCLUSIONS: This review provides a systematic synthesis of measures for the safety evaluation of virtual consultations. Further research into patient and physician perspectives is needed to identify aspects and indicators not captured in this study, followed by a consensus study to finalise safety metrics. Ultimately, having a robust methodology for safety evaluation of virtual consultations in place will enable safety monitoring, root cause analyses and safety improvement. PROSPERO REGISTRATION NUMBER: PROSPERO CRD42023464878.
20 May 2026
Read appraisal →The Journal of clinical endocrinology and metabolism
Predicting incident type 2 diabetes using wearable activity and polygenic risk: A survival-modeling study in All of Us
BACKGROUND: Evidence is limited on whether daily step counts are uniform across individuals or whether combining them with genetic risk improves prediction of type 2 diabetes (T2D), both essential for precision prevention. We aimed to assess whether objectively measured daily step count and Polygenic Risk Score (PRS) predict the incidence of T2D, and to evaluate the predictive performance of machine-learning models. METHODS: We conducted a prospective cohort study of 4589 adults in the National Institutes of Health's All of Us Research Program with valid Fitbit step data and whole-genome-derived PRS, excluding individuals with T2D before or within a 180-day lead-in period. Incident T2D was defined by HbA1c ≥ 6.5%, plasma glucose ≥126 mg/dL, or an All of Us T2D condition record. We evaluated Cox and machine-learning survival models. FINDINGS: Over a median of 2.92 years (15 340 person-years), 265 participants developed T2D (5.77% cumulative incidence; 17.27 per 1000 person-years). Risk-reducing thresholds were ∼7000 steps/day (P < .001) but differed by PRS group (∼7800 high vs ∼5800 low; P < .001). Each additional 1000 steps/day was associated with lower T2D risk (adjusted hazards ratio, 0.83; 95% CI, 0.79-0.88; P < .005), whereas each 1-SD higher PRS was associated with higher T2D risk (adjusted hazards ratio, 2.62; 2.32-2.96; P < .005). Adding steps to a clinical model increased the C-index from 0.748 to 0.774; adding PRS increased it to 0.867. Penalized Cox achieved the highest discrimination (C-index 0.859), followed by survival support vector machine (0.85) and classical Cox (0.846). Calibration was best with Random Survival Forests. INTERPRETATION: Step-count thresholds for reducing T2D risk varied across genetic risk groups, indicating that step counts do not confer uniform protection. Step counts and PRS provided independent, complementary predictive information, and their combination improved prediction of incident T2D.
20 May 2026
Read appraisal →BMJ open
Implementation of a clinical decision support tool for postpartum depression: protocol for a prospective randomised clinical trial
INTRODUCTION: Perinatal mood and anxiety disorders affect more than one in five pregnant individuals. Despite the large percentage of individuals impacted by mood disorders, they continue to remain underdiagnosed and undertreated. Recent interventions such as risk prediction modelling offer opportunities to predict patients at risk prior to symptom onset. Leveraging technology platforms such as electronic health records allow for timely diagnosis and intervention. To date, there are no established clinical decision support (CDS) tools. We hypothesise that subjects who use a CDS aid will find them acceptable, appropriate and feasible. METHODS AND ANALYSIS: We will conduct a pilot randomised clinical trial at three Weill Cornell Medicine clinics with randomisation at the clinician level. Patients in the control arm will receive usual care, while those in the intervention arm will receive CDS. The primary implementation outcomes are the acceptability, appropriateness and feasibility. The effectiveness outcomes are mental health service utilisation, EuroQol 5 Dimensions questionnaire (EQ-5D), Edinburgh Postnatal Depression Scale and social support measured by the perceived support scale. We hypothesise that stakeholders will find the CDS acceptable, and the intervention arm will have lower mental health service needs compared with control. ETHICS AND DISSEMINATION: This study has been approved by the Weill Cornell Medicine Internal Review Board. IRB Protocol# 23-07026254. TRIAL REGISTRATION NUMBER: NCT06818734.
20 May 2026
Read appraisal →PloS one
A novel hybrid framework integrating GA-driven 3D ResUNetGAN for MRI brain tumor segmentation
Accurate brain tumor segmentation by multi-modal MRI is crucial for diagnosis, treatment planning, and prognostic assessment. This work proposes a novel hybrid architecture within a GA optimized by 3D residual U-Net (ResUNet) with generative adversarial network (GAN) for the accurate segmentation of tumor subregions, namely the tumor core (TC), whole tumor (WT), and enhancing tumor (ET), using a dataset collected from BraTS2023 (437 training and 188 validation). By using GAs, we enable the computer to automatically find the best model structure and training settings, which helps keep the model simple enough to avoid overfitting while being powerful enough to generalise well to new data. The GAN part of our method trains the model to draw more realistic and consistent boundaries around tumor regions in MRI scans, thereby improving the overall quality of the segmentation. In our experiments, this GA-ResUNetGAN approach performed better than several leading methods, such as 3D UNet, DynUNet, UNETR, SwinUNETR, and the standard ResUNet, especially in accurately identifying TC, WT, and ET parts-achieving Dice scores of 0.94, 0.96, and 0.91 on the validation set. In addition, K-Fold cross-validation was performed to ensure consistent performance across different data splits. While the method involves more processing work than some baseline techniques, it provides improved segmentation quality and boundary delineation on the evaluated dataset. These results demonstrate the advantages of combining evolutionary optimization with adversarial deep learning for challenging medical imaging tasks.
19 May 2026
Read appraisal →JMIR infodemiology
Performance of Large Language Models in the Cognitive Analysis of Misinformation: Evaluation Study
BACKGROUND: Public discourse is significantly impacted by the rapid spread of misinformation on social media platforms. Human moderators, while capable of performing well, face many challenges due to scalability. While large language models (LLMs) show great potential across various language tasks, their capacity for cognitive and contextual analysis, in detecting and interpreting misinformation, remains less evaluated. OBJECTIVE: This study evaluates the effectiveness of LLMs in detecting and interpreting misinformation compared to human annotators, focusing on tasks requiring cognitive analysis and complex judgment. Additionally, we analyze the influence of different prompt engineering strategies on model performance and discuss ethical considerations for using LLMs in content moderation systems. METHODS: We evaluated 4 OpenAI models against a panel of human annotators using a subset of posts from the MuMiN dataset. Each model and human annotator responded to structured questions on misinformation, following an established cognitive framework. Both human annotators and LLMs also provided scores indicating how confident they were in their responses. Various prompting strategies were used in this research, including: 0-shot, few-shot, and chain-of-thought, with performance evaluated through precision, recall, F1-score, and accuracy. We used statistical tests, including the McNemar test, to quantitatively assess differences between LLM and human ratings of misinformation. RESULTS: GPT-4 Turbo with chain-of-thought prompting achieved the highest performance of all LLMs for detecting misinformation, with an accuracy of 67.2% and an F1-score of 78.3%, but was outperformed by human annotators, who achieved 70.1% accuracy and an F1-score of 81%. LLMs performed well in tasks involving logical reasoning and straightforward misinformation detection, but struggled with complex judgments, including detecting sarcasm, understanding misinformation, and analyzing user intent. LLM confidence scores positively correlated with accuracy in simpler tasks (r=0.72, P<.01) but were less reliable in subjective and complex contextual evaluations. CONCLUSIONS: LLMs show significant potential for automating misinformation detection. Their limitations in understanding and interpreting these posts highlight the current necessity of human oversight. A hybrid framework combining LLMs for preliminary screening with human moderators for more complex evaluation presents a promising future direction. Future research could prioritize the fine-tuning of LLMs using datasets that emphasize cognitive and emotional linguistic features, alongside the development of advanced prompting techniques.
19 May 2026
Read appraisal →JMIR mHealth and uHealth
Multimodal Personalized Mobile Health Just-in-Time Adaptive Intervention for Occupational Stress Management: Pilot Study
BACKGROUND: As digital health solutions gain traction, there is an urgent need for effective, person-centered stress management tools for employees. Advances in wearable stress monitoring and machine learning now enable the collection of high-resolution, real-time data and the delivery of personalized interventions with respect to both timing and content. Despite this technological progress, there remains a notable paucity of mobile health (mHealth) interventions that capitalize on these capabilities to implement just-in-time adaptive interventions (JITAIs). OBJECTIVE: This pilot study aimed to examine the preliminary effects of the RELAX mHealth app, which uses a JITAI framework to reduce occupational stress, with a particular focus on initial indications of stress reduction, the influence of the app's degree of personalization on these effects, and overall user experience. METHODS: A 2-arm randomized trial was conducted with 46 employees across two phases of 3 weeks each. Participants used the RELAX mHealth app, which adapts intervention timing based on real-time stress data and tailors content according to users' preferences. Stress was assessed via multiple heart rate variability (HRV) parameters measured with wearable sensors, alongside subjective questionnaires and ecological momentary assessment (EMA) questionnaires. Usability and user satisfaction were also evaluated. In the second phase, a group randomization was implemented, with two groups of different degrees of in-app personalization. RESULTS: The findings were mixed. While selected subjective stress indicators decreased from baseline to the end of the study, one physiological measure increased. Retrospective chronic stress (P=.048) and arousal related to the most recent stress event prior to EMA entry (P=.02) were reduced; however, the low frequency (LF)-to-high frequency (HF) ratio of HRV increased (P=.03). Other stress variables, such as work-related stress, valence of daily experiences, and momentary arousal, showed no change over the course of the study. No between-group differences were observed with respect to the assigned degree of personalization. Additionally, usability increased (P<.001), whereas satisfaction decreased at a trend level (P=.05). CONCLUSIONS: Although the results provide selective preliminary indications of stress reduction with app use, the variability of outcomes raises concerns about its current practical utility. The effects are also discussed with regard to the specific design of the prototype and the study setup. Technical issues and user discomfort were noted despite increasing usability over the study period, indicating areas for improvement in the developed prototype. Given the absence of a control group and the use of interventional arms only, no definitive conclusions about effects can be drawn. Nevertheless, this study highlights both the potential and the challenges of a personalized mHealth approach to occupational stress management. Further refinement of the app and more comprehensive evaluations are needed to fully understand the impact and functional usability of this JITAI-based approach in real-world settings.
19 May 2026
Read appraisal →PloS one
Facilitating the measurement and treatment of Behavioral and Psychological Symptoms of Dementia (BPSD) and understanding caregiver burden using wearable devices in Rural Taiwan-Protocol for a dyadic feasibility pilot study
INTRODUCTION: Alzheimer's disease (AD) prevalence rises with societal ageing. In clinical care, behavioral and psychological symptoms of dementia (BPSD)-including depression, agitation/aggression, apathy, and sleep disturbance-worsen patients' quality of life and substantially increase caregiver burden, more significantly than the cognitive symptoms. Standard BPSD assessments rely on caregiver-rated questionnaires that are cross-sectional and may be biased when caregivers are themselves older adults. Device-based measures (e.g., research-grade wrist actigraphy) can provide objective longitudinal data and novel features. In parallel, therapeutic wearables may improve sleep and mood in adults, and might improve BPSD if accepted by people living with dementia. METHODS: This dyadic pilot study will recruit 20 participants (n = 10 AD patients; n = 10 caregivers) from outpatient services and affiliated day-care/dementia hubs in rural Taiwan. Participants will wear Geneactiv continuously for 8 weeks and Re-Timer ≥30 min/day for 4 weeks. Device-based data will be processed with GGIR, a well validated R-package designed for processing accelerometer data. Questionnaire assessments include Pittsburgh Sleep Quality Index (PSQI), Neuropsychiatry Inventory Questionnaire (NPI-Q), Caregiver Burden Inventory (CBI), and a semi-structured interview based on the Taiwanese version of Quebec User Evaluation of Satisfaction with Assistive Technology (T-QUEST) at prespecified timepoints. DISCUSSION: Wearable devices may facilitate the measurement and treatment of specific BPSD, as well as reduce caregiver burden. If proven feasible even in rural Taiwan where both digital and health literacy and resources are limited, this model will inform how device-based dementia care model can be considered and applied in the context of global ageing.
19 May 2026
Read appraisal →Physiological measurement
EdgeECG: a lightweight edge-oriented network with dual criterion pruning for real-time ECG arrhythmia classification
Objective.Miniature electrocardiogram (ECG) devices can rapidly and accurately acquire real-time cardiac signals, enabling timely warnings for patients with heart disease. To achieve accurate arrhythmia classification on resource-constrained ECG edge devices, we propose EdgeECG, an ultra-lightweight neural network designed for deployment on low-power microcontrollers.Approach.EdgeECG is first designed with a compact convolutional architecture to ensure compatibility with resource-limited embedded platforms. A dual criterion pruning (DCP) strategy is then introduced to evaluate weight importance using both magnitude and median deviation for more precise model compression. In addition, quantization is applied to reduce storage cost and improve deployment efficiency on an STM32F103 microcontroller.Main results.Experimental results on the benchmark MIT-BIH arrhythmia dataset show that EdgeECG achieves an overall classification accuracy of 98.07%, outperforming several representative methods. In patient-independent record-level evaluation, the accuracy reaches 80.73%. The model contains only 2680 parameters and is successfully deployed on an STM32F103 microcontroller with 64 KB SRAM, achieving an inference latency of 0.025 s and an energy consumption of 0.156 mJ per inference. In addition, DCP reduces the number of non-zero parameters by nearly 50% while maintaining promising classification performance.Significance.EdgeECG provides an effective solution for five-class ECG arrhythmia classification on severely resource-constrained edge devices. Its compact architecture, effective pruning strategy, and successful deployment on an STM32F103 demonstrate its potential for low-power edge-based ECG analysis and wearable cardiac monitoring applications. The source code of EdgeECG is publicly available at:https://github.com/jyanhuang/EdgeECG-code.
19 May 2026
Read appraisal →Journal of medical Internet research
Efficacy of Digital Speech Therapy for Poststroke Dysarthria: Randomized Noninferiority Trial
BACKGROUND: Poststroke dysarthria, a common speech impairment, affects up to half of all stroke survivors, often reducing their ability to communicate, and adversely affecting their quality of life. Although conventional speech therapy for poststroke dysarthria is effective, access is often limited by time and geographical constraints. Here, digital speech therapy may serve as a remotely deliverable alternative for selected patients. However, few trials have assessed its efficacy, safety, and usability. OBJECTIVE: This study aimed to evaluate whether a smartphone-based speech therapy app is noninferior to conventional workbook-based therapy in improving speech intelligibility among individuals with poststroke dysarthria. METHODS: This single-blind, randomized controlled, noninferiority trial was performed at 3 hospitals in South Korea. Adults (≥19 y) with poststroke dysarthria who were cognitively intact, without aphasia, and able to use a smartphone were eligible. Participants were enrolled between July 20, 2023, and April 15, 2024. Participants were randomly assigned (1:1), stratified by stroke phase, using a block randomization method, to receive either a smartphone-based digital therapy app or a conventional workbook-based therapy for 4 weeks. The primary outcome was speech intelligibility (0-100 perceptual rating) after the intervention. Primary analysis was intention-to-treat using analysis of covariance. A noninferiority margin of 19 points was pre-defined. RESULTS: A total of 73 participants were enrolled (median age 62.00 years). Among them, 38 were assigned to the digital speech therapy group and 35 to the control group. Intelligibility scores improved from 80.48 (SD 18.92) to 92.08 (SD 12.38) in the intervention group, and from 80.94 (SD 16.74) to 88.11 (SD 18.06) in the control group. The adjusted between-group difference was 4.49 (95% CI 0.61-8.37), and the lower bound of the 95% CI was above the prespecified noninferiority margin (-19), which supported noninferiority. No significant between-group differences were observed in the secondary outcomes related to speech function or psychological status. The system usability score was 89.6, and adherence in the digital speech therapy group was 64.6% based on app logs, with no treatment-related adverse events. CONCLUSIONS: Digital speech therapy was noninferior to conventional workbook-based therapy in improving speech intelligibility and was feasible across acute to early subacute and chronic stroke phases in cognitively intact stroke survivors with predominantly mild-to-moderate dysarthria. However, feasibility and efficacy in older stroke survivors with cognitive deficits or co-occurring aphasia, or in those unable to use smartphones, remain to be established.
19 May 2026
Read appraisal →Scientific reports
RDE-DR: robust deep ensemble CNNs for automated diabetic retinopathy detection from fundus images
Diabetic retinopathy (DR) is a leading cause of preventable blindness, motivating the development of reliable automated screening systems. This work proposes a Robust Deep Ensemble for Diabetic Retinopathy detection (RDE-DR) by analyzing ensemble fusion strategies. Four pre-trained convolutional neural networks (ResNet50, VGG16, VGG19, and DenseNet121) are trained using CLAHE-enhanced APTOS 2019 fundus images and integrated through seven heterogeneous fusion mechanisms, including voting-based, rank-based, and fuzzy-integral-inspired strategies. A consistent evaluation protocol is adopted, incorporating threshold optimization and probabilistic calibration analysis to validate robustness, decision margins, and accuracy-precision trade-offs. Experimental results show that multiple fusion techniques achieve comparable high performance and stable behavior on the APTOS 2019 benchmark, with the best configuration reaching 98.64% accuracy, 98.40% precision, 98.92% recall, 98.66% F1-score, and 99.78% Area-Under-Curve (AUC). Beyond peak accuracy, the study provides insights into ensemble reliability, calibration characteristics, and practical design choices for medical image classification systems. These results show that integrating transfer learning with CLAHE preprocessing and ensemble fusion yields stable experimental performance on the APTOS 2019 benchmark, suggesting potential for future medical decision support.
18 May 2026
Read appraisal →Drug discoveries & therapeutics
Artificial intelligence (AI)-assisted ultrasound in clinical trials: Endpoint automation, decentralized monitoring, and regulatory readiness
Ultrasound is among the most widely used imaging modalities in clinical trials, and yet its dependence on operator skill and equipment settings has historically limited the reproducibility of ultrasound-based endpoints in multi-center studies. Artificial intelligence (AI) now addresses this limitation across two complementary dimensions: automated measurement algorithms that quantify cardiac function, organ volume, and vascular parameters with reproducibility approaching or, in select settings, exceeding that of trained human readers and real-time acquisition guidance systems that enable clinicians with no formal sonography training to perform diagnostic-level examinations, making remote and decentralized assessment increasingly feasible. This narrative review synthesizes current evidence and regulatory developments across three interconnected domains. First, use of automated ultrasound to ascertain endpoints has advanced from single-institution validation to prospective and randomized evidence, with deep learning measurement of the left ventricular ejection fraction demonstrating formal equivalence to expert readers across multiple echocardiographic parameters and AI-first workflows shortening the time to diagnosis in a blinded non-inferiority trial. Second, AI-guided ultrasound acquisition by nurses and other non-expert operators has achieved a high rate of diagnostic acceptability in cardiac and pulmonary ultrasound, laying the groundwork for use of ultrasound-based endpoints in decentralized clinical trial designs, as reflected in Food and Drug Administration (FDA) guidance on decentralized trial elements. Third, the regulatory frameworks governing AI-enabled medical devices - including the U.S. FDA's Predetermined Change Control Plan guidance, the EU Artificial Intelligence Act, and internationally harmonized good machine learning practice relevant to Japan and other jurisdictions - increasingly emphasize overlapping principles such as specification of prospective performance, post-marketing oversight, and transparent reporting. Addressing remaining challenges in domain generalization across vendors, subgroup fairness, and algorithm change management during ongoing trials will be essential for AI-assisted ultrasound to fulfill its potential as a robust, scalable endpoint in clinical research worldwide.
18 May 2026
Read appraisal →European journal of preventive cardiology
Targeting better hypertension control with the usage of the ESH smartphone application
AIMS: Uncontrolled blood pressure is a main issue in the management of hypertensive patients, while mobile health (mHealth) offers a new perspective. This study aimed to investigate the efficacy of an mHealth app-based intervention, the European Society of Hypertension (ESH) CARE, in the management of uncontrolled hypertensive patients. METHODS AND RESULTS: This was a two-arm randomized, controlled trial conducted among 107 hypertensive patients, with uncontrolled office (≥140/90 mmHg) and 24-h ambulatory blood pressure (≥130/80 mmHg). After antihypertensive drug treatment initiation or intensification, patients were 1:1 randomized to application assisted strategy (AAS) group or usual care group, with scheduled visits at 1 and 3 months. In ASS group there was a virtual visit, while in usual care group there was an office visit. At 6 months, there was a final visit assessment with office and ambulatory blood pressure (BP) measurement. The mean age of the total population (n = 107) was 52.9 ± 9.6 years, 50.5% were women, without any significant differences in the baseline characteristics. At 6 months, 50% in the usual care group and 78.2% in the AAS group (P = 0.002) achieved both systolic and diastolic ambulatory BP targets (<130/80 mmHg). Regarding office BP, there was a reduction of 19.9 ± 9.1/12.4 ± 7.7 mmHg in the usual care group and 25.1 ± 6.4/16.3 ± 6.3 mmHg in the AAS group, while regarding ambulatory BP, there was a reduction of 6.6 ± 9.2/3.3 ± 6.2 mmHg and 10.9 ± 9.1/6.4 ± 5.5 mmHg, respectively. CONCLUSION: Follow-up of hypertensive patients with the ESH Care App is feasible and contributes to a significantly better office and out-of-office BP control after 6 months. Uncontrolled blood pressure is a main issue in the management of hypertensive patients, while mobile health (mHealth) offers a new perspective. This study aimed to compare the efficacy of an mHealth app-based—ESH Care App—intervention to usual clinical care, in the management of uncontrolled hypertensive patients. Telemonitoring with the ESH Care App is associated with: Significantly improvement in office and ambulatory blood pressure control. Significantly greater reduction in office and ambulatory BP measurements.
18 May 2026
Read appraisal →World journal of urology
Comparative study on the efficacy of large language models (LLMs) in health management for patients with urological and andrological diseases: a multidimensional quality evaluation and applicability analysis
PURPOSE: This study aims to evaluate the efficacy of large language models (LLMs) in health management for urological and andrological conditions by comparing two Chinese LLMs: DeepSeek and Kimi. METHODS: The responses of DeepSeek and Kimi to 30 questions on six urological and andrological diseases were quantitatively assessed using quality assessment tools, expert assessments, and readability metrics. RESULTS: In quality assessments, DeepSeek outperformed Kimi in only two instances: responses to "What are the surgical options for kidney stones?" (PEMAT-AI: P = 0.012; DISCERN-AI: P = 0.024) and "What are the treatment modalities for bladder cancer?" (PEMAT-AI and DISCERN-AI: P < 0.001). Expert assessments revealed that DeepSeek demonstrated superior accuracy and safety compared to Kimi only in addressing "Dietary considerations for kidney stone patients" (accuracy: P < 0.001; safety: P = 0.02). No statistically significant differences were found in quality assessments or expert assessments between the models for disease-specific categories. However, Kimi outperformed DeepSeek in readability evaluations. CONCLUSION: Both DeepSeek and Kimi exhibit substantial efficacy and clinical applicability in managing urological and andrological diseases. To enhance their practical use, Kimi requires improvements in accuracy and safety, while DeepSeek should focus on improving readability.
18 May 2026
Read appraisal →Scandinavian journal of primary health care
Shoulder-to-shoulder: healthcare professionals' experiences of learning during electronic health record implementation in municipal primary care.
BACKGROUND: Formal training is commonly used in electronic health record (EHR) implementation, but often fails to address healthcare professionals' ongoing, context-specific learning needs. There is limited knowledge of how learning unfolds in practice when such training is experienced as insufficient, particularly in municipal healthcare services. METHODS: This qualitative study examined learning processes during the implementation of a new EHR system in a municipal healthcare institution in Central Norway. Data was generated through interviews and observation, and was analyzed thematically. RESULTS: Participants experienced formal training as poorly aligned with their digital competencies and everyday clinical work. Limited time and staffing further constrained participation in formal training and follow-up. Alongside formal training, employees engaged in peer-based learning practices embedded in everyday work, described as 'shoulder-to-shoulder' learning. These practices became particularly important due to limited time and staffing, enabling rapid problem-solving and mutual support, while also reinforcing shared responsibility for learning. Rather than merely compensating for inadequate training, shoulder-to-shoulder learning emerged as an important adaptive practice that supported problem-solving and collaboration during EHR implementation. CONCLUSION: Learning to use EHR systems in municipal primary care should be understood as a socially embedded and ongoing process. While formal training may provide initial orientation, implementation strategies should also recognize and support peer-based learning practices that support ongoing learning and collaboration during periods of organizational change.
18 May 2026
Read appraisal →Computers in biology and medicine
A unified multimodal framework for chest X-ray retrieval and disease prediction for clinical decision support
Recent advances in medical imaging and natural language processing enable new opportunities for automated diagnostic support. Chest X-rays (CXRs) remain the most common imaging modality for screening pulmonary, cardiovascular, and systemic diseases; however, the growing volume of studies and free-text reports can overwhelm clinicians. We propose a unified multimodal retrieval and prediction framework that jointly leverages DICOM-format CXRs and radiology reports by projecting visual and textual features into a shared semantic space. Trained with contrastive and multi-label objectives, the system supports disease classification, case-based retrieval, and explainable AI. Experiments on the Open-I dataset demonstrate strong performance, achieving a Macro AUROC of 0.95 and Macro F1-score of 0.71 across 22 diagnostic categories. The retrieval module attains high ranking quality (MRR and nDCG >0.93) with sub-millisecond query latency. Quantitative explainability analysis further shows strong agreement between attention- and gradient-based attribution maps (Pearson ρ≈0.92), supporting trustworthy clinical decision-making.
17 May 2026
Read appraisal →JMIR mHealth and uHealth
WhatsApp-Supported Teledentistry to Reinforce Oral Health Promotion Among Older Adults Residing in Rural and Urban Areas: Randomized Controlled Trial
BACKGROUND: Access to oral health promotion for older adults is globally limited, especially in rural, low- and middle-income settings. Digital research often lacks theoretical foundation and focuses primarily on younger cohorts, yielding few randomized trials evaluating accessible tools for oral health education in older adults. OBJECTIVE: This study aimed to develop a telehealth reinforcement strategy for oral health promotion to improve knowledge, attitudes, and self-efficacy in community-dwelling older adults. METHODS: A single-center, parallel-group randomized controlled trial was conducted in 4 municipalities (2 urban and 2 rural) in La Araucanía, Chile. Eligible participants were functionally independent adults aged ≥60 years with smartphone and internet access; those with cognitive impairment, complete edentulism, or inability to use WhatsApp were excluded. Participants were recruited from regional databases and assessed using the Geriatric Dental Specialties Tele-platform, a teledentistry tool for older adults. Participants were randomized (1:1) to face-to-face instruction (comparator) or the same instruction plus 2 weeks of social cognitive theory-informed telehealth reinforcement (4 validated videos via WhatsApp). Clinicians and statistical advisors were blinded. Primary outcomes (oral health knowledge, attitudes, and self-efficacy) were measured via telephone-administered questionnaires at baseline and 6 weeks post intervention. Secondary outcomes included acceptability and self-reported behaviors. Analyses included hypothesis testing, multiple correspondence analysis, and k-means clustering. RESULTS: A total of 120 older adults were randomized (comparator: n=59; telehealth: n=61), with 103 analyzed (comparator: n=51; telehealth: n=52). Both groups showed substantial within-group improvements in oral health knowledge (comparator: Cohen d=0.93, 95% CI 0.52-1.34; P<.001; telehealth: Cohen d=1.07, 95% CI 0.66-1.48; P<.001) and self-efficacy (comparator: r=0.59, 95% CI 0.38-0.74; P<.001; telehealth: r=0.62, 95% CI 0.43-0.77; P<.001). In per-protocol analysis, telehealth improved dental caries knowledge (P=.03) and attitudes (P=.004), with no between-group differences in other domains (P>.05). In intention-to-treat analysis, telehealth showed a significant between-group difference for attitudes only (adjusted mean difference=0.91, 95% CI 0.34-1.48; P=.002), with no differences for overall oral health knowledge (P=.11) or self-efficacy (P=.59). Exploratory analyses indicated only the rural telehealth subgroup showed significant gains in attitudes (P=.003) and flossing (P<.001). Clustering suggested greater improvements among participants with higher baseline needs, predominantly rural, with fewer teeth. Telehealth demonstrated acceptability across multiple indicators (>80% for most measures) with no clinical adverse events; minor video-access issues occurred. CONCLUSIONS: Telehealth reinforcement provided significant advantages in oral health attitudes compared with face-to-face instruction. The intervention was acceptable and showed benefits among older adults with higher preventive needs, commonly seen in rural settings. By integrating theory-informed strategies into a familiar digital platform, this study adds evidence from rural and urban contexts, extending prior work on mobile oral health. It offers insights to address service gaps in underserved areas and highlights potential for feasible, context-aligned implementation. Future research should evaluate long-term effects, adaptability, and cost-effectiveness. TRIAL REGISTRATION: ClinicalTrials.gov NCT05917548; https://clinicaltrials.gov/study/NCT05917548.
17 May 2026
Read appraisal →JMIR medical informatics
Multimodal Fusion of Echocardiogram Images and Electronic Medical Records for Heart Disease Screening: Retrospective Algorithm Development and Validation Study
BACKGROUND: Echocardiography is a fundamental imaging modality for the diagnosis of heart disease (HD), but its interpretation remains operator-dependent and lacks standardized, data-driven decision support. Although artificial intelligence has improved image-based diagnosis, the added value and interpretability of integrating routinely collected electronic medical records (EMRs) with echocardiogram (ECHO) for large-scale screening remain underexplored. OBJECTIVE: This study aims to develop an explainable artificial intelligence framework that integrates multimodal data, including ECHO images and EMRs, with derived temporal and clinical features, to enhance the screening and interpretability of heart disease diagnosis. METHODS: In this retrospective single-center study, we analyzed 26,936 patients with linked ECHO and EMR records (1470 with clinically confirmed HD and 25,466 without). We constructed cross-sectional EMR features (demographics, comorbidities, and registration history) and extracted view-specific image features with a simplified Inception-v3 backbone. Five modality-specific feature vectors (4 imaging views + EMR) were concatenated and input to an extreme gradient boosting classifier within a patient-level stratified 5-fold cross-validation framework. To mitigate class imbalance during training, non-HD cases in the training folds were randomly downsampled to a 1:3 HD:non-HD ratio; held-out test folds preserved the original prevalence. Post hoc explainability was provided via Grad-CAM heatmaps for images and Shapley additive explanations analysis for EMR features. RESULTS: For the primary binary task (presence vs absence of HD), the multimodal model achieved an AUC of 0.8147 (SD 0.009) on held-out test folds, compared with 0.7785 (SD 0.014) for the image-only baseline, and 0.7343 (SD 0.009) for the EMR-only baseline. At the clinically selected decision threshold, sensitivity, specificity, positive predictive value, and negative predictive value were 84.6% (SD 1.6%), 72.4% (SD 2%), 15% (SD 2.3%), 98.8% (SD 0.3%) for the multimodal model, versus 82.3% (SD 1.9%), 73.5% (SD 2%), 15.2% (SD 2.1%), 98.6% (SD 0.3%) (image-only), and 77.5% (SD 2.1%), 67% (SD 2.3%), 11.9% (SD 1.7%), 98.1% (SD 0.4%) (EMR-only). Grad-CAM visualizations qualitatively highlighted anatomically and physiologically meaningful regions (eg, valvular structures and abnormal flow jets), while Shapley additive explanations analysis identified age, registration years, sex, and hypertension among the top EMR contributors-findings that align with established cardiovascular risk factors. CONCLUSIONS: Integrating echocardiographic images and EMR data in an explainable multimodal framework yields improved and clinically plausible screening performance for heart disease. Future work should focus on external multicenter validation, quantitative assessment of visual explanations against expert annotations, and prospective studies assessing clinical use and workflow integration.
17 May 2026
Read appraisal →PloS one
Design and evaluation of a resilient IBN architecture: Integrating post-quantum cryptography with adaptive threat detection using machine learning
As the domain of network security keeps on evolving rapidly, especially in sensitive areas such as healthcare systems, the demand for reliable device verification, controlling access, and spotting threats is growing sharply. This paper presents the design, implementation, and systematic evaluation of an improved Intent-Based Networking (IBN) system that integrates post-quantum cryptography, certificate-based identity management, and machine learning-based anomaly detection within a unified framework. The system incorporates SPHINCS+ post-quantum digital signatures for quantum-resilient authentication, X.509 certificate lifecycle management for establishing device trust, and hardware-aware cryptographic operations to maintain efficiency. It further enforces fine-grained access policies using Role-Based Access Control (RBAC) augmented with Multi-Factor Authentication (MFA), ensuring strong access governance across network segments. For early threat detection, machine learning models such as Isolation Forest and MiniBatch KMeans are employed to learn communication patterns and detect anomalous device behavior. Additionally, event logs are maintained using asynchronous, hash-chained logging mechanisms inspired by blockchain principles, ensuring auditability and data integrity. To address evaluation transparency and rigor, the framework is assessed using a controlled prototype testbed with explicitly defined traffic features and reproducible experimental settings. The evaluation considers cryptographic correctness, access control performance, anomaly detection capability, and scalability under increasing workloads. Experimental results demonstrate 100% success in post-quantum signature generation and verification, effective anomaly detection with no observed false negatives in the evaluated scenarios, and stable log-processing throughput as the number of events grows. Importantly, this work does not claim novelty in individual components, but contributes through the system-level integration and empirical evaluation of a quantum-safe, ML-assisted IBN security architecture. The findings highlight key trade-offs between security enforcement and usability, while also identifying limitations such as certificate expiry handling gaps, conservative policy behavior, and lack of large-scale statistical validation. These observations establish a reproducible baseline and motivate future work toward statistically rigorous validation, real-world deployment, and adaptive policy optimization.
17 May 2026
Read appraisal →PloS one
Multi class photoplethysmography-based deep model for cardiovascular disease classification
BACKGROUND: cardiovascular disease is the leading global cause of mortality. Photoplethysmography (PPG), widely embedded in consumer wearables, offers a scalable diagnostic modality. However, prior approaches are often constrained by handcrafted features, binary classification, and poor generalizability, limiting their clinical impact. METHODS: A deep hierarchical convolutional neural network (CNN) was designed to extract both morphological and rhythmic characteristics directly from raw photoplethysmography (PPG) signals. The architecture employs progressively structured convolutional filter hierarchies to capture multi-scale signal features. To enhance signal stability and training efficiency, a dual-stage normalization strategy was implemented, consisting of Z-score standardization followed by Min-Max scaling. In addition, batch normalization and dropout regularization were incorporated to improve model generalization and reduce the risk of overfitting. The proposed framework was trained and evaluated on a multi-source dataset comprising 612 patients and 2,448 annotated PPG segments distributed across six diagnostic classes: atrial fibrillation (AF), heart failure (HF), acute coronary syndrome (ACS), cerebral vascular accident (CVA), deep vein thrombosis (DVT), and normal sinus rhythm (NSR). RESULTS: The model achieved an overall accuracy of 93.48%, a macro-average F1-score of 0.9386, and a Cohen's Kappa of 0.8968, indicating "almost perfect" agreement. AF and HF were detected with flawless precision and recall (1.000), while ACS achieved high sensitivity (recall 0.964). Errors were primarily confined to physiologically related conditions (e.g., ACS vs. CVA). Inference efficiency was demonstrated with <5 ms per segment on consumer-grade hardware, confirming feasibility for real-time applications. CONCLUSION: The proposed framework advances beyond lightweight but underpowered or overly complex models by combining representational depth with computational efficiency. Limitations include the need for external, multi-center validation and explainability integration. This study establishes a robust foundation for PPG-based, multi-class cardiovascular diagnostics, supporting clinical decision support and next-generation wearable health technologies.
17 May 2026
Read appraisal →Journal of medical Internet research
Digital Patient Decision Aid for Antiobesity Medications: Mixed Methods Study of Human-Centered Design and Usability Evaluation
BACKGROUND: The global burden of obesity continues to rise, highlighting the need for patient-centered approaches to weight management. Shared decision-making is particularly important in the selection of antiobesity medications (AOMs), as treatment options differ in mechanism, effectiveness, side effects, routes of administration, and cost. Despite this preference-sensitive context, only a few patient decision aids (PDAs) have been culturally and clinically adapted for use in Asian populations. OBJECTIVE: This study aims to design, develop, and evaluate a digital PDA, OptiWeight, to support shared decision-making for AOM selection, incorporating perspectives from health care professionals and patients. METHODS: This mixed methods, multicenter study, conducted between August 2022 and November 2025, applied a 4-stage human-centered design process. An evidence-informed prototype was developed based on clinical guidelines, followed by 2 rounds of usability testing using think-aloud protocols to assess navigation structures, perceived usability (System Usability Scale [SUS]), and cognitive workload (NASA Task Load Index [NASA-TLX]). Semistructured interviews with health care professionals specializing in weight management, guided by the Consolidated Framework for Implementation Research, informed clinical implementation and workflow integration. Finally, patients with overweight or obesity evaluated usability, cognitive workload, and overall user experience in outpatient settings. Qualitative data were analyzed using content analysis, and 1-way analysis of variance examined changes in usability and workload across stages. RESULTS: A total of 174 individuals were included across all study stages (usability testing among adults: n=78; health care professional interviews: n=18; and clinical evaluation among patients: n=78). Iterative usability testing comparing system- and user-controlled navigation structures revealed complementary strengths and limitations, leading to the adoption of a hybrid navigation structure supporting both sequential guidance and flexible comparison. Additional design requirements included the use of icon arrays to enhance risk comprehension and localization features such as treatment cost displays and clarification of socially impactful side effects. Perceived usability increased from initial testing to clinical evaluation (SUS: 60.53-73.65, P<.001), meeting good usability thresholds, while cognitive workload decreased (NASA-TLX: 40.35-16.69, P<.001). CONCLUSIONS: Through a systematic human-centered design process integrating health care professional and patient perspectives, OptiWeight addresses the lack of culturally adapted PDAs for AOM decision-making in Mandarin-speaking populations while capturing user needs-particularly regarding navigation flexibility and risk visualization. The final tool demonstrated good usability and feasibility, and workflow considerations suggest potential for integration into routine weight-management care. Further research is needed to evaluate its impact on decision quality and real-world implementation outcomes.
17 May 2026
Read appraisal →JMIR medical informatics
A Multiassessment and Multiprofessional Agents Approach for Medical Chatbot Risk Estimation: Development and Evaluation Study
BACKGROUND: Assessing chatbot responses across 3 domains-medical, ethical, and legal-is essential to ensuring the safe use of artificial intelligence in health care. Although advancements in the use of large language models (LLMs) show significant improvements in evaluating question-answer datasets, such as multiple-choice medical exams, existing systems use general LLMs without incorporating specialized domain knowledge. They rely on standardized instructions without integrating real-world information, and ensemble methods such as majority voting fail to resolve disagreements among agents, resulting in misclassification and challenges in risk assessment. OBJECTIVE: This study aims to design, develop, and evaluate a synergistic approach for assessing risks associated with chatbot responses using multiassessment (MA) and multiprofessional agents (MPAs). METHODS: We designed and developed an approach consisting of MA and MPA, specifically initial assessment (MA1), which internalizes 3 roles and provides an initial risk estimation, and final assessment (MA3), which aims to reach a final consensus based on the previous assessments (MA1 and MA2), with each using 1 LLM. The verification assessment (MA2) incorporates an MPA or role-based LLM specialized agents for each risk domain (medical, ethical, and legal). We evaluated the proposed approach using the MedNLP-CHAT (Medical Natural Language Processing for AI Chat) corpus (N=226; 100 train, 126 test), covering baseline, enhanced prompt, embedding-based search, and retrieval-augmented generation (RAG). Primary metrics included macro F1-score and joint accuracy to evaluate system performance, along with CI and paired macro F1-score difference (Δ) as supporting metrics to assess the approach's effectiveness. RESULTS: The MA-MPA framework integrated with RAG achieved the highest average macro F1-score of 0.800 across risk domains and a joint accuracy of 76 (60.3%) correct predictions across all risk domains out of 126 question-answer pairs, with notable improvements over the best reported Eighteenth NII Testbeds and Community for Information Access Research Project (NTCIR-18) MedNLP-CHAT systems in the ethical (+0.252) and legal (+0.096) risk domains, while the medical domain showed a modest increase of +0.070. The MA approach contributed the largest gains, particularly from MA1 to MA2, with paired macro F1-score gains ranging from +0.176 to +0.214 across systems. The MPA approach performed better when integrated with MA and external knowledge, with paired bootstrap estimates showing a gain of +0.037 (95% CI 0.003-0.074) over baseline; however, joint accuracy gains were not evident (95% CI -2.9% to 7.7%), and gains relative to the enhanced prompt were small. Notably, MA alone achieved higher joint accuracy than RAG (62.7% vs 60.3%), indicating a metric-specific trade-off rather than consistent superiority across all metrics. CONCLUSIONS: The MA-MPA approach shows potential for improving risk estimation in chatbot responses. The results suggest that the framework is particularly useful for enhancing balanced overall performance, especially when combined with external knowledge, although the medical risk domain remains challenging. Furthermore, more specialized LLMs may further improve contextually grounded risk estimation.
17 May 2026
Read appraisal →JMIR mental health
Factors Influencing the Initiation and Continued Engagement of Digital Mental Health Tools Among Adults: Theory of Planned Behavior-Informed Systematic Review
BACKGROUND: Digital mental health tools (DMHTs) offer scalable support, but engagement varies. Understanding the shapes of initiation and ongoing use is essential for effective design and implementation. OBJECTIVE: This study aims to synthesize determinants of adults' initiation and engagement with DMHTs, organized through two lenses: (1) psychological factors aligned with the theory of planned behavior (TPB) and (2) design and access features. METHODS: A systematic search of 9 databases (June 2025) identified qualitative and mixed methods primary studies reporting end-users' experiences with DMHTs. Studies were screened and reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Quality appraisal used quality assessment with diverse studies (QuADS). Data were synthesized using a framework-guided thematic approach, mapping findings to TPB constructs and complementary design and access domains. RESULTS: A total of 22 studies met inclusion criteria. Findings clustered into 2 interdependent domains. TPB constructs explained how beliefs, social expectations, and perceived control shaped decisions to start and persist with DMHTs. Design and access features frequently acted through these same pathways, especially by altering perceived behavioral control (PBC), with cost, connectivity, device constraints, and time flexibility affecting feasibility, with content design and privacy shaping perceived value and trust. Perceived fit (goals, cultural or linguistic relevance, and routine alignment) consistently influenced both initiation and continuation. Several features operated bidirectionally; depending on context, the same feature could facilitate or hinder engagement. CONCLUSIONS: Engagement with DMHTs is jointly determined by users' beliefs and the design and access conditions within which tools are offered. Implementation should pursue a dual strategy, strengthening willingness to seek support (addressing attitudes, norms, and perceived control) while engineering low-effort, trustworthy, and context-appropriate experiences. Priorities include equity-focused policies (data costs, devices, and connectivity), transparent data practices, co-design with diverse communities, and consistent, theory-informed outcome measures.
17 May 2026
Read appraisal →Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention
Large language models in public health: opportunity or threat? The case of button battery injuries.
BACKGROUND: Button battery (BB) injuries in children represent a severe and growing public health burden. The literature on the topic is extensive; however, there is a notable lack of structured public health initiatives addressing the problem. The present study aimed to test the feasibility of using large language models (LLMs) to draft recommendations for preventing and managing BB ingestion in children. METHODS: A set of questions was generated and submitted to ChatGPT-4o and ChatGPT-o1-preview. Questions were based on statements and websites of scientific societies and not-for-profit organisations and were developed to produce comprehensive recommendations that provided information on BB risks, primary and secondary prevention, clinical management and follow-up, and general public health initiatives. Two independent reviewers rated the accuracy and readability of the questions submitted to the LLMs. The accuracy was rated using a four-level scale, while the readability was assessed using two established readability tools, the Flesch Reading Ease (FRE) and the Flesch-Kincaid Grade Level (FKGL). RESULTS: None of the answers provided by the LLMs were rated as completely incorrect or partially incorrect. ChatGPT-o1-preview outperformed ChatGPT-4o in accuracy, with eight answers rated as accurate and complete. Both models showed similar readability levels, with high FKGL and FRE scores indicating college-level comprehension. DISCUSSION: LLM demonstrated a strong performance in this study, with no responses rated as incorrect or partially incorrect, showing its great potential and feasibility for use in public health. CONCLUSIONS: The present findings suggested the potential feasibility of LLMs in public health for preventing paediatric injuries.
17 May 2026
Read appraisal →JMIR human factors
A Chronic Pain Self-Management Mobile App (Dolodoc): Cross-Sectional Acceptability Study
BACKGROUND: Approximately 19% of adults in Europe are affected by chronic pain, which reduces the quality of life. Pain-management mobile health (mHealth) apps offer a promising solution for self-management, but user engagement and adherence can limit their clinical impact. User experience design and research play a crucial role in optimizing usability and long-term adoption of digital health interventions. OBJECTIVE: This study aims to evaluate the user experience of Dolodoc, a mobile app for chronic pain self-management, using a mixed methods approach that assesses acceptability through a content quality survey and examines use by analyzing overall use patterns. METHODS: A cross-sectional acceptability study of the main content of Dolodoc was conducted among patients with chronic pain recruited from the Geneva University Hospitals pain center and through snowball sampling. Participants rated 84 evidence-based self-management strategies by using a 5-point Likert scale based on 5 acceptability criteria: understandability, motivation, feasibility, relevance, and alignment with the related quality-of-life dimension. To reduce participant fatigue and avoid random responses, the 84 strategies were randomly divided across survey versions. Use was assessed through metrics collected over 6 months with Piwik PRO Analytics to observe real-world use behaviors among Dolodoc users. RESULTS: In the acceptability study, 33 participants rated the self-management strategies positively across all dimensions. On a scale from -2 to 2, the strategies were well understood (mean 1.47, SD 0.76), motivational (mean 1.12, SD 0.96), feasible (mean 1.01, SD 1.05), relevant (mean 0.99, SD 1.09), and aligned with the dimensions (mean 1.33, SD 0.89). The use study demonstrated that 60% (486/802) of the patients used Dolodoc only once, indicating that long-term adherence remains a challenge. Within Dolodoc, pain tracking, useful links, and medication logging were the most actively used features. CONCLUSIONS: This study highlights the gap between acceptability and long-term adherence to mHealth solutions. Improving personalization and accessibility could increase user engagement and long-term adherence. Future iterations of the app should incorporate tailored interventions and real-time feedback mechanisms. In addition, leveraging a digital navigation follow-up could facilitate user adoption and sustained engagement.
17 May 2026
Read appraisal →JMIR mHealth and uHealth
Smartphone Apps for Cannabis Cessation: Quality Assessment and Content Analysis
BACKGROUND: Over the past 2 decades, global rates of cannabis use have risen significantly, especially among young adults. This has corresponded to an increase in cannabis-related problems and hospitalizations. Thus, there has been significant interest in developing new interventions that can help facilitate cannabis cessation and reduce hospitalization rates. Specifically, mobile apps have emerged as scalable and accessible stand-alone or adjunct interventions that can help individuals with cannabis use disorders. OBJECTIVE: This study aimed to evaluate the quality of free cannabis cessation apps available on both the Apple App Store and Google Play Store, focusing on the analysis of their features, content, and adherence to evidence-based practices. METHODS: A systematic search was conducted in April 2023 using a variety of keywords. The apps were deemed eligible if they were free, available in English, accessible on both the Apple App Store and the Google Play Store, and related to cannabis cessation. Eligible apps were used for at least 1 month and were rated on the Mobile App Rating Scale by 2 reviewers. Interrater reliability was excellent, with a weighted Cohen κ of 0.893 (95% CI 0.835-0.943). RESULTS: Four apps were included in the analysis, namely, "Grounded-Quit Weed," "Quit Weed," "Marijuana Addiction Calendar," and "Marijuana Anonymous." The mean overall quality score of the apps was 3.4 out of 5, indicating poor to acceptable quality. The apps scored the highest on the "functionality" section and the lowest on the "information" section. Of the 4 apps, 3 focused on tracking cannabis use and duration of abstinence, whereas 1 focused on peer support. A limited number of cannabis cessation apps were identified, and those that were available were of low quality due to a lack of evidence-based information. CONCLUSIONS: This study is the first to evaluate the current availability and quality of mobile apps designed for cannabis cessation. Unlike previous research that broadly assessed cannabis-related mobile apps, this study focuses on the limited number of free cannabis cessation tools, reflecting what is most available to the general population. The findings highlight a significant gap between the growing demand for virtual cessation tools and the quality of existing options. With the rising global prevalence of cannabis use disorders, there is an increasing need for robust, accessible, and evidence-based therapeutic options. While mobile health apps may be a viable option to support cannabis cessation, the current landscape is limited by poor quality apps and a lack of evidence-based information. From a real-world perspective, this study highlights the need for users to exercise caution when relying on current cannabis cessation apps and underscores the urgent need for the development and evaluation of new evidence-based digital interventions.
17 May 2026
Read appraisal →European journal of preventive cardiology
Cuffless Blood Pressure Monitoring Devices: Technical Foundations and Clinical Implications
Cuffless blood pressure (BP) monitoring devices represent a promising innovation in hypertension management. This scientific statement provides a comprehensive update on these emerging technologies, their specific validation requirements, their potential clinical applications, and their present and future challenges. These devices generate considerable interest by enabling non-invasive BP measurement without arterial occlusion, thereby eliminating the discomfort associated with traditional cuff-based monitoring, particularly during sleep. The technologies on which these devices are based comprise a heterogeneous group, primarily utilizing pulse wave propagation time or waveform analysis through contact or non-contact sensors. They can be categorized as continuous or intermittent, automated or manual, calibration-free or requiring cuff/demographic calibration, and wearable or stationary. This technological diversity necessitates validation protocols distinct from those used for conventional cuff-based monitors, with specific requirements for each device category. Potential clinical applications include widespread out-of-office BP monitoring, unbiased assessment of circadian BP patterns and BP variability, improved detection of nocturnal hypertension, enhanced treatment adherence and long-term BP control, and continuous monitoring in hospital settings. Additionally, their lower cost compared with conventional technologies could enhance the early detection of hypertension in resource-limited settings. However, due to insufficient accuracy validation, this scientific statement does not recommend their use in clinical decisions in spite of their potential interest, in line with international guidelines not recommending their use in hypertension management. Key challenges ahead include developing standardized validation protocols, establishing normative BP data, managing the resulting burden on clinicians in handling huge volumes of data, exploring additional haemodynamic parameters, and advancing sensor technology, mathematical models, and algorithms.
16 May 2026
Read appraisal →PloS one
Deep learning-based bimodal speech and facial expression recognition of miners' unsafe emotions
Under the influence of unsafe emotions, miners' ability to perceive risks is hindered, which can easily lead to decision-making errors and safety accidents. To recognize unsafe emotions exhibited by miners during operations, this study proposes a deep learning-based bimodal framework that integrates speech and facial expression features. A convolutional neural network (CNN) combined with a bidirectional long short-term memory (Bi-LSTM) network is employed to model local spectral patterns and temporal dependencies in speech signals, and ShuffleNet-V2 is used to capture deep facial features. In addition, three feature enhancement strategies are proposed to improve the generalization ability of the model. By constructing a dataset containing five categories of miners' unsafe emotions for network training, the model achieves a mean recognition accuracy of 85.56%. Furthermore, we conducted a preliminary field test of the bimodal model in a real mining environment. The results provide preliminary evidence of its potential applicability in real-world mining conditions.
16 May 2026
Read appraisal →JMIR mHealth and uHealth
Engagement of Users in Digital Health Applications: Scoping Review
BACKGROUND: Mobile health (mHealth) uses mobile technology as a tool for prevention and health promotion. Research indicates that user engagement is crucial for effective mHealth interventions and improved health outcomes. However, many studies report low adoption rates, rapid decline after initial use, and a lack of acknowledgment of user implications in achieving outcomes. Thus, conceptualizing participation in mHealth is essential to identify key determinants for engaging users. OBJECTIVE: This scoping review aims to identify and characterize the attributes and definitions of user engagement in mHealth, examine engagement methods, and analyze barriers and facilitators influencing participation. METHODS: Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, Scopus, Web of Science, and PubMed databases were searched for publications between 2000 and 2025 with a 2-stage selection process. RESULTS: Out of 2489 articles identified, 1416 were screened, and 52 met the inclusion criteria. Half were recently published in the last 5 years (2020-2025). Existing literature focused on digital interventions for specific populations and health topics. Analysis revealed four main perspectives on engagement in mHealth: (1) usage metrics, (2) subjective user experiences, (3) a hybrid approach that combines both, and (4) a goal-oriented perspective (behavior change or health outcomes). CONCLUSIONS: To understand the complexity and multifactorial nature of participation, it is relevant to conceptualize it as a dynamic mechanism enabling users to achieve their objectives. Both quantitative use and subjective user experience should be integrated to reach the optimal intervention dose. Recognizing users' evolving needs, uniqueness, and their socioenvironmental context interdependence, it is essential to involve users in all stages (design, implementation, and iterative evaluation of mHealth). Findings will inform an e-Delphi study to establish consensus on engagement criteria.
16 May 2026
Read appraisal →European heart journal
Foundation models for electrocardiogram interpretation: clinical implications.
BACKGROUND AND AIMS: The 12-lead electrocardiogram (ECG) remains a cornerstone of cardiac diagnostics, yet existing artificial intelligence (AI) solutions for automated interpretation often lack generalizability, remain closed source, and are primarily trained using supervised learning (SL), which requires extensive labelled datasets and may limit adaptability across diverse clinical settings. Self-supervised learning (SSL) can potentially overcome these limitations by learning robust representations from unlabelled data. To address these challenges, this study developed and compared two open-source foundational ECG models: DeepECG-SL, a supervised multilabel ECG model, and DeepECG-SSL, a self-supervised model. METHODS: Both models were trained on over 1 million ECGs using a standardized preprocessing pipeline and automated free-text extraction from ECG reports to predict 77 cardiac conditions. DeepECG-SSL leveraged unlabelled data through self-supervised contrastive learning and masked lead modelling before fine-tuning for downstream tasks, while DeepECG-SL was trained directly on labelled diagnostic data in an end-to-end fashion. Performance was evaluated across seven private, multilingual healthcare systems and four public ECG repositories, with assessment of fairness by age and sex, and investigation of privacy vulnerabilities as well as memory and compute requirements. RESULTS: DeepECG-SSL achieved micro-averaged area under the receiver operating characteristic curves (AUROCs) across all 77 cardiac conditions for ECG interpretation of 0.990 [95% confidence interval (CI): 0.990, 0.990] on the internal dataset (MHI-ds), 0.981 (95% CI: 0.981, 0.981) on external public datasets (UKB, CLSA, MIMIC-IV and PTB), and 0.983 (95% CI: 0.983, 0.983) on external private datasets (UW, UCSF, JGH, NYP, MGH, CSH and CHUM), while DeepECG-SL demonstrated AUROCs of 0.992 (95% CI: 0.992, 0.992), 0.980 (95% CI: 0.980, 0.980), and 0.983 (95% CI: 0.983, 0.984), respectively. Fairness analyses revealed minimal disparities (true-positive rate and false-positive rate difference <0.1) across age and sex groups for both models. DeepECG-SSL demonstrated superior performance on limited-data digital biomarker tasks, with the largest improvements in long QT syndrome (LQTS) genotype classification (AUROC 0.931 vs 0.850, P = .026, n = 127 ECGs) and 5 year atrial fibrillation risk prediction (AUROC 0.742 vs 0.734, P < 0.001, n = 132 050 ECGs), while achieving superior performance in left ventricular ejection fraction ≤40% classification (AUROC 0.926 vs 0.917, P < 0.001, n = 25 252 ECGs) and comparable performance in LQTS detection (AUROC 0.767 vs 0.735, P = 0.117, n = 934 ECGs). CONCLUSIONS: This study establishes SSL as a promising paradigm for ECG analysis, particularly in settings with limited annotated data, enhancing accessibility, generalizability, and fairness in AI-driven cardiac diagnostics. By releasing model weights, preprocessing tools, and validation code, this work aims to support robust, data-efficient AI diagnostics across diverse clinical environments and questions.
16 May 2026
Read appraisal →European journal of preventive cardiology
Visit-to-visit blood pressure variability and cardiovascular outcomes: a systematic review and dose-response meta-analysis
AIMS: Visit-to-visit blood pressure variability (VVV BPV) is a recognized risk factor for cardiovascular disease (CVD) that is underutilized in clinical practice. The reliability of electronic health record (EHR) data in estimating BPV and predicting CVD remains uncertain. This study compared BPV estimation methodologies using EHR vs. non-EHR data and examined dose-response associations with CVD. METHODS AND RESULTS: A systematic review and meta-analysis was conducted across five databases (MEDLINE, Scopus, EMBASE, CINAHL, and Web of Science) for studies published from January 2012 to August 2024. Studies assessing VVV BPV in adults and its association with CVD outcomes (e.g. myocardial infarction, stroke, heart failure, and cardiovascular mortality) were included. A dose-response meta-analysis (DRMA) evaluated BPV thresholds linked to increased CVD risk using standard deviation (SD) and coefficient of variation (CV). A total of 4926 studies were screened, 49 of which met the inclusion criteria. No consensus has emerged on BPV estimation methodologies, although non-EHR studies have followed stricter protocols. The meta-analysis showed that VVV BPV predicted any CVD outcome. Effect sizes were comparable between EHR [the hazard ratio (HR): 1.17, 95% confidence interval (CI): 1.09-1.24] and non-EHR (HR: 1.14, 95% CI: 1.10-1.17) studies (P-value = 0.468). A BPV threshold of SD 6.72 mmHg or CV 9.05% was linked to a 10% higher CVD risk. CONCLUSION: The EHR data reliably estimate BPV, yielding effect sizes similar to those of non-EHR sources. A non-linear dose-response relationship suggests that a higher BPV increases CVD risk. Visit-to-visit blood pressure variability needs to be incorporated into clinical practice, and further research is required to identify strategies to implement and scale up into routine workflow. Visit-to-visit blood pressure variability (VVV BPV) is capable of predicting CVD development in 6 years. However, its clinical use remains limited, and the reliability of electronic health records (EHR) in measuring BPV is uncertain. Having a standard deviation of VVV BPV ≥6.72 mmHg or a coefficient of variation of VVV BPV ≥9.05% mmHg is linked to a 10% higher CVD risk. Blood pressure variability estimates from EHR data were comparable to those from non-EHR studies, suggesting that EHR-based BPV assessment is reliable for CVD risk evaluation.
16 May 2026
Read appraisal →JMIR mental health
Large Language Models and Their Applications in Mental Health: Scoping Review
BACKGROUND: Large language models (LLMs) are poised to transform mental health care, offering advanced capabilities in diagnosis, prognosis, and decision support. Since their inception, numerous mental health-focused LLMs have emerged in the scientific literature, reflecting the growing interest in leveraging these models across various clinical applications. With a broad range of models available, diverse optimization strategies, and multiple use cases, reviewing the current landscape is critical to understanding where future impact lies. OBJECTIVE: This study aimed to conduct a scoping review investigating the use of LLMs in mental health across diagnostic, prognostic, and decision support tasks. METHODS: We screened 3121 papers from PubMed, Scopus, and Web of Science for studies published between January 2023 and October 2025, using terms related to LLM and mental health. After removing duplicates, 2 reviewers (MCL and WWBG) independently screened the studies, with a third (JJK) to resolve conflicting opinions. We extracted and synthesized information on the models, use cases, datasets, and adaptation methods from selected papers. RESULTS: In total, 41 papers were selected. Many studies included evaluations on OpenAI's GPT series applications: GPT-4 (24 studies, 58.5%) and GPT-3.5 (16 studies, 39%). Others included Bidirectional Encoder Representations from Transformers-derived models (9 studies, 22%), LLaMA (8 studies, 19.5%), and RoBERTa-derived models (6 studies, 14.6%). While all studies initially applied out-of-the-box LLMs, several adapted them through few-shot learning or fine-tuning to better align with specific research goals. The most common use case was in diagnostics (31 studies, 75.6%), while the most common target condition was depression (11 studies, 26.8%). While many studies reported superior performance of LLMs, only a minority of studies (13 studies, 31.7%) validated LLM performance against clinician assessments using real patient data, with the majority relying on proxy outcomes such as clinical vignettes, examination questions, or social media posts. CONCLUSIONS: Despite rapid growth and diversity of LLM applications in mental health, the field remains nascent and exploratory. Future developments must emphasize consistent model adaptation procedures to ensure safety and clinical workflow alignment. Models must also be evaluated on robust evaluation criteria by using standardized protocols and real clinical outcome measures.
16 May 2026
Read appraisal →European heart journal
Wearable devices and cardiovascular health: revolutionizing remote monitoring and disease prevention
Wearable devices are transforming cardiovascular medicine by enabling continuous monitoring of physiologic and behavioural measures outside of traditional clinical settings. Smartwatches and activity trackers, the most widely used wearables, employ motion and biometric sensors to measure physical activity, sleep quality, heart rate, and rhythm. By converting health goals into objective, quantifiable measures, wearable devices empower patients to assume a more active role in their health while providing clinicians with novel opportunities for longitudinal, real-world assessment. Clinical applications span the cardiovascular continuum from lifestyle interventions targeting physical activity and sleep to the remote management of chronic conditions such as heart failure. Widespread clinical adoption of wearables remains limited by challenges, such as variability in device methodology, data outputs, validation, and intended use; incompatibility with existing electronic health records; and the lack of standardized, evidence-based workflows for clinicians to efficiently interpret and act upon wearable data. This review summarizes the current landscape of wearable technologies in cardiovascular medicine by highlighting key clinical applications, evidence gaps in the existing literature, the role of artificial intelligence, and barriers to implementation. We discuss strategies to enhance clinical integration and strengthen the current evidence base while also providing practical guidance to help clinicians navigate commonly encountered clinical scenarios.
16 May 2026
Read appraisal →Journal of medical Internet research
A Digital Diabetes Self-Management Education and Support Program Integrated With Continuous Glucose Monitoring for Type 2 Diabetes: Randomized Controlled Trial
BACKGROUND: Previous research has demonstrated that the use of continuous glucose monitoring (CGM) can improve glycemic control in people with type 2 diabetes when used regularly alongside a digital diabetes self-management education and support (DSMES) program. However, to date, there is limited evidence showing the benefits of a digitally delivered DSMES program combined with real-time CGM for adults with type 2 diabetes. OBJECTIVE: The objective of this study is to evaluate the impact of a DSMES program coupled with CGM on hemoglobin A1c (HbA1c) and CGM-derived glycemic measures compared to usual care for adults with type 2 diabetes over 6 months. METHODS: Participants with type 2 diabetes and HbA1c of 8% or higher (64 mmol/mol) who were not using mealtime bolus insulin (aged 26-83 y; mean HbA1c 9.6%, SD 1.4% [mean 81.2 mmol/mol, SD 15.8 mmol/mol]) were randomly assigned to a digital DSMES+CGM integrated solution (n=51) or usual care (n=49) for 6 months. The primary outcome was HbA1c. The secondary outcomes were CGM-derived glycemic measures, including glucose management indicator, percent time in range 70 to 180 mg/dL, percent time above range (>180 mg/dL), percent time below range (<70 mg/dL), and mean glucose. Linear mixed effects models were used for intention-to-treat analyses. RESULTS: HbA1c was lower among the intervention group versus the usual care group at 3 months (difference=-0.7%, 95% CI -1.4% to -0.1% or difference=-8.1 mmol/mol, 95% CI -15.5 to -0.7 mmol/mol; P=.03) and at 6 months (difference=-0.6%, 95% CI -1.4% to 0.2% or difference=-6.9 mmol/mol, 95% CI -15.7 to 1.9 mmol/mol; P=.12) but only reached statistical significance at 3 months. CGM-derived glycemic measures, including glucose management indicator (difference=-0.9%, 95% CI -1.7% to -0.1%; P=.03), time in range (difference=14.6%, 95% CI 1.0% to 28.2%; P=.04), time above range (difference=-14.9%, 95% CI -29.0% to -0.9%; P=.04), and mean glucose (difference=-36.4 mg/dL, 95% CI -70.0 to -2.9 mg/dL; P=.03), also significantly improved for the intervention group versus the usual care group at 6 months. CONCLUSIONS: The combination of digital DSMES+CGM is effective for supporting adults with type 2 diabetes in managing their condition and has the potential to reduce the risk of long-term health complications.
16 May 2026
Read appraisal →JMIR formative research
Use of Commercially Available Large Language Models to Generate Information Leaflets on Post-Intensive Care Syndrome: Clinical Utility Assessment
BACKGROUND: Patients and their families without medical knowledge may find professional health care information difficult to understand. The use of large language models (LLMs) to simplify and translate complex medical content holds promise for improving comprehension while reducing the burden on health care providers tasked with delivering explanations. OBJECTIVE: This study aims to evaluate the quality of information leaflets generated using commercially available LLMs. METHODS: Informational texts on post-intensive care syndrome were generated using 6 different LLMs and 4 prompt designs with varying levels of instructional guidance. Clinical practice guideline documents were uploaded and provided to the models as reference context, reflecting a pragmatic clinical scenario without model tuning or advanced retrieval pipelines. In total, 72 texts were generated (6 models × 4 prompts × 3 outputs). After excluding texts shorter than 500 characters (n=16) and those without explicit mention of post-intensive care syndrome (n=3), 53 texts remained. To enable balanced human evaluation across model-prompt combinations, the longest eligible response from each pair was selected (4 prompts × 4 models; n=16). Following independent expert review by 2 medical specialists, 7 texts were excluded, leaving 9 texts for final analysis. Ten individuals, including health care professionals and nonmedical personnel, assessed the texts using a 10-point Likert scale across multiple quality domains. An LLM-based parallel assessment was also conducted, and scores were compared across models and evaluator groups. RESULTS: In the human evaluation of the selected 9 texts, the generated texts achieved an average score of 6.8 or higher across all evaluation criteria, with no potentially harmful content identified. The text generated by LLaMA 3 70B, using a step-by-step approach combined with text-augmented prompting based on clinical guidelines, received the highest overall score, whereas the lowest-rated text was produced using a simple prompt without text augmentation. Although no consistent trends were observed across LLMs or prompt engineering strategies, text-augmented prompting was generally associated with higher evaluation scores. Ratings differed between professional and nonprofessional evaluators. Given the feasibility-driven screening process and the resulting limited sample size, the findings should be interpreted as exploratory and descriptive rather than definitive estimates of overall model performance. CONCLUSIONS: Among the selected texts included in the final human evaluation, informational materials generated using commercially available LLMs were generally rated as acceptable by human evaluators, and none contained harmful content. These findings suggest that LLMs may support the development of patient-facing informational materials under feasibility-constrained conditions, although further validation with larger and more diverse samples is warranted.
16 May 2026
Read appraisal →PloS one
Optimising supervised machine learning algorithms predicting cigarette cravings and lapses for a smoking cessation just-in-time adaptive intervention (JITAI).
This study aimed to optimise the balance between participant burden and algorithm performance for predicting high-risk moments in a smoking cessation just-in-time adaptive intervention (JITAI) by systematically varying ecological momentary assessment (EMA) prompt frequency, predictor count, and training data source. Thirty-seven participants completed 16 EMAs per day for the first 10 days of their smoking cessation attempt, reporting mood, context, behaviour, cravings, and smoking lapses. Random forest algorithms predicting lapses and cravings were evaluated in terms of F1-score and ROC-AUC via mixed effects models accounting for clustering within individuals. Performance across out-of-sample individuals ranged from excellent to poor but was, on average, modest. Lapse prediction outperformed craving prediction, particularly for ROC-AUC (Median F1-score: Lapses 0.436 [IQR 0.180-0.625], Cravings 0.400 [IQR 0.048-0.649]; Median ROC-AUC: Lapses 0.659 [IQR 0.514-0.809], Cravings 0.628 [IQR: 0.510-0.729]). A substantial proportion of configurations fell below commonly used minimum performance thresholds, particularly for F1-score. Reducing EMA frequency had outcome- and metric-dependent effects. Lapse F1-scores improved with fewer prompts (16 EMAs: 0.254 [IQR 0.081-0.500], 3 EMAs: 0.588 [IQR 0.353-0.667]), while ROC-AUC showed a slight, inconsistent decline (16 EMAs: 0.661 [IQR 0.520-876], 4 EMAs: 0.613 [IQR 0.494-0.786], 3 EMAs: 0.704 [IQR 0.567-0.809]). For cravings, both metrics declined with fewer prompts (F1-score: 16 EMAs: 0.470 [IQR 0.141-0.745]; 3 EMAs: 0.333 [IQR 0.000-0.600]; ROC-AUC: 16 EMAs 0.700 [IQR 0.582-0.811], 3 EMAs 0.544 [IQR 0.421-0.676]). Feature reduction had negligible impact on lapse prediction (F1-score: all features 0.435, selected features 0.441; ROC-AUC: all 0.660, selected 0.657), but slightly reduced craving performance (F1-score: all 0.410 [IQR 0.117-0.646], selected 0.400 [IQR 0.000-0.650]; ROC-AUC: all 0.632, selected 0.622). Including participant-specific data improved lapse F1-scores (None 0.286 [IQR 0.000-0.571], 30 pc 0.542 [IQR: 0.329-0.667]), but not ROC-AUC (None 0.655 [IQR: 0.512-0.786], 30 pc 0.694 [IQR 0.513-0.852]); and impaired craving ROC-AUC (None 0.650 [IQR: 0.544-0.734], 30 pc 0.614 [IQR 0.493-0.730]; F1-score: None 0.424 [IQR 0.143-0.649], 30 pc 0.400 [IQR 0.000-0.703]). Overall, EMA-based machine learning detected lapse risk but showed modest overall performance and substantial inter-individual variability. Algorithms using higher EMA density, larger predictor sets, and participant-specific training data did not consistently outperform over more parsimonious approaches. However, machine learning prediction alone is unlikely to be sufficient for real-world JITAI implementation and may be best combined with complementary rules-based approaches.
16 May 2026
Read appraisal →BMJ open gastroenterology
Development and validation of multivariable prognostic machine learning models to identify patients at risk for inadequate bowel preparation when undergoing colonoscopy: a prospective, multicentre study
OBJECTIVE: Inadequate bowel preparation impairs the accuracy of colonoscopy and increases the burden on patients and healthcare systems. Consequently, the quality of bowel preparation is an important quality indicator. We aim to develop and validate multivariable prognostic models for the identification of patients at risk of inadequate bowel preparation using machine learning (ML). METHODS: Demographic and clinical data from consecutive patients ≥18 years of age who underwent colonoscopy at six centres in Germany were prospectively collected. Adequate bowel preparation was defined as Boston Bowel Preparation Scale ≥6 with a value ≥2 in each colonic segment. We used statistical and ML methods to build prognostic models and to compare them to published models. RESULTS: Overall, we analysed 2652 patients, including 699 (26.4%) inpatient procedures. The mean patient age was 57.6 years (SD 16 years), and 48.9% were women. In 1401 (52.8%) patients, the indication was screening or surveillance, and 1035 (39%) patients had a first-time colonoscopy. The rate of inadequate bowel preparation was 16%. Sensitivities, specificities and areas under the curve of predictive models obtained by generalised boosting models, Ranger, support vector machine (radial), CatBoost and Net Regularised Generalised Linear Models were 0.51-0.86, 0.52-0.83 and 0.71-0.74, respectively. They were only marginally superior to a logistic regression model. All models had high negative predictive values >0.9 for inadequate bowel preparation. To detect one patient with inadequate bowel preparation, 8-10 patients need to be evaluated. CONCLUSIONS: Predictive models to identify patients at risk for inadequate bowel preparation obtained by ML showed comparable results compared with a logistic regression model. TRIAL REGISTRATION NUMBER: DRKS00018878.
16 May 2026
Read appraisal →Journal of affective disorders
An exploratory randomized controlled trial of an AI-enabled mental health intervention for generalized anxiety.
Generalized Anxiety Disorder (GAD) is prevalent and often co-occurs with depression, contributing to significant disability and healthcare burden. Although treatments such as CBT and SSRIs are effective, access remains limited. This exploratory, randomized controlled trial evaluated the effectiveness of an AI-powered mental health app (PATH) in reducing symptoms of anxiety and depression. A total of 316 UK-based participants (aged 19-70) were randomized to either the intervention group (PATH) or a control group (NHS self-help website). The intervention provided evidence-based strategies including CBT-informed chat therapy and interactive tools. Anxiety (GAD-7) and depression (PHQ-9) scores were measured at baseline, two, eight, and twelve weeks. Of the 316 randomized participants, 235 completed the post-intervention assessment (33.0% attrition in the intervention group vs. 18.5% in the control group). Retention at 8-week and 12-week follow-ups was 77.4% and 54.0%, respectively. At two weeks, the intervention group showed significantly lower GAD-7 and PHQ-9 scores compared to control, with medium effect sizes. At eight weeks, those who continued using the app showed significant reductions in both anxiety and depression, while those who discontinued still showed medium improvements in anxiety. Effects were maintained at twelve weeks, with moderate to large effect sizes. Findings suggest that PATH significantly reduces anxiety and depressive symptoms, particularly with continued use. These results support the app's potential as a scalable, accessible digital intervention to address mental health treatment gaps.
16 May 2026
Read appraisal →Journal of affective disorders
Sociodemographic and clinical predictors of digital mental health intervention engagement among treatment-seeking psychiatric outpatients
BACKGROUND: Digital mental health interventions (DMHIs) have shown promise improving depression, anxiety, and psychiatric distress, yet real-world engagement remains low. Increasing engagement has great potential to improve the impact of DMHIs, but little is known about the drivers of engagement in naturalistic settings. To better understand predictors of engagement, we examined sociodemographic and clinical characteristics associated with DMHI usage among a large clinical sample of adults. METHOD: 1223 adults (74% White, 68% women, Mage = 36.8 years) with scheduled intake appointments for outpatient psychiatric services were randomized to either a mindfulness-based app (Headspace) or a CBT-based app (SilverCloud). Usage data were automatically collected, and participants were neither required nor compensated to use the apps. RESULTS: Participants engaged with their assigned DMHIs a median of 8 days, with 88.2% of participants using their assigned DMHI at least once. Participants engaged with Headspace for more than twice as many days [IRR (95% CI) = 2.4 (2.1, 2.7)] as SilverCloud. Female sex, white race, a college degree, and older age up to 60 predicted greater engagement. Further, depression severity was associated with engagement in a non-linear manner for those assigned to Headspace, with less engagement at minimal/mild and severe symptoms compared to moderate and moderately-severe symptoms. CONCLUSIONS: These findings indicate meaningful differences in engagement between DMHIs based on sociodemographic and clinical characteristics. There may be opportunities to improve engagement by tailoring DMHI offerings, with a particular emphasis on meeting the needs of less-engaged populations.
16 May 2026
Read appraisal →The British journal of general practice : the journal of the Royal College of General Practitioners
Pollution to prevention: digital decision support for air pollution and exercise counselling in general practice.
BACKGROUND: Outdoor exercise supports physical, mental, and social wellbeing. However, exposure to air pollution during activity can increase cardiorespiratory risk. The Royal College of Physicians (A Breath of Fresh Air, 2025) urges clinicians to address environmental determinants of health, including air quality, during routine care, including annual checks. Patients increasingly use digital tools providing air-quality data and actionable behavioural recommendations, yet the quality, evidence base, and integration of these tools into clinical workflows remain unclear. AIM: To evaluate digital tools that help patients modify exercise behaviour based on air-pollution exposure to inform the development of a custom chatbot to support exercise counselling in primary care. METHOD: A scoping review of app libraries, grey literature, and peer-reviewed studies identified digital tools providing air-quality data with actionable exercise recommendations. Data extraction covered functionality, pollutants, validation, and quality using the Mobile App Rating Scale (MARS) and Behaviour Change Technique Taxonomy (BCTTv1). RESULTS: Seven tools were identified: IQAir, Plume Labs, BreezoMeter, EPA AirNow, PurpleAir, airTEXT, and AirTrack. MARS scores ranged from 2.9 to 4.3 (mean = 3.6/5.0), highest for functionality and aesthetics but lower for information accuracy and engagement. Behaviour change content was limited to general health information, and no tools demonstrated formal validation or clinical integration. CONCLUSION: Existing tools provide accessible air-quality data but lack individualised recommendations for exercise. Insights informed a custom chatbot embedding air-quality and exercise advice into GP cardiorespiratory annual reviews. Next steps include further development of the custom chatbot and pilot implementation in general practice to assess feasibility, clinician uptake, and patient outcomes.
16 May 2026
Read appraisal →Computers in biology and medicine
Imaging and machine learning reveal pigment ring spatial characteristics in vitiligo progression
BACKGROUND: Vitiligo is a chronic autoimmune disorder causing skin depigmentation due to melanocyte loss, with pigment ring dynamics being key to understanding and managing the disease. METHODS: Data from 105 vitiligo patients were collected using reflectance confocal microscopy (RCM) imaging to analyze pigment ring patterns across progressive and stable stages. A multi-task self-supervised learning framework was used to identify and classify pigment ring types (complete, depressed, incomplete, and absent). Graph-based structural analysis was applied to characterize the spatial and network dynamics of pigment rings, incorporating metrics such as clustering coefficients, centrality measures, and Ripley's statistics. RESULTS: Progressive-stage patients exhibited denser clustering and spatial propagation of absent and depressed pigment rings, which played dominant roles in the network. Stable-stage patients showed higher clustering coefficients for complete rings, indicating restored structural stability. The learning framework demonstrated high accuracy in identifying pigment ring types, with an AUC of 0.99 and PR score of 0.95. Ripley's K-function analysis highlighted significant clustering of absent and depressed rings in the progression stage, correlating with active disease. CONCLUSION: Integrating imaging, machine learning, and graph-based analysis offers key insights into vitiligo progression, enabling better staging and personalized treatments, with future research needed for broader validation and model refinement.
16 May 2026
Read appraisal →JMIR mental health
Adoption of Digital Mental Health Interventions in National Health Service England, Scotland, and Wales: Freedom of Information Questionnaire Study
BACKGROUND: Digital mental health interventions (DMHIs) have been widely promoted to improve access to mental health care within the UK National Health Service (NHS), particularly following the COVID-19 pandemic. In 2015, a total of 48 technologies were reportedly used in NHS services in England, but over the past decade, substantial changes to regulatory requirements, evidence standards, and procurement processes have reshaped the digital mental health landscape. There is limited clarity regarding which DMHIs are currently being formally procured and funded by NHS mental health services across the United Kingdom. OBJECTIVE: This study aimed to identify and describe the DMHIs currently procured, contracted, or paid for by NHS mental health service providers in England, Scotland, and Wales for adult common mental health problems and to compare current procurement practices with findings reported in 2015. METHODS: Freedom of Information requests were submitted to all NHS mental health trusts in England and all health boards in Scotland and Wales. Responses were collated and screened to provide an updated and extended record of which technologies are reportedly procured or paid for by services. RESULTS: In total, 19 different DMHIs were identified as being procured across mental health service providers for adult common mental health problems at the time of data collection. This demonstrates a substantial reduction in the number of technologies being adopted into practice compared to the 48 reported in England in 2015. The findings reveal several key insights, including that only 2 technologies have remained in use for a decade, and they shed light on the types of technologies being selected and the variations in procurement practices among the 3 national health services. CONCLUSIONS: Despite the expansion of the digital mental health marketplace, the number of DMHIs formally procured by NHS mental health services has markedly decreased over the past decade. This consolidation may reflect increased selectivity and the adoption of higher-quality products, driven by strengthened regulatory oversight, evidence standards, and national guidance. Although these developments may enhance safety and quality assurance, they also raise important questions about innovation, market sustainability, and equitable access to digital mental health care. Ongoing monitoring of procurement practices is needed to inform policy, service design, and the future development of DMHIs.
16 May 2026
Read appraisal →JMIR research protocols
A Natural Language Processing Framework for Structuring and Visualizing Clinical Trial Eligibility Criteria at Scale: Protocol for a Quantitative Study
BACKGROUND: Eligibility criteria are essential to clinical trial design, guiding recruitment, and ensuring patient safety and scientific rigor. However, criteria are often lengthy, heterogeneous, and inconsistently formatted, which hinders large-scale interpretation and slows patient-trial matching. Manual review is time-consuming and error-prone. Advances in natural language processing and large language models (LLMs) offer opportunities to standardize and analyze eligibility text at scale. OBJECTIVE: This study aims to develop and evaluate a scalable system that uses LLM-enabled natural language processing and unsupervised learning to identify, normalize, categorize, and visualize clinical trial eligibility criteria, with the goal of improving patient-trial matching and revealing domain-level trends. METHODS: We designed a three-part pipeline: (1) representation of eligibility text using embeddings, followed by clustering to group semantically similar criteria; (2) dual-layer zero-shot LLM summarization for concept normalization, refinement, and deduplication of cluster exemplars; and (3) an interactive, web-based visualization interface to explore criteria distributions and trends by disease domain and over time. The pipeline was applied to 53,872 oncology trials (breast, lung, and gastrointestinal cancer) indexed on ClinicalTrials.gov. Outputs include cluster labels, normalized criterion summaries, and per-domain frequency profiles. Feasibility was assessed via successful end-to-end processing and inspection of face validity for cluster coherence and domain-specific patterns. RESULTS: The system successfully processed all 53,872 trials and generated stable clusters of inclusion and exclusion concepts. The LLM summarization layers produced concise, nonredundant labels that improved the interpretability of clustered criteria. The visualization interface enabled rapid exploration of cross-trial patterns and temporal trends within breast, lung, and gastrointestinal oncology, facilitating identification of common inclusion requirements and potential barriers to enrollment. A public, open-source demonstration instance allows for interactive exploration of these clusters and summaries. Benchmarking through human validation on a random sample of eligibility criteria found the system to be 94% (470/500) accurate, reflecting its ability to consistently categorize criteria correctly in congruence with human judgment. CONCLUSIONS: A combined embeddings-clustering-LLM pipeline can standardize heterogeneous eligibility text and surface domain-level patterns at scale. This framework provides a foundation for accelerating patient-trial matching and informing future trial design. While the current implementation was evaluated on ClinicalTrials.gov oncology trials, the approach is readily generalizable to additional diseases and alternative modeling configurations.
16 May 2026
Read appraisal →Computers in biology and medicine
Artificial intelligence for metabolic dysfunction-associated steatotic liver disease diagnosis: A systematic review
Metabolic dysfunction-associated steatotic liver disease (MASLD) poses significant diagnostic challenges. We conducted a systematic review of artificial intelligence (AI) applications in MASLD diagnosis, focusing on predictive modeling, screening, imaging analysis, and therapeutic assistance. Following PRISMA guidelines, we searched five databases (Web of Science, SciFinder, Elsevier ScienceDirect, EBSCOhost, Springer) for studies published January 2020-January 2026. Of 1247 initially identified records, 191 studies met inclusion criteria. AI models demonstrate high diagnostic performance across multiple modalities. Deep learning analysis of US, CT, and MRI enables automated steatosis quantification and fibrosis staging with accuracy comparable to expert interpretation. AI integration with multi-omics data facilitates novel biomarker discovery and therapeutic target identification. Despite these advances, model generalisability, standardization, and clinical integration remain challenges. AI holds substantial potential to transform MASLD diagnostics, but prospective validation and implementation strategies are needed before widespread clinical adoption.
16 May 2026
Read appraisal →Journal of medical Internet research
Understanding Patient-Reported Offenses in Electronic Health Records: Cross-Sectional Mixed Methods Survey
BACKGROUND: Patients' access to their electronic health record (EHR) supports their participation and satisfaction with care. Despite the benefits, some patients have been upset after reading their EHR. Additionally, health care professionals are concerned that patients, particularly those with mental health conditions, may be offended, and they have expressed a need for further guidelines on how to write EHRs. Experiences among various patient groups are essential to support the relationship between patients and professionals. However, prior studies have often focused on single patient groups or specific clinical contexts, leaving a limited understanding of differences across multiple patient groups. OBJECTIVE: This study aimed to determine whether certain patient groups are more likely to feel offended while reading their EHRs and which information is perceived as offensive and to provide a comparison across multiple patient groups using a mixed methods approach. METHODS: A cross-sectional survey was conducted via the Finnish national patient portal using a web-based patient survey, adopting a mixed methods approach. The survey included multiple-choice and open-ended questions. The total sample comprised 4681 respondents. The survey respondents were placed into 4 patient groups: those who had received care for mental health, cancer, or other conditions and those who had received no care. Associations between the type of care and patients who felt offended were estimated using multivariate binary logistic regression. Inductive content analysis (n=502) was conducted to identify information perceived as offensive in the EHR. RESULTS: The patients who had received mental health care (166/654, 25.4%) or cancer and mental health care (9/39, 23.1%) were more likely to be offended by information in their EHR compared to the other groups (cancer care: 37/375, 9.9%; other conditions care: 383/3316, 11.6%; no care: 22/206, 10.7%; other conditions care: odds ratio 0.37, 95% CI 0.29-0.46; P<.001; model A). Additionally, female patients, those with bad or very bad health conditions, and patients with bachelor's or master's degrees were significantly more likely to feel offended. Errors, the health care professionals' disrespectful language, and perceived unnecessary information were the most frequently mentioned reasons for being offended. Patients with mental health care reported more often that unnecessary information and professionals' opinions and word choices were experienced as offensive compared to other patients. CONCLUSIONS: This study contributes new knowledge by identifying differences across multiple patient groups. Although a minority of patients felt offended by their EHR, health care professionals should consider that some patients, particularly those who have received mental health care or cancer and mental health care, may be offended by specific information or word choices in their EHRs. To address this, health care professionals should receive education on how to write their notes in a neutral tone and avoid potentially offensive topics. Improving the quality of EHRs could strengthen the relationship between patients and professionals.
15 May 2026
Read appraisal →BMJ open
AI chatbots for health information seeking among Chinese patients with precancerous ENT lesions: a descriptive qualitative study
OBJECTIVE: To explore how Chinese patients with precancerous ear, nose and throat (ENT) lesions experience using artificial intelligence (AI)-driven chatbots for health-related information seeking, with particular attention to perceived benefits, challenges and influences on information-seeking practices. DESIGN: Descriptive qualitative study. SETTING: Department of Otolaryngology, West China Hospital, Sichuan University, China. PARTICIPANTS: 12 adult patients with clinically diagnosed precancerous ENT lesions who had used AI-driven chatbots at least three times in the previous 30 days to seek health-related information about their condition were purposively recruited. Face-to-face, semistructured interviews were conducted between 11 October 2024 and 10 November 2024. RESULTS: Interviews were analysed using Colaizzi's method. Four themes were identified. First, participants described AI chatbots as an immediate and accessible source of health information, particularly when questions arose outside clinical encounters or during periods of uncertainty. Second, many reported moving away from conventional search engines towards conversational information seeking, valuing direct and synthesised responses over link-based retrieval. Third, participants emphasised that obtaining useful answers depended on learning to ask clear and specific questions, suggesting that effective prompting was an important user skill. Fourth, some participants perceived chatbot interaction as emotionally safer than asking healthcare professionals certain questions, particularly when they felt embarrassed, worried about asking repetitive questions or feared being judged. CONCLUSIONS: Among this sample of Chinese patients with precancerous ENT lesions, AI-driven chatbots were perceived as a convenient and conversational supplementary source of health information. Participants valued their accessibility and interactional ease, but also indicated that their usefulness depended partly on users' ability to formulate effective prompts. Some participants additionally perceived chatbots as a more comfortable channel for asking sensitive or basic questions. The findings suggest that AI chatbots may have a complementary role in patient information support, but further research is needed to evaluate response accuracy, safety and appropriate integration into clinician-led care.
15 May 2026
Read appraisal →Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
A sentence is worth a thousand pictures: can large language models understand hum4n L4ngu4ge and the W0rld behind W0rds?
The current generation of large language models (LLMs) has been linked to claims about human-like linguistic performance, and their applications are hailed both as a step towards artificial general intelligence and as a major advance in understanding the cognitive and even neural basis of human language. To assess these claims, first, we analysed the contribution of LLMs as theoretically informative representations of a target cognitive system versus atheoretical mechanistic tools. Second, we evaluated the models' ability to see the bigger picture through top-down feedback from higher levels of processing, which requires grounding in previous expectations and past world experience. We hypothesize that since models lack grounded cognition, they cannot take advantage of these features and instead solely rely on fixed associations between represented words and word vectors. To assess this, we ran a novel leet task (l33t t4sk), which requires decoding sentences in which letters are systematically replaced by numbers. In line with our hypothesis, the results suggest that humans excel in this task, whereas models struggle. We interpret these results by identifying the key abilities that are still missing from the current state of development of these models, which require solutions that go beyond increased system scaling. This article is part of the theme issue 'World models in natural and artificial intelligence'.
15 May 2026
Read appraisal →Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Levels of analysis for large language models
Modern artificial intelligence systems, such as large language models, are increasingly powerful but also increasingly hard to understand. Recognizing this problem as analogous to the historical difficulties in understanding the human mind, we argue that methods developed in cognitive science can be useful for understanding large language models. We propose a framework for applying these methods based on the levels of analysis that David Marr proposed for studying information processing systems. By revisiting established cognitive science techniques relevant to each level and illustrating their potential to yield insights into the behaviour and internal organization of large language models, we aim to provide a toolkit for making sense of these new kinds of minds. This article is part of the theme issue 'World models in natural and artificial intelligence'.
15 May 2026
Read appraisal →BMC endocrine disorders
Effects of digital and remote exercise interventions on HbA1c in type 2 diabetes: a systematic review and network meta-analysis
BACKGROUND: To compare the effects of different digitally and remotely deliveredexercise interventions on glycated hemoglobin (HbA1c) in adults with type 2diabetes mellitus (T2DM) using a network meta-analysis (NMA), and to clarifythe relative effectiveness of distinct delivery models. METHODS: A systematic review and network meta-analysis of randomized controlledtrials was conducted following PRISMA-NMA guidelines and prospectivelyregistered in PROSPERO (CRD420261288210). Major databases were searched frominception to November 11, 2025. Exercise-centered digital and remoteinterventions were categorized as digital exercise interventions (DEX),structured exercise prescription (SEP), multi-behavior lifestyle interventions(MBLI), and tele-exercise/tele-rehabilitation (TEX). The primary outcome waschange in HbA1c. RESULTS: Fifty-four RCTs were included. Compared with control, TEX demonstratedthe greatest reduction in HbA1c (MD -0.51%, 95% CrI -0.88 to -0.14), followedby MBLI (MD -0.32%, 95% CrI -0.51 to -0.13). SEP showed a non-significanttrend, and DEX did not significantly reduce HbA1c. SUCRA rankings favored TEX(0.87), followed by MBLI (0.66), SEP (0.62), and DEX (0.24). Subgroup analysessuggested that HbA1c reductions with TEX and MBLI were more evident among olderadults (≥60 years) and those with longer diabetes duration（>10 years）;however, network inconsistency was detected in these subgroups, and findingsshould be interpreted cautiously. CONCLUSIONS: Digitally and remotelydelivered exercise interventions differ in their effectiveness for HbA1ccontrol in T2DM. Interventions incorporating ongoing professional supervisionare associated with the most consistent glycemic benefits, whereas largelyautomated approaches show limited effects, highlighting the importance ofdelivery structure and supervision intensity.
15 May 2026
Read appraisal →Systematic reviews
Barriers and facilitators to the implementation and adoption of computerised clinical decision support systems: an overview of reviews
BACKGROUND: The use of computerised clinical decision support systems (CDSSs) holds considerable potential in enhancing healthcare delivery by improving patient safety, practitioner performance, and patient outcomes. However, despite the numerous reported advantages of CDSSs, their adoption remains restricted, thereby compromising the full realisation of their potential. To enable a profound evaluation and successful implementation of these systems, it is imperative to identify the reasons for their limited uptake. The objective of this study is to provide a comprehensive overview of the barriers and facilitators to the implementation and adoption of decision support systems across healthcare settings. METHODS: This study was reported in accordance with the Preferred Reporting Items for Overviews of Reviews (PRIOR) statement. A systematic search was conducted in the databases PubMed, IEEE Xplore, Scopus, and Web of Science from inception to 15 October 2024, targeting at review articles of primary studies focusing on the identification and reporting of barriers and facilitators to the implementation and adoption of CDSSs across healthcare settings. The risk of bias of the included reviews was assessed with the Risk of Bias in Systematic Reviews (ROBIS) tool, and the primary study overlap was calculated using the corrected covered area method. All data on barriers and facilitators to the implementation and adoption of CDSSs were extracted from the included reviews, synthesised through a mapping of identical factors, and ranked according to their frequency of occurrence. RESULTS: Of the 1640 records retrieved through database searches, 30 reviews were included in this overview, which summarised the findings of 721 unique primary studies. A total of 101 distinct factors influencing the implementation and adoption of CDSSs were identified and could be categorised into the overarching categories of human, technology-related, and contextual factors. The following six factors were reported most frequently: usability, usefulness/perceived benefits, organisational readiness, training, trust, and workflow integration. CONCLUSIONS: The findings of this study highlight the diversity of factors that influence a successful implementation of CDSSs and emphasise the need for their comprehensive evaluation that goes beyond the assessment of general performance aspects but takes into account human and contextual factors. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42024507614.
15 May 2026
Read appraisal →The Journal of clinical ethics
Ethical Responsibility in the Off-Label Use of AI in Medical Imaging
AbstractArtificial intelligence in medical imaging (AI-MI), categorized by the U.S. Food and Drug Administration (FDA) as "Software as a Medical Device," can offer significant benefit to medical care. FDA approval-represented by a "label"-indicates that the device has demonstrated safety and efficacy for a specific clinical task and population for which the device is intended. Off-label use of AI-MI outside of the task and/or population can be medically and ethically problematic, yet benefits may be reduced or missed if entirely prohibited. Ethical responsibility frameworks can assist in evaluating and establishing boundaries and opportunities in off-label use of AI-MI. In this work, we review three different models for ethical responsibility and apply them to off-label use of AI-MI. We find that a future-looking collective responsibility framework, involving stakeholders such as industry developers, healthcare organizations, clinicians, and, most importantly, patients and/or their representatives, promotes proactive and practical actions to help ensure safe and ethical use of AI-MI off-label. By adopting this approach, stakeholders can balance innovation with safeguards, advancing patient-centered care while mitigating potential harms associated with off-label AI-MI use. This framework is crucial as AI-MI becomes increasingly integral to clinical decision-making processes.
15 May 2026
Read appraisal →Scientific reports
Exploring the psychological and physiological effects of a virtual reality-based bicycle exercise in hemodialysis patients: a randomized controlled trial
This study aimed to evaluate the efficacy of a virtual reality (VR)-based bicycle exercise in psychological distress (depression and anxiety) and selected biochemical parameters among patients receiving maintenance hemodialysis (MHD). A total of 70 patients from the dialysis station at Changzhou Medical District of the No. 904th Hospital were randomly allocated into study group(VR-based bicycle exercise, n = 35) and control group(routine nursing care, n = 35). The depression and anxiety levels were assessed by Self-rating Depression Scale (SDS), and Self-rating Anxiety Scale (SAS) respectively. Physiological indicators, including serum creatinine (Scr) and blood urea nitrogen (BUN) levels, were also analyzed. Following the intervention, the study group exhibited significant reductions in SDS and SAS scores (both P < 0.05). The levels of Scr and BUN in peripheral blood were also observed to be significantly decreased in study group (P < 0.05). Correlation analysis revealed a significant positive correlation between SAS/SDS scores with both Scr and BUN levels (P < 0.05 or P < 0.01). Furthermore, regression models identified SAS scores as significant predictors of Scr and BUN levels, accounting for 27.8% and 31.9% of their variance, respectively (P < 0.05 or P < 0.01). VR-based bicycle exercise can improve psychological well-being, and was associated with beneficial changes in pre-dialysis serum biomarkers (Scr and BUN) in MHD patients. This integrated intervention represents a promising non-pharmacological strategy to complement standard care in MHD patients.
15 May 2026
Read appraisal →BMJ open
Performance of large language models (GPT-5.2, Gemini 3 Pro, Claude Sonnet 4.6 and Grok 4.1) on the Fellowship of The Royal College of Surgeons Urology Part A examination.
OBJECTIVES: To assess and compare the performance of four contemporary frontier large language models (LLMs)-GPT-5.2 (OpenAI), Gemini 3 Pro (Google DeepMind), Claude Sonnet 4.6 (Anthropic) and Grok 4.1 (xAI)-on a simulated Fellowship of The Royal College of Surgeons Urology (FRCS(Urol)) Part A examination, evaluating overall accuracy, subspecialty-level performance, output consistency and response time. DESIGN: Controlled comparative evaluation study using a standardised simulation framework with repeated independent testing runs per model. SETTING: All models were accessed via their respective consumer-facing interfaces. No clinical setting or patient data were involved. Testing was conducted under uniform conditions with conversational memory disabled across all sessions. PARTICIPANTS: Four large language models were evaluated. No human participants were involved. Models were selected to represent the current frontier of publicly accessible LLMs from four distinct commercial developers. No models were excluded following selection. INTERVENTIONS: Each model was presented with 240 FRCS (Urol) Part A single best answer questions, mapped to the Joint Committee on Intercollegiate Examinations' Urology Syllabus Blueprint (2023). A standardised prompt was delivered at the start of each session. Each model completed five independent examination runs. No fine-tuning or system-level modification was applied to any model. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was overall examination accuracy for each model, benchmarked against an indicative pass threshold for the FRCS (Urol) Part A examination. Secondary outcomes were performance across 18 individual urology subspecialty topics; response time reported as mean total and per-question elapsed time; and consistency of performance quantified by SD and 95% CIs derived from a sequential Monte Carlo sampling procedure. All outcomes were prospectively planned and fully measured as specified. RESULTS: Three of four models exceeded the indicative 74% pass threshold: Gemini 3 Pro (82.4%±0.9%; 95% CI 81.3 to 83.6%), Claude Sonnet 4.6 (79.3%±1.1%; 95% CI 77.9 to 80.6%) and GPT-5.2 (76.1%±2.4%; 95% CI 73.1 to 79.1%). Grok 4.1 failed (70.4%±0.6%; 95% CI 69.6 to 71.2%), with its entire CI below 74%. All models completed the assessment in under 3 min. Strong performance was observed in research methodology (90-98%) and andrology (92-98%), with the weakest results in paediatric urology (38.7-54.7%) and testicular cancer (48.2-67.3%). Substantial within-model output instability was identified across several domains, most notably GPT-5.2 in female urology (SD±22.8%) and anatomy (SD±14.2%). CONCLUSIONS: Three of four frontier LLMs achieved scores consistent with passing the FRCS (Urol) Part A examination, representing a substantial advance since ChatGPT-3.5. Aggregate accuracy alone, however, obscures important subspecialty weaknesses and output instability. LLMs should be regarded as adjunctive revision aids rather than authoritative knowledge sources and always used alongside expert-led teaching. Future work should evaluate performance on Part B and viva-style assessments.
15 May 2026
Read appraisal →BMJ mental health
Emulated trial of artificial intelligence use and subsequent depressive outcomes in a survey of US adults
BACKGROUND: Generative artificial intelligence (AI) use has been suggested to have adverse mental health consequences but a causal relationship has not been examined. OBJECTIVE: To simulate a randomised controlled trial of AI use in a work, school or personal context by applying target trial emulation to multiple waves of data from a nationally representative survey. METHODS: We conducted a target trial emulation using non-probability survey data from three waves of a nationally representative survey conducted between 18 June 2024 and 8 January 2025. Participants aged ≥18 years reported generative AI use frequency at baseline. High-frequency use was defined as multiple times per week or more. The primary outcome was depressive symptom severity measured using the Patient Health Questionnaire 9-item (PHQ-9) at follow-up. Generalised causal forests assessed heterogeneity of treatment effects. FINDINGS: Among 19 099 participants assessed at baseline, 2862 (15.0%) reported AI use at least multiple times per week. A subset of 3109 (16.3%) returned for follow-up. In the primary weighted analysis, high-frequency use was not significantly associated with change in PHQ-9 score at follow-up (mean difference -0.18, 95% CI -0.94 to 0.59; p=0.65). Multiple sensitivity analyses using alternate outcome definitions also did not identify significant causal effects. Generalised causal forests yielded no significant evidence of heterogeneity of effect (p=0.81). CONCLUSIONS: In an emulated randomised trial among US adults, generative AI use was not associated with subsequent depressive symptoms. This result does not support the premise that AI use causes greater depressive symptoms, although adverse outcomes among vulnerable individuals cannot be excluded. CLINICAL IMPLICATIONS: AI use is unlikely to cause increased depressive symptoms among most US adults. Continued monitoring should clarify potential risks among vulnerable populations.
15 May 2026
Read appraisal →PloS one
Classification of soft decision-making methods via fuzzy parameterized fuzzy soft matrices and their performance-based statistical analysis in machine learning
This study provides a comprehensive evaluation and classification of 35 soft decision-making (SDM) algorithms based on fuzzy parameterized fuzzy soft matrices (fpfs-matrices). Although fpfs-matrices offer a strong mathematical framework for modeling uncertainty, there has been a lack of large-scale comparisons of their derivative SDM methods in machine learning. To address this, we used the Comparison Matrix-Based Fuzzy Parameterized Fuzzy Soft Classifier (FPFS-CMC) to benchmark these 35 algorithms across ten diverse datasets from the UCI Machine Learning Repository. The methods were thoroughly assessed utilizing metrics such as accuracy, precision, recall (sensitivity), specificity, and F1-score, and statistical significance was confirmed using the Friedman and Nemenyi tests. Our results show that SDM methods via fpfs-matrices perform competitively in classification tasks involving uncertainty. Notably, the best algorithms according to F1-scores were A19 (Rank 1), YHX14 (Rank 2), and a three-way tie for Rank 3 among VMH16, AKO18o, and A19/2. By identifying the most effective algorithms and offering a structured decision-support framework, this research provides both a theoretical reference and practical guidance for practitioners selecting SDM methods for complex machine learning challenges.
15 May 2026
Read appraisal →JMIR aging
Leveraging Multimodal Large Language Models for Fall Risk Reduction in Older Adults in the Home: Proposed Model Design
This research letter proposes a novel model design leveraging natively multimodal large language models to identify fall risks and generate visualizations of recommended home environmental modifications, aiming to improve the accessibility and impact of personalized fall prevention advice for older adults. Through a pilot rating study, this work demonstrates that multimodal large language models can generate safe and actionable advice to reduce fall risk in lived spaces of older adults, and also generate realistic edits based on original images. While this concept needs further testing and clinical comparison, it highlights a promising avenue for further innovation of fall prevention tactics.
15 May 2026
Read appraisal →Neuroscience
Recent trends in electrodermal activity signal processing and deep learning methods for emotion recognition
Electrodermal activity (EDA) has emerged as one of the most informative physiological signals for emotion recognition due to its close association with sympathetic nervous system activation. Although research on affective computing has expanded rapidly in recent years, systematic investigations of signal-processing techniques specifically tailored to EDA remain limited. This gap is significant because the selection of decomposition and signal processing strategies directly influences the accuracy, robustness, and interpretability of emotion recognition systems. Furthermore, defining emotion itself and tracing its physiological manifestations through EDA, from raw measurements to recognized affective states, remains an open challenge that highlights the need for methodological rigour. In this review, we synthesise developments published between 2018 and 2025, with a dual focus on advanced EDA signal processing methods and deep learning (DL) approaches for emotion recognition. We review common EDA acquisition sites, discuss challenges arising from non-stationarity and inter-subject variability. The review systematically compares time-domain, frequency-domain, time-frequency, and advanced time-series analysis techniques, alongside emerging end-to-end DL architectures for affective modelling. Unlike prior surveys that emphasise system-level design, this work adopts a signal-processing-centric and physiology-informed perspective. Additionally, we provide a structured comparative evaluation of widely used EDA decomposition methods using key performance metrics, offering a unified framework for method selection and interpretation. Overall, this review serves as a comprehensive resource for researchers and practitioners while advocating hybrid approaches that integrate the interpretability of classical signal processing with the predictive power of predictive methods.
15 May 2026
Read appraisal →PLoS computational biology
Teaching artificial intelligence through drug-drug interaction clustering analysis: Integrating project-based learning and large language models.
In recent years, artificial intelligence (AI) has increasingly influenced daily life and scientific research. Traditionally, AI-related courses have targeted computer science majors, while systematic instructional opportunities for early-stage undergraduates from non-computing backgrounds remain limited. To bridge this gap, we developed an AI course that integrates project-based learning with large language models (LLMs). Specifically, we designed four progressive assignments based on our research project (i.e., drug-drug interaction network clustering analysis). The course does not require prior knowledge of pharmacology or programming. Instead, LLMs are used as assistive tools to support programming, data analysis, and result interpretation. Students engage in a complete workflow, including data curation, algorithm implementation, and critical evaluation of results. Preliminary feedback shows that this approach supports the development of problem-solving skills and increases student engagement. This study provides a framework for integrating LLMs into project-based learning. We believe that this teaching proposal will be valuable and inspiring for educators seeking to design or enrich similar courses.
15 May 2026
Read appraisal →JMIR formative research
Automatic Speech Recognition and Large Language Models for Multilingual Pathology Report Generation: Proof-of-Concept Study
BACKGROUND: Accurate transcription of pathology gross examination dictation is important for clinical documentation, but multilingual dictation remains challenging in settings where clinicians mix Chinese and English while final pathology reports are written in English. OBJECTIVE: This study aimed to evaluate whether a Whisper-based automatic speech recognition (ASR) pipeline guided by contextual system messages and combined with open-source large language models (LLMs; Qwen2:72b, Llama3.1:70b, Gemma2:27b) could improve multilingual (Chinese-English) pathology dictation transcription accuracy and generate clinically appropriate English gross description reports. METHODS: We conducted a controlled proof-of-concept study using 125 simulated mixed Chinese-English pathology gross examination audio recordings created by physicians or pathologists. Audio recordings were transcribed using Whisper ASR with and without a contextual system message. The ASR transcripts were then converted into English gross description reports using 3 open-source LLMs: Qwen2:72b, Llama3.1:70b, and Gemma2:27b. Outcomes included character error rate, Bilingual Evaluation Understudy, Recall-Oriented Understudy for Gisting Evaluation (ROUGE)-1, ROUGE-2, ROUGE-L, Metric for Evaluation of Translation with Explicit Ordering, pathologist Win-Tie-Lose rankings, report-level error categories, inference time, and interrater agreement. RESULTS: The ASR contextual system message reduced the mean character error rate from 0.344 (SD 0.176; 95% CI 0.313-0.375) to 0.066 (SD 0.100; 95% CI 0.048-0.084; P<.001). Qwen2:72b achieved the highest automated metric scores, including a Bilingual Evaluation Understudy of 0.644 (SD 0.307), ROUGE-1 of 0.866 (SD 0.163), ROUGE-2 of 0.771 (SD 0.235), ROUGE-L of 0.842 (SD 0.178), and Metric for Evaluation of Translation with Explicit Ordering of 0.805 (SD 0.214). Pathologist-coded total error rates were 16.8% (21/125) for Qwen2:72b, 45.6% (57/125) for Llama3.1:70b, and 92.8% (116/125) for Gemma2:27b. The exact agreement between the 2 pathologists across full ranking categories was 76.8% (96/125; Cohen κ=0.668), and agreement on the top-ranked model or tied top group was 81.6% (102/125; Cohen κ=0.722). CONCLUSIONS: In this proof-of-concept evaluation, contextual prompting improved ASR transcription accuracy, and Qwen2:72b generated the most accurate English pathology reports among the evaluated LLMs. However, the study used simulated audio recordings, a local vocabulary prompt, and report-level rather than term-level clinical annotation. LLM-generated reports should therefore be considered draft documentation requiring pathologist verification, and prospective validation in real clinical workflows is needed before clinical deployment.
15 May 2026
Read appraisal →Journal of medical Internet research
Stimulating Future-Oriented Thinking and Goal-Achievement Through the Future Self Using Virtual Reality and a Smartphone App: Randomized Controlled Trial
BACKGROUND: Research suggests that identification with one's future self-encompassing vividness, connectedness, and valence-plays a key role in motivating future-oriented choices and goal pursuit. Interventions aiming to strengthen future self-identification have been shown to reduce maladaptive behaviors and promote well-being, but traditional approaches often rely heavily on imagination. Emerging technologies offer novel opportunities to make the future self more vivid and tangible, potentially reducing cognitive burden, and enhancing intervention effectiveness. OBJECTIVE: This randomized controlled trial evaluated the effectiveness of a digital intervention on the following primary outcomes: future self-identification, future orientation, consideration of future consequences, self-defeating behavior, goal commitment, and goal achievement. METHODS: In this parallel, 3-arm randomized controlled trial, 321 first-year students from a large public university in the Netherlands were randomized (1:1:1) with blocks of 9 to a smartphone app-based future-self intervention, an immersive virtual reality (VR) version of the same intervention, or an active goal-setting control condition. Participants assigned to the smartphone or VR condition engaged with the intervention over a 3-week period, during which they interacted with their 10-year-old self, their future self. Participants in the control condition received no further support. Each of the 3 conditions consisted of 107 participants, who were all included in the analyses following the intention-to-treat principle. Due to the nature of the intervention, blinding was not possible. RESULTS: Compared to the goal-setting control condition, both intervention conditions yielded significant short-term improvements on all 3 aspects of future self-identification (smartphone: dvividness=0.49; dvalence=0.44; dconnectedness=0.43; VR: dvividness=0.35; dvalence=0.44; dconnectedness=0.43), and a small decline in vividness (smartphone: d=-0.36; VR: d=-0.23) and connectedness (smartphone: d=-0.36; VR: d=-0.32) at 6-month follow-up. Additionally, the intervention buffered declines in future orientation during the study period (smartphone: d=.16; VR: d=0.18), and VR delivery led to significantly higher weekly goal achievement (d=0.88). However, effects on future orientation were not sustained over time (smartphone: d=-0.18; VR: d=-0.25), resulting in similar levels across conditions at 6-month follow-up. Furthermore, no significant effects emerged for other primary or secondary outcomes, such as self-defeating behavior (smartphone: doverall=-0.10; VR: doverall=-0.03), impulsivity (smartphone: doverall=-0.07; VR: doverall=-0.09), or academic performance (η2partial=0.00). No adverse events were reported in any of the conditions. CONCLUSIONS: This randomized controlled trial innovatively compares smartphone and immersive VR future-self interventions. Findings suggest that digital interventions leveraging visual and interactive representations of the future self can strengthen future self-identification and future orientation, and support (short-term) goal pursuit. Together, they highlight the potential of scalable digital future self approaches for educational and preventive health contexts.
15 May 2026
Read appraisal →Journal of medical Internet research
Plain Language Summarization of Environmental Health Research Using Generative AI: Community-Engaged Qualitative Study
BACKGROUND: Plain language summaries (PLSs) are increasingly required in environmental health publications to improve public accessibility. Generative artificial intelligence (AI) systems such as large language models can automatically produce such summaries; however, automated summaries often overlook local context and cultural relevance-limitations that are critical in environmental health research, where affected communities face disproportionate exposure and health risks. OBJECTIVE: This study aimed to develop and refine community-informed prompts for generating PLSs of environmental health research using generative AI. METHODS: We conducted a community-engaged qualitative study in Louisville, Kentucky, involving workshops with 97 participants from 4 stakeholder groups: summer interns at a local social justice nonprofit organization, participants in a youth development program, participants in a faith-based community organization, and members of a community advisory board focused on environmental justice. Participants reviewed PLSs generated using 3 different prompt styles in GPT-4o (ChatGPT, OpenAI). Feedback was collected through structured discussions and facilitator notes. Data were analyzed using thematic analysis informed by knowledge translation frameworks to identify preferred structural, linguistic, and contextual features of the summaries. RESULTS: Participants consistently preferred summaries between 300 and 400 words, written at a sixth- to eighth-grade reading level. Key priorities included presenting definitions before findings, using headings and bullet points to improve readability, and clearly explaining real-world and environmental justice implications. Narrative summaries without structure were viewed as overly long and difficult to interpret, while purely bullet-based formats were considered too simplified. Feedback from youth participants emphasized clarity and practical relevance, while faith-based participants highlighted the importance of trust and contextual framing. These insights informed the development of a community-refined prompt that included definitions, key findings, an introduction, and a concluding section on community implications. CONCLUSIONS: Community-engaged prompt development may improve the relevance and interpretability of AI-generated PLSs of environmental health research. Incorporating stakeholder perspectives into prompt design offers a replicable strategy for improving research translation and ensuring AI-generated summaries reflect the informational needs of affected communities.
15 May 2026
Read appraisal →Journal of medical systems
When Chatbots Become Agents: The Next Phase of Healthcare AI
Healthcare artificial intelligence (AI) has moved beyond answering medical questions. In early 2026, OpenAI, Anthropic, and Google launched agentic systems that retrieve evidence, use tools, and execute multi-step workflows. These systems can integrate information across multiple clinical knowledge domains within a single workflow, but the evidence base for evaluating their reliability in clinical practice has not kept pace with deployment. This perspective argues that healthcare is adopting a technology evolving faster than anyone can evaluate, and that a brief window exists to shape integration before the architecture becomes entrenched. These systems share common architectural principles, yet only 19 prospective trials exist among over 4,600 clinical large language model studies, none evaluating agentic pipelines. The architecture is locking in, professional roles are being redefined, and builders hold a structural information advantage. Transparency, adaptive governance, and broader participation are needed before these choices become irreversible, and to ensure that the boundary between human clinical judgment and automated capability is drawn in a way that strengthens both. Every stakeholder in healthcare must engage with this shift now.
15 May 2026
Read appraisal →Journal of cross-cultural gerontology
Mapping the Use of Information and Communication Technologies in Health Interventions for Older Adults: A Rapid Review
This rapid review mapped how information and communication technologies (ICTs), particularly digital health applications, are being used within healthcare services for adults aged 55 years and older. Following the Joanna Briggs Institute methodology and guided by the Population-Concept-Context framework, studies published between 2016 and 2024 were identified and reported in accordance with the PRISMA extension for scoping reviews, based on a previously published protocol registered on the Open Science Framework. Nineteen studies were included after critical appraisal. The findings indicate that ICT use is most frequently associated with improved accessibility and coordination of healthcare services, support for health professionals' training and care management practices, and positive health-related outcomes for older adults, particularly in the domains of mental health, social participation, and self-care. However, the evidence remains heterogeneous and often limited in its reporting of design and implementation processes, highlighting the need for further context-sensitive research that addresses ethical, equity, and implementation considerations in the use of digital health technologies for older populations.
14 May 2026
Read appraisal →Current heart failure reports
Emerging Artificial Intelligence Tools for the Screening of Structural and Valvular Heart Disease
PURPOSE OF REVIEW: Structural heart disease (SHD) encompasses diseases involving the heart valves, chambers, walls, and muscles. Current diagnostic methods have limited accessibility and predictive value. This review aims to present recent advances in artificial intelligence (AI)-guided tools in the screening of SHD and valvular heart disease (VHD), and to present challenges and opportunities for their use in clinical practice. RECENT FINDINGS: AI-guided models trained on ECGs, chest X-rays, and coronary artery calcium scans have a high accuracy in the diagnosis of SHD, heart failure, low left ventricular ejection fraction, and VHD. Some of these models can highlight the signals that influence their predictions, improving explainability. The use of AI in screening for SHD and VHD could lead to earlier diagnosis, enhanced accuracy, and better accessibility. However, outcome data on earlier diagnosis using these tools is required before broad deployment.
14 May 2026
Read appraisal →JMIR mental health
Machine Learning for Comparative Antidepressant Selection in Major Depressive Disorder: Systematic Review
BACKGROUND: Major depressive disorder (MDD) affects approximately 1 in 6 adults during their lifetime, yet antidepressant selection relies predominantly on trial-and-error, with response rates of only 42% to 53%. While machine learning (ML) models have shown promise in predicting treatment outcomes, most focus on single treatments rather than comparative selection across therapeutic alternatives, limiting their clinical utility for the medication choice decisions that clinicians face in practice. OBJECTIVE: This systematic review evaluates ML approaches that examine 2 or more pharmacological interventions for predicting treatment outcomes in MDD, with a focus on their capacity to facilitate comparative treatment selection between medications or medication classes for individual patients. METHODS: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we searched PubMed, Scopus, and Web of Science for studies published from 2015 to 2025. We included studies involving adults with MDD that used ML models to predict treatment outcomes across 2 or more pharmacological treatments and reported medication-specific prediction outcomes. Risk of bias was assessed using PROBAST-AI (Prediction Model Risk of Bias Assessment Tool for Artificial Intelligence). We conducted a narrative synthesis organized by modeling strategies, data integration approaches, validation methodologies, and performance patterns. RESULTS: From 5370 initial records, 19 studies met the inclusion criteria, with dataset sample sizes ranging from 49 to 77,226 participants. Studies employed 3 distinct modeling strategies: drug-specific supervised models trained independently for each medication, subtype- or trajectory-based approaches using clustering methods to identify differential response patterns, and a unified differential prediction framework generating calibrated cross-treatment predictions. Performance varied substantially, with area under the curve values ranging from 0.59 to 0.95 and classification accuracies between 62% and 95.4%, though high performance was concentrated in studies with small samples, high-dimensional neurobiological features, and internal-only validation. Only 7 studies conducted external validation, which generally yielded more conservative performance estimates. Feature informativeness was more consistently associated with performance variation than algorithm complexity. Most studies did not formally distinguish between prognostic features predicting general outcomes and predictive features identifying differential medication responses, and none applied formal explainability techniques. CONCLUSIONS: ML for comparative antidepressant selection remains in an early stage of development. Only 1 study implemented a unified framework directly supporting patient-level treatment ranking. Key barriers to clinical translation include insufficient distinction between prognostic and predictive markers, limited cross-trial validation, near-absent calibration reporting, and absent explainability. Future research should prioritize unified comparative frameworks with calibrated predictions, rigorous external validation on diverse cohorts, explicit modeling of heterogeneous treatment effects, and integration of explainability into model development.
14 May 2026
Read appraisal →JMIR nursing
Exploring the Impact of Electronic Medical Record-Enabled Versus Paper-Based Systems on the Quality of Nursing Handover: Comparative Case-Study
BACKGROUND: Ineffective clinical handover has the potential to compromise patient safety and quality of care. Standardizing the handover process is a widely adopted improvement strategy intended to reduce failures of information transfer. By enabling real-time access to patient information, electronic medical records (EMRs) could address communication issues inherent to nursing handover. OBJECTIVE: This case study sought to compare the quality of nursing handover occurring at EMR-enabled sites with that occurring at paper-based sites, within a single Australian public health service. METHODS: A comparative case study design was used, using quantitative data collected from observational audits of 60 handovers and posthandover surveys conducted in EMR-enabled and paper-based ward environments. Handover quality was measured through compliance with the organization's Clinical Handover Standard and staff-reported perceptions, enabling comparison between cohorts. RESULTS: Compared with paper-based wards, EMR-enabled wards demonstrated more consistent communication of clinical alerts and risks and fewer interruptions, whereas paper-based wards showed higher rates of bedside handover and patient engagement. CONCLUSIONS: EMR implementation alone does not ensure high-quality nursing handover. EMR interface design and functionality may act as a barrier to bedside handover and patient engagement, and contribute to continued reliance on paper-based artifacts. Targeted EMR design and nursing informatics-led optimization are required to better support nursing handover as a complex and cognitively challenging communication process.
14 May 2026
Read appraisal →PloS one
TDGN: A text-guided dual-gated network for multimodal sentiment analysis
Multimodal Sentiment Analysis (MSA) aims to interpret emotions by integrating textual, acoustic, and visual information. However, the heterogeneous quality and weak correlation among nonverbal modalities often lead to unstable alignment and ineffective fusion. To address these challenges, we propose a Text-Guided Dual-Gated Network (TDGN) that introduces a hierarchical gating and text-anchored contrastive learning framework. During the alignment phase, a Text-Anchored Gated Attention (TGA) module employs text as a semantic anchor to guide fine-grained alignment between audio and visual modalities while suppressing noise and emphasizing salient emotional cues. For the fusion stage, a Dual-layer Gated Fusion (DGF) module performs intra-modal gating to refine modality-specific features and inter-modal gating to dynamically balance cross-modal contributions. Furthermore, we introduce a Text-Anchored Contrastive Learning (TACL), which constructs contrastive targets based on textual similarity anchors, ensuring the fused features maintain both modal consistency and modal diversity. Extensive experiments on the CMU-MOSI and CMU-MOSEI benchmarks demonstrate that TDGN achieves state-of-the-art performance, achieving higher accuracy and robustness. Ablation studies and visualization further validate the effectiveness of each component.
14 May 2026
Read appraisal →Heart (British Cardiac Society)
Individual hearts: computational models for improved management of cardiovascular disease
Cardiovascular disease remains a leading cause of morbidity and mortality worldwide, with conventional management often applying standardised approaches that struggle to address individual variability in increasingly complex patient populations. Computational models, both knowledge-driven and data-driven, have the potential to reshape cardiovascular medicine by offering innovative tools that integrate patient-specific information with physiological understanding or statistical inference to generate insights beyond conventional diagnostics. This review traces how computational modelling has evolved from theoretical research tools into clinical decision support systems that enable personalised cardiovascular care. We examine this evolution across three key domains: enhancing diagnostic accuracy through improved measurement techniques, deepening mechanistic insights into cardiovascular pathophysiology and enabling precision medicine through patient-specific simulations. The review covers the complementary strengths of data-driven approaches, which identify patterns in large clinical datasets, and knowledge-driven models, which simulate cardiovascular processes based on established biophysical principles. Applications range from artificial intelligence-guided measurements and model-informed diagnostics to digital twins that enable in silico testing of therapeutic interventions in the digital replicas of individual hearts. This review outlines the main types of cardiovascular modelling, highlighting their strengths, limitations and complementary potential through current clinical and research applications. We also discuss future directions, emphasising the need for interdisciplinary collaboration, pragmatic model design and integration of hybrid approaches. While progress is promising, challenges remain in validation, regulatory approval and clinical workflow integration. With continued development and thoughtful implementation, computational models hold the potential to enable more informed decision-making and advance truly personalised cardiovascular care.
14 May 2026
Read appraisal →Journal of medical systems
VR-Based Physical Activity and Self-Management Interventions for Glycemic Control and Behavioral Outcomes in Type 2 Diabetes: A Systematic Review and Meta-Analysis
A growing body of original research has recently investigated virtual reality (VR)-based interventions for physical activity and self-management, yet yielded inconsistent results. This systematic review and meta-analysis aims to evaluate the effectiveness of VR-based physical activity and self-management interventions in improving glycemic control and modifying health-related behaviors among patients with type 2 diabetes mellitus (T2DM). A total of 10 studies involving 1,005 adults with T2DM were included, comprising 5 randomized controlled trials (RCTs), 1 non-randomized controlled trial (nRCT), and 4 self-controlled before-after studies. Of these, participants in the intervention groups received VR-based interventions, including immersive or interactive VR programs for diabetes self-management education or physical activity promotion, with most intervention durations ranging from 8 to 24 weeks. Control groups received traditional approaches, including routine care, in-person diabetes self-management programs, and content-equivalent 2D websites. The pre-post intervention analysis revealed that glycated hemoglobin (HbA1c) levels decreased significantly post-intervention (mean difference (MD) = -0.50, 95% CI: -0.92 to -0.08, p = 0.02). While VR-based physical activity and self-management interventions did not demonstrate superior efficacy to traditional intervention methods in glycemic control or behavioral outcomes, their unique advantages - including enhanced adherence, greater accessibility, and reduced spatiotemporal constraints - position them as a viable alternative for diabetes management. Nevertheless, the field requires more rigorously designed RCTs to establish efficacy and a systematic exploration of VR implementation protocols to maximize therapeutic benefits.
14 May 2026
Read appraisal →JMIR research protocols
Technology-Based Prehabilitation for Patients With Cancer Before Elective Treatment: Protocol for a Scoping Review
BACKGROUND: Advances in cancer treatment have improved survival rates; however, patients continue to experience significant treatment-related side effects, leading to reduced quality of life. Prehabilitation is an intervention that occurs before treatment and can improve patients' functional capacity, recovery, and well-being through exercise, nutrition, and psychological support. Typical hospital-based prehabilitation is not accessible to all patients due to geographical, socioeconomic, and time-related barriers. Technology-based approaches, including eHealth and mobile health (mHealth) interventions, may overcome these barriers by enabling remote, patient-centered delivery. However, the current evidence base is heterogeneous and lacks synthesis regarding feasibility, acceptability, and outcomes. OBJECTIVE: This protocol for a scoping review aims to outline how we will systematically map and synthesize the evidence on technology-based prehabilitation interventions for people with cancer to identify intervention designs, assess feasibility and accessibility, and highlight knowledge gaps to guide future research and practice. METHODS: The review will follow the Joanna Briggs Institute (JBI) methodology and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. A 3-step search strategy will be applied across multiple databases and gray literature sources. Eligible studies will include adults (aged ≥18 years) with a cancer diagnosis who are scheduled for elective treatment (surgery, radiotherapy, chemotherapy, immunotherapy, or hormone therapy). Interventions must involve eHealth or mHealth approaches supporting unimodal or multimodal prehabilitation activities such as exercise, nutrition, psychological support, or lifestyle modification. Outcomes of interest include functional fitness, quality of life, psychological well-being, treatment preparedness, recovery, adherence, and feasibility. Two independent reviewers will conduct title, abstract, and full-text screening, with disagreements resolved through discussion or consultation with a third reviewer. Data will be charted and presented in tables and figures and as a narrative synthesis. Critical appraisal using JBI tools will contextualize methodological quality but not exclude studies. Risk of bias will be assessed using the Cochrane Risk of Bias Tool for Randomized Trials version 2 (RoB 2) and Risk of Bias in Non-Randomized Studies of Interventions version 2 (ROBINS-I V2) tool. This will not be used to exclude studies, but to determine the quality of articles included. RESULTS: The search strategy has been pilot tested and finalized. Database searches are scheduled to commence in March 2026, with study selection and screening anticipated to be completed by April 2026. Data analysis and synthesis are expected to begin in May 2026, and final results will be available by October 2026. CONCLUSIONS: This protocol outlines a rigorous and transparent approach to mapping the current evidence on technology-based prehabilitation in cancer care. By systematically characterizing intervention features, outcome domains, and evidence gaps, the review will provide an up-to-date evidence map to guide future research priorities, inform clinical implementation, and support the development of more standardized and inclusive prehabilitation pathways.
14 May 2026
Read appraisal →JMIR nursing
From Concept to Practice: Lessons From the Balanced Nursing Teams Decision-Support System
The global nursing workforce crisis demands a shift from reactive staffing to strategic workforce optimization through data-driven decision-support systems. This viewpoint paper reflects on the development and attempted implementation of the balanced nursing teams system, a decision-support tool integrating approximately 250 data points-of which roughly 150 are extracted from existing organizational systems (human resources, scheduling, electronic health records, quality registries) through flexible import mechanisms, and the remainder collected through a built-in 360-degree staff survey with automated analysis-across 10 domains to evaluate nursing team balance between capacity, performance, and outcomes. Following crowdfunding by 18 Belgian health care organizations, balanced nursing teams were implemented across 8 diverse settings (home health care, general hospitals, academic centers) between 2019 and 2023. Using the Human-Organization-Technology fit framework, we analyze why evidence-informed, organization-endorsed digital innovations struggle to achieve adoption. Our analysis reveals 3 interdependent barrier categories: technological fragmentation (vendor lock-in, legacy systems, prohibitive integration costs), organizational siloing (Chief Nursing Officers [CNOs] lacking budgetary authority, nursing framed as peripheral to strategic priorities), and managerial hesitance (fear of punitive data use, cognitive overload from staffing crises). These barriers were worsened by the substantial data-integration burden that the system's breadth imposed on organizations with limited digital maturity. Critically, only one site (ie, a nurse-led home health care organization where leadership held both strategic authority and resource control) achieved sustained implementation. This contrast demonstrates that workforce optimization through data depends not on software maturity alone, but on achieving simultaneous fit across human, organizational, and technological domains. We argue that the persistent marginalization of nursing leadership within hospital governance structures represents the fundamental barrier to digital transformation in nursing workforce management. The urgency paradox is striking: while nursing represents health care organizations' highest operational cost and most direct patient interface, workforce optimization tools are consistently deprioritized in favor of regulatory compliance systems and billing infrastructure. Bridging this gap requires systemic investment in nursing leadership authority, data interoperability standards, and recognition that data-driven workforce decisions are strategic imperatives rather than operational luxuries.
14 May 2026
Read appraisal →PloS one
Comparing machine and deep learning models for pediatric anxiety classification using structured EHRs and area-based measures of health data
OBJECTIVE: This retrospective, case-control study with internal validation evaluates the performance of machine learning (ML) and deep learning (DL) models in classifying pediatric patients at risk for anxiety disorders using structured electronic health records (EHRs) and area-based measures of health (ABMH). The aim is to enable proactive care by monitoring potential anxiety onset across developmental stages. METHODS: We trained a series of ML models (Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, XGBoost) and DL models (LSTM, GRU, RETAIN, Dipole) using structured EHR data from 30-day windows prior to diagnosis. Two datasets were used per age group: one with structured EHR data only, and another including both EHR and ABMH data. ML models were trained using short-term cross-sectional features, while DL models leveraged full longitudinal patient histories. Performance was assessed using AUROC, AUPRC, PPV, NPV, F1 score, and accuracy. Due to differences in input scope, model performance reflects both algorithmic and temporal design differences and is not intended as a direct comparison between ML and DL. RESULTS: ML models offered strong baseline performance, with XGBoost achieving AUROC scores of 0.817 (EHR) and 0.816 (EHR+ABMH) for 8-year-olds. Adding ABMH features did not significantly improve performance. DL models, particularly RETAIN and Dipole, achieved the highest AUROC values (e.g., Dipole: 0.853 with EHR, 0.857 with EHR+ABMH for 8-year-olds), outperforming other DL and ML models within their respective design constraints. CONCLUSION: Both ML and DL models successfully identified likely anxiety onset using structured EHR data. DL models using longitudinal data achieved the highest performance, while XGBoost provided a robust ML baseline. The minimal impact of ABMH features highlights integration challenges, and performance variation across ages emphasizes the need for age-stratified modeling approaches.
14 May 2026
Read appraisal →PloS one
Narrative-affect discrepancy as a regulated degree of freedom in 351,734 relationship narratives
In naturalistic emotional narratives, the intensity of expressed affect does not scale proportionally with narrative structure. Using 351,734 English-language relationship narratives from online support communities (the ANEST Narrative-Affect Dataset, ANAD v1.1.0), we constructed a two-dimensional expressive space defined by narrative complexity (N) and linguistically inferred affective intensity (A), with their signed discrepancy ([Formula: see text]) treated as a derived coordinate. Rather than converging toward low discrepancy, human narratives occupied a broad but structured space consistent with trade-offs between relational exposure and cognitive effort. We identified four empirically separable regimes of expressive organization: coupled expression (non-extreme discrepancy; the complement of the extreme regimes), strategic understatement (high A, low N, D < 0), strategic overstatement (high N, low A, D > 0), and collapse (high A with limited narrative scaffolding). A data-anchored cost model (NCS) formalized these regimes as arising from the interaction of exposure risk and cognitive effort, not from discrepancy minimization per se. Coupled expression dominated the corpus (91.3%), while the remaining regimes formed smaller but non-negligible subpopulations (Understatement: n = 20,223; Collapse: n = 8,040; Overstatement: n = 2,223), indicating that extreme discrepancy configurations occur systematically rather than as isolated outliers. As a comparative probe, we projected an RLHF-aligned large language model into the same space using matched prompts and identical feature extraction. Because D is a deterministic function of N and A, expressive extent was quantified as convex hull area in the clipped [Formula: see text] plane. Under this procedure, the model occupied a markedly smaller region (approximately [Formula: see text] smaller hull area; bootstrap 95% CI [1.68, 1.70]; permutation p < 0.0001) and concentrated near low-discrepancy configurations, with sparse occupancy of extreme under- and overstatement regimes. Together, these findings suggest that narrative-affect discrepancy is a measurable and regulated dimension of emotional expression and provide a reproducible geometric basis for comparing expressive degrees of freedom across populations and systems.
14 May 2026
Read appraisal →European journal of pediatrics
Wheeze detection in real-world pediatric care: AI applied to smartphone lung auscultation.
UNLABELLED: Smartphone-based lung auscultation, combined with artificial intelligence (AI), may offer a promising alternative for pediatric respiratory telemonitoring. The aim of our study was to evaluate the performance of an AI model on wheeze detection from pediatric respiratory sounds recorded via smartphone. An observational cross-sectional study was conducted at the Pediatric Department of a Portuguese tertiary hospital, including children aged 0-17 years (pre-school, school-aged, and adolescent), with or without respiratory disease. Respiratory sounds were recorded at four locations using smartphone microphones and independently classified by at least 2 blinded annotators for quality and presence of wheezes. A hybrid convolutional-recurrent neural network (CNN + LSTM) trained on public electronic stethoscope databases was used to detect wheezes. AI model performance was assessed using positive predictive value, sensitivity, specificity, accuracy, and F1-score. A total of 217 children (59.9% male; median 10 [Q1-Q3 4.5-13] years) were included. From 2020 respiratory sound recordings, 1500 (74.3%) met quality criteria. Manual annotation identified 271 recordings with wheezes, whereas AI detected 217, most frequently in pre-school children (64.5% AI; 53.9% manual). The model achieved an overall accuracy of 87% (95% CI 86-89) and an F1-score of 61% (95% CI 56-66). The best accuracy was found in adolescents (92%, 95% CI 90-94) and the best global F1-score in pre-school children recordings (64%, 95% CI 58-71). CONCLUSION: This study supports the feasibility of AI-assisted analysis of smartphone-recorded pediatric respiratory sounds in real-world settings. Future multicenter studies with larger datasets and model fine-tuning using smartphone recordings are expected to improve AI model performance and generalizability. WHAT IS KNOWN: • Continuous monitoring of pediatric respiratory diseases is essential to improve outcomes and reduce healthcare and family burden. • Smartphone lung auscultation is feasible in children but AI wheeze detection models have only been applied to recordings from electronic stethoscopes. WHAT IS NEW: • First study applying an AI model to detect wheezes in smartphone-recorded lung sounds. • Quality recordings were obtained across all pediatric age groups and the AI model performance showed promising results, comparable to studies using electronic stethoscopes.
14 May 2026
Read appraisal →Sleep
Effects of a guided digital intervention on sleep and mental health outcomes in university students - a randomized controlled trial
STUDY OBJECTIVES: To evaluate the effectiveness of a guided digital intervention targeting sleep and circadian rhythms in university students. METHODS: We randomized 195 university students with self-reported insomnia (Insomnia Severity Index ≥10) from nine Dutch Universities to receive either the 5-week guided digital cognitive-behavioral therapy for insomnia "i-Sleep & BioClock", or digital sleep psychoeducation and sleep monitoring. Online assessments were at baseline, 3 (mid-treatment), 6 (post-treatment), and 18 (follow-up) weeks. The primary outcome was insomnia severity at 6 weeks. Secondary outcomes were symptoms of depression and anxiety; functioning; quality of life (QoL); academic performance; and sleep and light exposure diary outcomes. RESULTS: From Nov 1st 2023 until Sept 5th 2024, we randomly assigned 96 students (age 23.8 ± 3.8, 75.4% female) to the intervention and 99 to the control condition, stratified by university and gender. Fifty-one percent completed the post-treatment assessment, and 30% the follow-up. 31% were intervention completers. Linear mixed models showed no significant time x treatment interaction for insomnia severity ("i-Sleep & BioClock": baseline ISI M = 16.8, SD = 4.0; post-treatment ISI M = 10.2, SD = 3.9; Digital psychoeducation: baseline ISI M = 16.5, SD = 3.6; post-treatment ISI M = 11.1, SD = 4.2). The difference in change between groups was -1.17 points (95% CI -2.72 to 0.38), p=.14, Cohen's d = -0.31. Most secondary outcomes showed no significant time x treatment effects. CONCLUSIONS: "i-Sleep & BioClock" was not more effective than online psychoeducation on most outcomes, possibly due to active elements present in both conditions or low adherence. Future research should identify factors for early disengagement and investigate which intervention components drive improvements in sleep. CLINICAL TRIAL: Effectiveness of a Guided Self-help Intervention for Improving Sleep in University Students, https://clinicaltrials.gov/study/NCT06023693, prospectively registered at ClinicalTrials.gov on August 3rd, 2023.
14 May 2026
Read appraisal →JMIR medical informatics
Scalable Identification of Clinically Relevant Chronic Obstructive Pulmonary Disease Documents in Large-Scale Electronic Health Record Datasets With a Lightweight Natural Language Processing Model: Retrospective Cohort Study
BACKGROUND: The widespread adoption of electronic health records has resulted in the generation of large volumes of clinical notes. Learning algorithms and large language models can be trained on these resources, but they are susceptible to noise-irrelevant or noninformative data. This sensitivity can lead to significant challenges, including performance degradation and the generation of inaccurate predictions or "hallucinations." This study addresses a critical challenge in clinical informatics: efficiently filtering millions of documents for relevance before advanced language model processing, particularly in resource-constrained environments. OBJECTIVE: We present a novel framework for determining document relevance in clinical settings using a chronic obstructive pulmonary disease (COPD) dataset. METHODS: We developed a novel framework using weak supervision and domain-expert heuristics to generate "silver standard" labels for training data and gold standard expert-annotated labels, creating 2 datasets to optimize the model during the development phase and subsequent testing phase. Various text representation techniques (bag of words, term frequency-inverse document frequency, lightweight document embeddings, compression-based features, and Unified Medical Language System concept extraction) were evaluated. These representations were used to train random forest, extreme gradient boosting, and k-nearest neighbor classifiers. Models were optimized on a small expert-annotated dataset and evaluated on a held-out test set. RESULTS: The combination of lightweight document embedding with a random forest classifier demonstrated the best performance, achieving a precision of 0.73, recall of 0.86, and F1-score of 0.80 (95% CI 0.76-0.87) for identifying relevant COPD documents. This significantly outperformed baseline heuristics (precision=0.70; recall=0.38; F1-score=0.50, 95% CI 0.43-0.56) and other tested methods. CONCLUSIONS: Our study presents a novel framework for identifying COPD-relevant clinical documents using lightweight embedding and machine learning. This approach effectively filters pertinent documents, enhancing information retrieval precision. The framework's scalability and minimal annotation needs make it promising for diverse health care applications, potentially optimizing clinical outcomes through efficient document selection for data-driven decision support systems.
14 May 2026
Read appraisal →Progress in biomedical engineering (Bristol, England)
Cross-subject generalization for EEG decoding: a survey of deep learning methods
Deep learning for cross-subject electroencephalography (EEG) decoding is hindered by high inter-subject variability, which introduces a severe domain shift between training and unseen test subjects. This survey presents a comprehensive review of deep learning methodologies specifically engineered to address this cross-subject generalization challenge. To ground this analysis, we formalize the cross-subject setting as a multi-source domain problem and delineate the rigorous, subject-independent evaluation protocols required for valid assessment. Central to this survey is a systematic taxonomy of the current literature into discrete methodological families, including feature alignment, adversarial learning, feature disentanglement, and contrastive learning. We conclude by examining three critical elements for advancing robust, real-world decoding: the theoretical limitations of current methodologies, the structural value of subject identity, and the emergence of EEG foundation models.
14 May 2026
Read appraisal →Biomedical physics & engineering express
Differentiating depression subtypes through three-electrode EEG and emotional stimuli: a machine learning approach
We propose an EEG-based framework for depression subtype assessment using emotion-modulated neural dynamics elicited by immersive virtual reality (VR). EEG was recorded from 70 participants (31 first depressive episode, FDE; 18 recurrent depressive episode, RDE; 21 control participants, HC) using a compact frontal montage (Fp1/Fpz/Fp2) during positive and negative VR conditions, focusing on the immediate post-stimulus regulation period. Subject-level features were analyzed with a linear mixed-effects model to probe group-by-condition interaction patterns, and a Bi-Emotional Siamese Network (BESN) was developed to model baseline-anchored within-subject deviations by fusing positive/negative reactivity streams with path-signature-based temporal encoding. Feature-level analyses revealed interaction signals suggestive of differential emotion-related modulation across groups. In subject-wise evaluation, BESN achieved 83.1% accuracy for HC versus FDE discrimination and 70.6% accuracy for three-class classification (HC versus FDE versus RDE), outperforming conventional machine-learning baselines. Robustness was further supported on an external public resting-state EEG dataset (MODMA), achieving 78.2% accuracy. These results suggest that baseline-anchored, emotion-modulated EEG dynamics combined with path-signature modeling provide an objective and generalizable computational approach for depression-related group discrimination.
14 May 2026
Read appraisal →JMIR mental health
Therapeutic Interventions Targeted at Problematic Use of Digital Technology: Systematic Review and Meta-Analysis of Evidence
BACKGROUND: Problematic use of digital technology has increased across the world. Despite growing research, evidence on treatment effectiveness across digital behaviors remains fragmented. OBJECTIVE: This study aimed to systematically evaluate and compare the effectiveness of therapeutic interventions targeted at problematic use of digital technology across various behavioral domains. METHODS: A systematic review and meta-analysis was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines (PROSPERO: CRD420251052442). Electronic searches of PubMed, Scopus, and Embase (up to April 2025) were conducted. It identified 125 eligible studies, including 73 randomized controlled trials (RCTs), 32 non-RCTs, 14 pre-post studies, and 6 pilot studies. The interventions that were assessed in these studies included psychological therapies, digital or web-based programs, exercise-based interventions, pharmacological treatments, neuromodulation, parent-focused programs, virtual reality-based interventions, educational programs, and multicomponent approaches. Random-effects meta-analyses using standardized mean differences (SMDs) were performed. RESULTS: For problematic internet use, psychological treatments showed a strong effect (effect size=-2.68; P<.001). Digital interventions also showed significant benefit (effect size=-1.16; P<.001). For smartphone addiction, psychological treatments (effect size=-1.49; P<.001) and exercise-based programs (effect size=-3.07; P=.001) showed significant improvement. For gaming disorder, psychological treatments showed improvement (effect size=-1.01; P=.02), but results were mixed. There were limited studies to calculate pooled results for social media addiction, pornography use, gambling, screen time, and over-the-top content watching. No treatment studies were found for problematic over-the-top content watching. High heterogeneity and evidence of small-study effects were observed in several studies. CONCLUSIONS: Overall, structured psychological therapies showed the most consistent benefit. These findings support structured interventions that aim for control of use and reduce cues linked to high use. Evidence remains limited for several emerging digital behaviors. More high-quality studies are needed in clinical settings and for less-studied forms of digital addiction.
14 May 2026
Read appraisal →JMIR aging
Self-Identified Age Cohorts and Personal Experience Sharing in Pseudonymous Online Spaces: Natural Language Processing Analysis of Reddit
BACKGROUND: While older adults' social media use has been widely studied for its instrumental benefits, such as accessing health information or maintaining family ties, research has largely focused on identity-based platforms that mirror offline social networks, leaving pseudonymous, interest-driven environments such as Reddit underexplored. Although older adults actively participate in these spaces to share personal narratives and engage beyond their existing social roles, the literature has yet to center their own voices, with most existing work focusing on caregivers or younger users discussing older adults rather than older adults speaking for themselves. OBJECTIVE: Integrating a life course perspective with the concept of subjective age cohort, this study examines how Reddit users who explicitly self-identify as being in their 50s, 60s, or 70s engage in self-expression and personal experience sharing within pseudonymous online spaces. METHODS: Using data collected via the Reddit application programming interface, this study analyzed posts and comments from 848 self-identified older Reddit users, including 488 future older adults in their 50s (57.5%) and 360 current older adults in their 60s and 70s (42.5%) identified through age-based flairs in the subreddit r/AskOldPeople. A fine-tuned large language model (BLOOM-560m; F1-score=0.96) classified 4,055,275 sentences into three personal experience domains: (1) health and wellness, (2) personal relationships and identity, and (3) professional and financial life. Chi-square analysis compared domain distributions across age groups. BERTopic topic modeling identified thematic patterns within each domain. RESULTS: Of 569,107 personal experience sentences identified, personal relationships and identity comprised the largest share (n=268,212, 47.1%), followed by professional and financial (n=186,768, 32.8%) and health and wellness (n=114,127, 20.1%). Chi-square analysis revealed significant between-group differences (χ22=34.7; P<.001): current older adults shared proportionally more about health and wellness, whereas future older adults shared more about relationships and identity. Topic modeling further revealed qualitatively distinct emphases within each domain. Future older adults' posts frequently discussed menopause, depression, and friendships, while current older adults more frequently addressed chronic illness, aging, and financial security. Both groups used pseudonymity to disclose sensitive or stigmatized topics. CONCLUSIONS: This study demonstrates that older adults' personal experience sharing on pseudonymous platforms is shaped by the simultaneous interplay of life course stage, age cohort background, and platform affordances, rather than by any single factor. Pseudonymous environments such as Reddit enable disclosures of health vulnerabilities, contested social identities, and emotional experiences that are structurally discouraged on identity-based networks, and this has direct implications for how digital inclusion is conceptualized and practiced. Beyond operational literacy, supporting older adults' expressive inclusion in interest-based, pseudonymous online communities represents a meaningful yet underaddressed dimension of digital participation policy.
14 May 2026
Read appraisal →Journal of medical systems
Artificial Intelligence in Physical, Occupational and Neuro-Rehabilitation: Clinical Effectiveness, Prognostic Performance, and Pre-Implementation Feasibility - A Systematic Review
BACKGROUND: Global rehabilitation needs far exceed capacity, and artificial intelligence (AI) is proposed to extend access, personalise therapy, and support adherence. We aimed to synthesise evidence on clinical effectiveness, prognostic performance, and implementation feasibility of AI-enabled rehabilitation across conditions and care settings. METHODS: We conducted a mixed-methods systematic review of AI-enabled rehabilitation and rehabilitation-led prevention relevant to physiotherapy practice. MEDLINE, Embase, Web of Science, CINAHL, Scopus, and IEEE Xplore were searched. Methodological quality was appraised with MMAT and PROBAST + AI (prediction/diagnostic models), with a priori AI-reporting minimums checklist. Given heterogeneity, evidence was integrated via prespecified thematic synthesis. FINDINGS: Thirty studies from diverse regions met inclusion, comprising randomised trials, non-randomised comparisons, cohorts, surveys, qualitative work, and prediction/diagnostic models. Clinical effects were modest and heterogeneous. Most AI-enabled interventions were comparable to conventional rehabilitation; signals of benefit appeared in selected musculoskeletal and telerehabilitation contexts but rarely persisted beyond short follow-up. Internal validity was frequently limited by asymmetric adherence measurement objective telemetry in AI arms versus self-report or attendance in controls alongside low uptake and declining engagement over time. Safety and usability were generally favourable within short horizons, although surveillance and explicit attribution to AI components were inconsistently reported. Prognostic and adaptive models showed encouraging discrimination in development settings but lacked multicentre external validation, calibration, subgroup-error profiling, and prospective impact evaluation, leaving them unready for clinical use. Stakeholders reported willingness to adopt AI while highlighting gaps in training, governance and information technology support, costs, and digital equity. Overall certainty for comparative clinical outcomes was low to moderate, for models, very low. CONCLUSION: AI in rehabilitation presently acts more as a behavioural amplifier structuring home programmes and supporting execution than as a replacement for dose-matched therapist-delivered care. Credible scale-up should position AI as an adjunct and hinge on arm-symmetric capture of adherence and execution, clinically meaningful outcomes paired with verified behaviour change over treatment and longer-term follow-up, and model deployment only after transparent development, external validation with calibration and subgroup-error characterisation, and demonstration of clinical impact. Embedding equity and economic evaluation, together with living oversight and version control, will be essential to convert promising prototypes into trustworthy, durable, and widely accessible services.
14 May 2026
Read appraisal →Physiological measurement
Turning glasses into sensors: capacitive facial expression recognition under frame geometry constraints
Objective.Wearable facial expression recognition requires sensing modalities that are unobtrusive, illumination-independent, and compatible with everyday eyewear form factors. Existing optical and electromyographic approaches are limited by line-of-sight requirements, skin contact, or restricted action unit (AU) coverage. This work investigates the feasibility of non-contact capacitive sensing for facial muscle activity monitoring within the strict geometric constraints of standard eyewear.Approach.We developed FERGlasses, integrating eight loading-mode capacitive channels via a custom flexible PCB and active shielding to detect skin-to-frame displacement. A validation study with 40 participants was conducted to elicit seven facial action coding system-defined AUs. Signal fidelity was assessed using independent Random Forest regressors and benchmarked against commercial optical-proximity (OCOsense) and computer-vision (Py-Feat) standards.Main results.The system demonstrated high agreement for upper-face activity, achieving median cosine similarities of 0.97 for the Outer Brow Raiser (AU02) and 0.87 for the Nose Wrinkler (AU09), while lower-face actions such as the Chin Raiser (AU17) yielded a median of 0.33, consistent with mechanical signal attenuation along the frame. Channel-ablation analysis quantified the specific anatomical contributions of each electrode group, identifying eyebrow and rhinion sensors as primary signal drivers for upper-face and perinasal actions, respectively, and temple sensors as critical for cheek raiser detection.Significance.This study provides the first metrological validation of multi-channel capacitive sensing for AU-level recognition in eyewear. The results establish the modality as a robust, privacy-preserving, and illumination-independent alternative to optical methods for longitudinal physiological monitoring, with the current prototype serving as a wired research platform and full consumer-grade integration identified as a future engineering objective.
14 May 2026
Read appraisal →PloS one
Framing artificial intelligence in Chilean digital press before and after the launch of ChatGPT: From concern to optimism
This article examines the evolution of AI framing in Chilean national digital media before and after the launch of ChatGPT in 2022. Through topic modeling and emotion analysis of 1,466 articles published by six media outlets over periods that begin between 2000 and 2014, depending on the outlet, and extend in all cases until 2024, this study identifies significant thematic and affective changes. In the pre-ChatGPT period, coverage combined recognition of AI's capabilities with concerns regarding labor displacement, governance, and human identity, frequently referencing global corporations and state institutions. In the post-ChatGPT period, topics became narrower in scope, with fewer actors and greater emphasis on universities and cultural organizations implementing AI. The focus shifted from the development of AI systems to their applications, predominantly framed in positive terms. At the same time, negative sentiment was largely confined to epistemological uncertainty. This transformation aligns with national surveys indicating growing public optimism, diverges from Global North debates centered on regulation and privacy, and converges with East Asian framings that are oriented toward innovation and creativity. These findings suggest that the launch of ChatGPT coincided with a reinforcement of a socio-technical imaginary of AI as inevitable and beneficial, yet potentially limiting the diversity of public debate.
14 May 2026
Read appraisal →Current nutrition reports
Digital Solutions for the Control of Lifestyle Parameters in Individuals With High Cardiometabolic Risk: a Scoping Review
BACKGROUND: Digital technologies are increasingly used to promote healthier behaviors and prevent noncommunicable diseases (NCDs). Diet and physical activity are key determinants of cardiometabolic health, where digital tools can support self-monitoring and behavior change. OBJECTIVE: This scoping review aimed to evaluate the effectiveness of digital health technologies in improving diet quality and adherence, assessing body composition, and promoting physical activity among adults at cardiometabolic risk. METHOD: A systematic search was performed in Web of Science and PubMed (2000-2025) following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) guidelines and the PICo framework. Inclusion criteria focused on digital solutions for diet and exercise in adults with overweight, obesity, or metabolic complications. RESULTS: 15 studies were included (8 randomized controlled trials, 2 pilot studies, and 5 validation studies). Most intervention studies reported significant improvements in weight reduction, body mass index, or waist circumference, especially when mobile applications or wearable devices were combined with personalized feedback. Validation studies confirmed a high reliability of AI-based image analysis tools for estimating body composition compared with DXA. Improvements in dietary adherence and physical activity levels were also observed in several studies. However, heterogeneity in study design, short follow-up duration, and variability in outcome measures limited comparability across studies. Major gaps included limited long-term evidence, scarce use of AI-driven personalized interventions, insufficient reporting of measurement validity, and underrepresentation of vulnerable populations. CONCLUSION: Digital health technologies, including mobile apps, wearables, and AI-based systems, show promising potential as a complementary tool to clinician-guided care. They can enhance dietary adherence, promote self-monitoring, and support healthier lifestyles, although their long-term effectiveness remains to be confirmed. Future research should prioritize long-term randomized trials, standardized outcomes measures, validation of AI-based tools in diverse populations, and structured integration of digital interventions into clinical pathways.
14 May 2026
Read appraisal →Scientific reports
Deep learning-based automatic field of view planning for prostate MRI in oblique coronal and oblique axial planes
Accurate field-of-view (FoV) prescription in oblique coronal and axial planes is essential for high-quality prostate MRI but remains operator-dependent and variable. We developed and evaluated a ResNet-based deep learning framework for automated FoV planning. In this retrospective multicenter study, FoV prescriptions were annotated on PI-CAI dataset. Three readers assessed intra- and inter-rater variability to establish reference consistency. Three neural network variants were trained on 1,474 examinations from PI-CAI dataset (2012-2021), and the optimal model was selected by internal validation. Generalizability and clinical utility were tested on three external cohorts totaling 530 examinations (2021-2024) using a non-inferiority design. The selected model achieved non-inferior performance for slice positioning, with differences ranging from 0.16 ± 0.99 to 0.37 ± 0.48. Across sites, FoV overlaps ranged from 82.4 ± 4.1% to 88.7 ± 6.0%, and the angle differences between predicted and reference planes were 4.66 ± 4.89° (Site I), 3.46 ± 2.80° (Site II), and 2.99 ± 2.90° (Site III). Clinical utility was high at all sites, with acceptability rates of 97.9%, 97.7%,98.8%, 98.1% and 98.1% for Site I (Raters 1-5), 95.7%, 97.8%, 100%, 95.7% and 97.8% for Site II (Raters 1-5), and 100% for all raters at Site III. These findings demonstrate the feasibility of automated FoV positioning for prostate MRI and indicate excellent clinical utility.
14 May 2026
Read appraisal →Scientific reports
Federated learning with swarm intelligence for efficient and secure medical image analysis
Collaborative learning in healthcare faces challenges, including strict regulations and fragmented data. This research introduces a federated learning framework that employs swarm intelligence to augment communication and enhance the analysis of medical images. The method optimizes hyperparameters, selects features, and assigns aggregation weights to federated clients simultaneously by combining Particle Swarm Optimization (PSO) and the Firefly Algorithm (FA) with deep Convolutional Neural Networks (CNNs). The framework was tested on three medical datasets: COVID-19 chest X-rays (5,856 images), monkeypox skin images (569 images), and breast cancer mammograms (320 images). These datasets were shared among four fake healthcare institutions. It strives to strike a balance between privacy, communication costs, and classification accuracy. The results showed that the test was 96.71% accurate in detecting COVID-19, 96.06% accurate in classifying monkeypox, and 97.0% accurate in diagnosing breast cancer. The framework was able to handle noise and attacks from individuals who sought to disrupt it, which reduced communication rounds by 25-30%. A privacy-utility analysis revealed that there were acceptable trade-offs, with accuracy remaining above 94%. This study employs robust privacy measures and statistical validation. It also shows how to use medical AI in smaller healthcare settings without putting patients' privacy at risk.
14 May 2026
Read appraisal →Journal of medical Internet research
AI-Based Automation for Medication Reconciliation: Scoping Review
BACKGROUND: Medication reconciliation (MedRec) has the potential to improve patient safety by enhancing the continuity of medication information across settings. MedRec involves 3 core tasks: the creation of a best possible medication history, the identification of medication discrepancies among medication lists, and the resolution of medication discrepancies. While artificial intelligence (AI) has the potential to improve MedRec, existing reviews have not identified the ways in which researchers have used AI to facilitate MedRec tasks and their constituent subtasks or the level of automation achieved. OBJECTIVE: This scoping review aimed to map how previous research has applied AI to MedRec tasks and subtasks and assess the extent of automation achieved. METHODS: We searched MEDLINE, Embase, Web of Science, IEEE Xplore, and Compendex in June 2024 for studies that used AI to support a MedRec task or subtask, excluding entirely rule-based tools or studies focused on other aspects of medication management. After screening 2345 unique records, we conducted backward citation searching of studies included at the full-text stage, identifying an additional 795 unique records. We used a 4-stage model of human information processing as a structural lens to guide our considerations of automation, mapping the core tasks of MedRec onto this model. RESULTS: A total of 94 studies met the inclusion criteria. All studies addressed subtasks related to the creation of a best possible medication history. Only 2.1% (n=2) of the studies also addressed the identification of discrepancies. Thus, the highest stage of automated information processing achieved was information analysis, although most studies (92/94, 97.9%) only automated information acquisition steps. Most studies (67/94, 71.3%) used free-text clinical notes from the electronic health record, although a significant proportion (21/94, 22.3%) used images of pills or images of other medication-related items. Studies using text-based data used a variety of machine learning methods (eg, recurrent neural networks, conditional random fields, support vector machines, and transformers), whereas those that leveraged images typically used convolutional neural networks. Most studies (61/94, 64.9%) used publicly available data from benchmarking datasets (eg, n2c2 2022) and were strictly model development studies, with only 1.1% (n=1) being usability studies. CONCLUSIONS: This is the first review to consider the role of AI in the automation of MedRec tasks, offering a basis for prioritizing future development efforts. Current applications of AI to automate MedRec tasks are preliminary, with most work focusing on the extraction of medication information and limited to proof-of-concept model development. Future work should consider addressing infrastructural barriers to the AI-based automation of MedRec tasks (eg, data incompleteness in sources of medication information) and exploring approaches to automate discrepancy resolution. Beyond developing models, there is also a need to implement them in tools and evaluate them in real-world contexts.
13 May 2026
Read appraisal →PloS one
Healthy living environments create healthy physical activity-related habits (ELEVATE): A protocol for a smartphone-based mixed-method study
Physical inactivity remains a key risk factor for adverse public health outcomes, disproportionately burdening lower socioeconomic groups. While individual-level interventions may temporarily increase walking-related physical activity (WPA) for some, they rarely achieve population-wide benefits. In contrast, built and natural environments are better positioned to support sustained WPA at the population level. Despite the importance of the environment to WPA, state-of-the-art research has limitations: it often relies on cross-sectional self-reports, uses coarse, simplistic environmental indicators, and focuses narrowly on residential neighborhoods, overlooking the diverse streetscapes people encounter in daily mobility. Using a transdisciplinary approach, ELEVATE addresses these gaps by 1) assessing how adults perceive and experience the built and natural streetscape environment during their WPA, 2) longitudinally assessing the relationships between streetscape environments and WPA, and 3) developing an artificial intelligence-powered system to suggest place-based environmental interventions that promote WPA. ELEVATE will employ a mixed-methods study design grounded in participatory co-creation, on-site and walk-along interviews to capture the everyday practices and experiences of WPA. We will use deep learning to extract objective environmental features and perceived streetscape qualities from street view images. A sample of approximately 200 adults residing in Amsterdam, The Netherlands, will be recruited. To examine relationships between micro-scale streetscape features and WPA, we will collect seven days of intensive longitudinal data using smartphones, including global positioning system (GPS)-based mobility and step counts. We will use spatiotemporal clustering to identify where people are most and least physically active, and mixed models to assess associations between these environmental measures and step counts. Qualitative and quantitative results will be triangulated in co-created focus groups. Finally, to support evidence-based public health and urban planning interventions, we will develop an AI-powered recommender system that generates context-sensitive (re)design guidelines framing the built and natural environment as a modifiable determinant of WPA. This study will generate evidence on how micro-scale built and natural street environments encountered during daily mobility are associated with WPA in adults. The findings are expected to inform place-based public health strategies by identifying modifiable environmental characteristics associated with physical activity and socioeconomic inequalities in urban settings.
13 May 2026
Read appraisal →PloS one
A remote monitoring system based on deep learning for real-time assessment of free flaps
PURPOSE: Venous congestion is a major cause of postoperative free flap compromise, and early detection is crucial for improving flap salvage rates and patient outcomes. This study aimed to develop and validate a deep learning (DL)-integrated remote monitoring system with a smartphone application for real-time, quantitative assessment of free flaps, with a specific focus on the early detection of venous congestion. METHODS: This diagnostic study was conducted at our institution. Patients aged 18-60 years who underwent free flap reconstruction between January 2019 and June 2025 were included. The study was divided into three phases: a 5-month model training phase for DL model development and internal validation, a 5-month external validation and clinical application phase, and a 4-month clinical comparison phase. The DL model was developed using TensorFlow Lite and a Flap Segmentation Network (FS-Net). Performance was evaluated through accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and clinical outcomes including time to detection and flap survival. RESULTS: A total of 1649 photographs from 615 patients were analyzed in the development and validation of the DL classification model between January 2019 and February 2025. During model development, the model achieved an accuracy of 86.8%, sensitivity of 92.4%, specificity of 79.7%, and AUC of 0.88. Internal validation improved these metrics to 87.6%, 95.0%, 81.1%, and 0.92, respectively. External validation demonstrated the model's generalizability, with an accuracy of 89.3%, sensitivity of 96.2%, specificity of 84.9%, and AUC of 0.93. The clinical application phase showed that the system had an overall accuracy of 92.16%, sensitivity of 95.18%, false-positive rate of 2.62%, and false-negative rate of 4.82%. A total of 113 patients were included in clinical comparison between March 2025 and June 2025. The remote monitoring group exhibited a trend towards a shorter mean time to congestion detection, higher flap survival rate, and shorter mean time to re-exploration, although these differences were not statistically significant. CONCLUSIONS: The DL-integrated remote monitoring system demonstrated high accuracy and reliability in detecting venous congestion. It provided an objective and real-time tool that may help reduce clinical burden and support timely intervention in free flap management. However, its impact on definitive clinical outcomes required further validation in larger studies.
13 May 2026
Read appraisal →JMIR mHealth and uHealth
Contribution of Longitudinal Mobile Health Measures in the Dynamic Track of Patients With Major Depressive Disorder: Multiple Centers, Prospective Cohort Study Using Functional Data Analysis and Machine Learning
BACKGROUND: Continuous follow-up for patients with major depressive disorder (MDD) is essential for treatment decisions and a better prognosis. There remains limited evidence regarding the critical issue of depression variation trajectory prediction using mobile health (mHealth) measures. Moreover, the temporal dynamics of mHealth measures have not been fully modeled in previous studies, and the poor patient adherence to mHealth records poses great challenges to the dynamic feature modeling. OBJECTIVE: This study aimed to examine the contribution of mHealth measures in predicting depression variation trajectory for patients with MDD, with full consideration of the temporal dynamics of mHealth measures. METHODS: A total of 229 patients with MDD from a multiple-center, prospective cohort were included. A 12-week follow-up was conducted involving the collection of the Hamilton Depression Rating Scale (HAMD-17), along with patient-reported outcomes (Immediate Mood Scaler and Altman Self-Rating Mania Scale) via mobile devices and sleep duration through wearable wristbands. We used functional data analysis to extract dynamic features from the sparse mHealth records, rather than aggregating the data to a single scalar summary measure through collapsing over time. Subsequently, 3 machine learning models were applied to predict the depression variation trajectory classes based on the baseline characteristics and these extracted dynamic features. RESULTS: Based on the variation of HAMD-17 scores within 12 weeks, the participants were labeled into 4 classes through the k-means algorithm. The classes included stable decline (n=93), fluctuate decline (n=44), fast decline (n=60), and delayed and fluctuate (n=32), in light of the shape of depression trajectories. With both baseline features and dynamic features of the mHealth measures, accuracy rates for the overall data were 54.35%, 60.87%, and 56.52%, for the stable decline patients were 78.95%, 84.21%, and 73.68%, for the nonstable decline patients were 59.26%, 62.96%, and 70.37% based on the 3 machine learning models, respectively. The results were significantly superior to the prediction obtained without mHealth measures (with an overall accuracy below 50%) and only showed a marginal reduction in accuracy relative to the ideal prediction with assessment obtained from clinical visits. Moreover, in the construction of the most accurate prediction model, dynamic features of the Immediate Mood Scaler, the Altman Self-Rating Mania Scale, and sleep duration emerged as the most influential predictors, ranking first, third, and fourth, respectively, in terms of their relative importance. CONCLUSIONS: Longitudinal mHealth measures show potential in depression variation trajectory monitoring for patients with MDD even under poor patient adherence. Our work provides practical help in alleviating the follow-up burden for patients with MDD and validates the effectiveness of mHealth measures in clinical applications.
13 May 2026
Read appraisal →PloS one
Extracting user profile via large language models and ontologies
Extracting user-specific profiles that include general personal information, such as hobbies, occupation, or age, is a valuable asset for systems like recommendation engines or personalized chatbots. Currently, most approaches moved toward exploiting the capabilities of large language models (LLMs) on rich languages while less-resourced languages still present significant opportunities for exploration and improvement. In this research, we present a multi-step approach for profile extraction from dialogue systems in Persian as a use case. Through an extensive set of experiments and analyses on various models, we show that LLMs struggle with these languages due to limited language-specific resources and complex linguistic structures. To address this, we propose a hybrid method that combines techniques such as slot filling, in-context learning, and ontology-based inference. Our final results demonstrate a significant improvement over current state-of-the-art models, including LLMs with few-shot examples and even their fine-tuned version. Our method achieves an F-score of 90.46, outperforming GPT-4o and Llama-3-70B by an absolute difference of 17.08 and 24.51 respectively. Our system can also detect inconsistencies in presented information in which our performance substantially exceeds the best performance of GPT-4o and Llama-3-70B with an accuracy of 92%. It is 21% absolutely better than GPT-4o and 47% better than Llama.
13 May 2026
Read appraisal →Journal of medical Internet research
Combining Machine Learning Models and Screening to Enhance Suicide Risk Identification for American Indian Patients: Retrospective Cohort Study
BACKGROUND: American Indian and Alaska Native communities experience disproportionately high suicide rates. While machine learning (ML) models leveraging electronic health records have emerged as promising tools for suicide risk identification, the optimal integration of these models with existing screening practices remains unclear. OBJECTIVE: The objective of this study was to compare parallel and serial testing strategies that combine an ML suicide risk model and the Ask Suicide-Screening Questions (ASQ) against using the ASQ alone. To achieve this, we conducted a retrospective secondary analysis of electronic health record data. The cohort consisted of adult emergency department visits at an Indian Health Service facility between October 1, 2019, and October 2, 2021. METHODS: Sensitivity, specificity, predictive values, and 95% CIs were averaged across 10 cross-validated patient-level folds. The final sample included 7897 American Indian patients with 26,896 visits, 824 (3.1%) of which had a positive ASQ result and 102 (0.4%) of which had the outcome of suicide attempt or death within 90 days of the visit. The logistic regression ML model previously developed using Indian Health Service-specific data was operationalized at the 95th and 75th percentiles to evaluate high-risk and medium-risk thresholds, respectively. A sensitivity analysis was performed to evaluate identification approaches across all emergency department visits during this period. RESULTS: The ML medium-risk threshold alone identified the most true positives (sensitivity: 0.782, 95% CI 0.648-0.915; specificity: 0.751, 95% CI 0.725-0.777; positive predictive value [PPV]: 0.012, 95% CI 0.009-0.014; negative predictive value [NPV]: 0.999, 95% CI 0.998-0.999) in comparison to the ML high-risk threshold alone (sensitivity: 0.429, 95% CI 0.287-0.572; specificity: 0.955, 95% CI 0.948-0.961; PPV: 0.035, 95% CI 0.022-0.048; NPV: 0.998, 95% CI 0.997-0.999) or the ASQ alone (sensitivity: 0.178, 95% CI 0.073-0.282; specificity: 0.970, 95% CI 0.968-0.971; PPV: 0.022, 95% CI 0.010-0.034; NPV: 0.997, 95% CI 0.996-0.998). Combining the ML high-risk threshold with the ASQ in series yielded the greatest positive predictive ability (PPV: 0.050, 95% CI 0.014-0.086) at the cost of reduced sensitivity (0.129, 95% CI 0.036-0.221). Finally, the parallel testing approach using the ML medium-risk threshold yielded the greatest sensitivity (0.795, 95% CI 0.671-0.920; specificity: 0.742, 95% CI 0.716-0.767; PPV: 0.012, 95% CI 0.009-0.014; NPV: 0.999, 95% CI 0.998-0.999) without missing any cases identified through screening. CONCLUSIONS: Unlike existing studies that evaluate ML and screening tools in isolation, this study innovates by assessing combined parallel and serial testing strategies in a real-world setting. We demonstrated that, while serial testing maximizes predictive accuracy, it is often infeasible. Instead, parallel testing brings value as a clinical "safety net" to catch at-risk patients missed by standard practices. Ultimately, integrating ML in suicide prevention requires balancing statistical accuracy with setting-specific, real-world workflows.
13 May 2026
Read appraisal →JMIR human factors
The Quality and Characteristics of Digital Mental Health Apps: Mixed Methods Study
BACKGROUND: There are around 20,000 mental health apps available in app stores. The Organisation for the Review of Care and Health Apps (ORCHA), a United Kingdom digital health compliance company, has assessed a number of digital mental health apps with regard to their quality, professional and clinical assurance, data privacy, and user experience. This study analyzes the data that were collected by ORCHA when they assessed mental health apps. OBJECTIVE: This study aimed to examine the characteristics of mental health apps regarding their quality, target users, features, underpinning evidence, and data privacy. METHODS: A dataset comprising ORCHA Baseline Review assessments of over 2000 digital health apps, including 436 mental health apps, was used. This study uses exploratory data analysis to gain insight into the quality and characteristics of mental health apps. Methods such as descriptive and inferential statistics, k-modes clustering, and association rule mining were used to explore the quality of mental health apps as well as reveal insights into the different cost types, target users, app features, data types, and evidence of app content. RESULTS: Information provision, data capture, and data sharing were the most common features within the 436 mental health apps. The examined apps primarily targeted the following groups: adults (n=229, 52.5%), everyone (n=184, 42.2%), and teens (n=135, 31%). The cost of apps has not been linked to the quality of mental health apps, although paid apps or apps with in-app purchases may include additional services. Indicated user acceptance or benefit is the most common type of evidence provided by these mental health apps. A total of 241 (55.3%) apps included a qualified professional in app development, and 251 (57.6%) apps provided evidence within the app that the developer validated any guidance with relevant reliable information sources or references. Usage data and email were the most commonly collected data types. Association rule mining showed that email, IP address, name, and usage data are often co-collected by the same apps. K-modes cluster analysis showed that mental health apps can be categorized into 2 clusters, where one cluster of apps (n=182, 41.7%) collected more data than apps in the other cluster. CONCLUSIONS: Mental health apps are commonly targeted for everyone to use, but many apps are targeted toward teens or adults. Our study suggests that many publicly available mental health apps did not take the precautions (such as the involvement of appropriate health professionals, literature references, or conducting tests) to ensure that their content is valid and research based. Greater effort on behalf of mental health app developers is needed to ensure that the public is provided with high-quality apps. Moreover, our study indicates that the mental health apps that collect more data tend to score better on the ORCHA Baseline Review assessment.
13 May 2026
Read appraisal →Journal of medical Internet research
Effectiveness of Digital Health Interventions in Older Adults With Frailty and Sarcopenia: Systematic Review and Meta-Analysis of Randomized Controlled Trials
BACKGROUND: Frailty and sarcopenia represent substantial global health challenges, frequently diminishing patients' quality of life through impaired muscle function and physical performance. Digital health interventions (DHIs) have shown promise in mitigating these conditions among older adults. However, outcomes of such interventions in this demographic are inconsistent, and a thorough synthesis of existing evidence is lacking. OBJECTIVE: This study aimed to evaluate the effectiveness of DHIs in older adults with frailty and sarcopenia. METHODS: A comprehensive search of PubMed, Web of Science, MEDLINE, Embase, and Cochrane Library was conducted from their inception until January 2026 to identify randomized controlled trials. Meta-analyses were performed using R software (R Foundation for Statistical Computing). Study quality was evaluated using the revised Cochrane Risk of Bias Tool 2.0 (Cochrane Collaboration), and evidence certainty was assessed using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). RESULTS: From 3506 records, 16 studies were included. DHIs significantly improved total skeletal muscle mass (weighted mean difference [WMD] 1.01, 95% CI 0.08-1.94, 95% prediction interval [PI] -0.95 to 2.96), gait speed (WMD 0.09, 95% CI 0.03-0.15, 95% PI -0.1 to 0.26), Timed Up and Go Test (TUGT: WMD -0.52, 95% CI -1.02 to -0.03, 95% PI -1.93 to 0.85), 30-second Chair Stand Test (30CST: WMD 2.19, 95% CI 0.89-3.48, 95% PI -1.59 to 5.66), balance (standardized mean difference [SMD] 0.61, 95% CI 0-1.21, 95% PI -0.94 to 2.13), and quality of life (SMD 0.16, 95% CI 0.05-0.27, 95% PI 0.04-0.28). No significant improvements were observed in Appendicular Skeletal Muscle Mass Index (ASMI), grip strength, 6-minute walk test (6MWT), 2-minute walk test (2MWT), Short Physical Performance Battery (SPPB), or BMI. Although the pooled effect was favorable, the wide 95% PI suggests substantial between-study heterogeneity. Subgroup analysis stratified by intervention duration revealed significant intersubgroup differences in ASMI (χ²₁=9.93; P=.0016), indicating interventions lasting ≥12 weeks were more effective for improving ASMI (WMD 0.28, 95% CI 0.06-0.50, 95% PI -0.30 to 0.83). Subgroup analysis stratified by intervention type showed significant intersubgroup differences in balance (χ²₃=9.89; P=.0195), with exergame-based interventions showing significant effects (SMD 0.83, 95% CI 0.26-1.40). CONCLUSIONS: This systematic review is the first to quantify the disease-specific efficacy of DHIs in improving muscle function, physical performance, and quality of life among older adults with frailty and sarcopenia, demonstrating their unique value as a scalable complementary approach. By overcoming geographical and resource constraints, DHIs support underserved populations. However, low evidence quality and heterogeneity warrant cautious interpretation. The 95% PIs indicate that actual effects may vary with population characteristics and implementation contexts. Nonetheless, DHIs represent a promising and cost-effective strategy for service expansion. Future high-quality studies are needed to better understand their effectiveness and implementation across settings.
13 May 2026
Read appraisal →Journal of medical systems
Clinical Plausibility in Large Language Model Robustness Testing for Medicine: A Scoping Review
Large language models (LLMs) show promise in medical applications, yet their translation into clinical practice requires rigorous validation. Current robustness testing often employs adversarial approaches borrowed from AI safety, raising questions about their alignment with authentic clinical scenarios. To systematically map methodologies used for robustness testing of LLMs in medical contexts and assess their clinical plausibility. A scoping review was conducted following PRISMA-ScR guidelines, searching PubMed, Embase, Web of Science, IEEE Xplore, ACM Digital Library, arXiv, and MedRxiv from January 2023 to September 2025. Two independent physician reviewers screened 5,331 articles, extracting data on testing methodologies, medical domains, expert involvement, and clinical plausibility. Thirty-three studies met inclusion criteria, predominantly from 2025 (82%). The most common robustness testing approaches were misleading prompts (49%) and adversarial prompts (39%). Only 33% of studies designed tests clearly mimicking plausible clinical scenarios. While 58% reported expert involvement, the depth of integration varied considerably. Studies predominantly addressed mixed medical domains (73%) rather than specialized fields. The emerging literature suggests that LLM robustness testing in medicine often emphasizes technical vulnerability detection, with fewer studies examining clinically plausible scenarios of routine use. Future frameworks should complement adversarial testing with clinically grounded, longitudinal, and specialty focused evaluations to support deployment-relevant inference.
13 May 2026
Read appraisal →Biofabrication
Resolving the multiscale design trade-off in bone tissue engineering: from generative design to digital twins
The clinical translation of bone tissue engineering is constrained by a severe multiscale design trade-off: the requirement for load-bearing mechanical strength competes directly with the need for vascular-permissive porosity. Conventional approaches address scaffold geometry, material formulation, and fabrication in a sequential, decoupled manner and therefore fail to navigate this multi-objective conflict. To overcome this limitation, we propose an 'AI-Driven Holistic Intelligence' framework. It spans the entire pipeline from generative inverse design to digital twin (DT)-based predictive maturation. The framework integrates gradient scaffold optimization, closed-loop adaptive manufacturing, and virtual clinical trial within a unified workflow. This architecture is designed to co-optimize the boundary conditions of stiffness and perfusion simultaneously, rather than in isolation. Nonetheless, realizing this vision as a clinically viable autonomous regeneration pathway will require bridging persistent translational gaps in data standardization, algorithm interpretability, and regulatory alignment. Importantly, the core innovation of this framework is not merely to navigate the empirical 'trade-off' along a fixed Pareto front, but to resolve it through multiscale co-optimization that expands the accessible design space, enabling simultaneous improvements in mechanical strength and vascular perfusion beyond the limits achievable by conventional single-objective approaches.
13 May 2026
Read appraisal →BMC health services research
Barriers and facilitators to telerehabilitation implementation: a mixed-methods study of German physiotherapists
BACKGROUND: The COVID-19 pandemic accelerated telehealth service adoption in physiotherapy, including telerehabilitation (TR). However, the extent of TR use and factors influencing its implementation in Germany remain unclear. This study aimed to evaluate TR use in physiotherapy (during COVID-19 lockdowns, current use, future intentions, conditions treated, content, setting, and session type) and identify barriers and facilitators from physiotherapists' (PTs) perspectives. METHODS: A mixed-methods sequential explanatory design was employed, combining a cross-sectional online survey (n = 152) with two focus group interviews (n = 9). The survey collected data on demographics, TR use, barriers, and facilitators. Focus groups explored themes emerging from the survey in greater depth. Data were analyzed using descriptive statistics and content analysis, integrating quantitative and qualitative findings. Quantitative and qualitative findings were integrated through explanatory linking and joint display in a comparative analysis table. RESULTS: TR use peaked during COVID-19 lockdown (32.26%) but decreased to 18.06% by October 2022, with 26.45% intending future use and 43.87% considering it. Among TR users, musculoskeletal conditions were most commonly treated (75%), followed by sports (38%), pulmonology (33%), and neurology (27%). The primary barrier was lack of physical examination (74% agreement). While technical challenges were not reported as a major barrier in the survey, interviews revealed significant concerns about insufficient internet bandwidth and technical infrastructure. Common reasons for using TR included promoting patient self-management (78% agreement) and broadening therapy options (69% agreement). Qualitative data identified additional implementation facilitators, including structured implementation processes, appropriate technical infrastructure, and patient involvement in decision-making. CONCLUSION: While TR implementation in German physiotherapy shows growth potential, several barriers currently limit its adoption. Successful implementation requires addressing PTs capabilities, knowledge gaps, professional identity concerns, and environmental factors. Addressing these issues could enhance patient care quality, increase service accessibility, and advance healthcare delivery models.
13 May 2026
Read appraisal →Integrative psychological & behavioral science
The Human Mind in Dialogue With its Replica AI - a Historical Perspective
Although artificial intelligence has exploded for the last couple of years, the idea of comparing the human mind with a computer is not new. One may say that Alan Turing started this movement, and this idea got off to a flying start in psychology through the symposium arranged by the "Special Interest Group in Information Theory" at Massachusetts Institute of Technology Sept. 10-11, 1956. This symposium was sponsored by IBM, and Noam Chomsky presented for the first time his ideas about a transformational generative grammar. There were several contributors to the cognitive revolution, but Chomsky's presentation of a linguistic system with a level of precision comparable with mathematics was symptomatic for how a cognitive model for the human mind aimed to be conceptualized. This has brought the question about artificial intelligence to the core of psychology, namely how it can be understood as a model for the human mind. Psychology and its understanding of the human mind have changed along the last 500 years of the Western psychology. After a short presentation of some of the premises for artificial intelligence of today and some aspects of the idea behind the computational mind, these perspectives are compared with the very early use of the term 'psychology' in the early 16th century. The role of classical logic as a model for the human mind was at stake back then and one of the main factors that paved the way for psychology was that Aristotelian logic was criticized and questioned as a model for the human mind. On the other hand, the importance of classical logic has never been completely outdated Consequently, for the last 500 years Western psychology can in many ways be characterized as an incessant battle about the role of logic in the understanding of the human mind. However, the conclusion is that AI of today presupposes an understanding of the human mind that has been outdated for more than 500 years.
13 May 2026
Read appraisal →Journal of medical systems
Feasibility, Adherence, Acceptance and Usability of a Multimodal Telemonitoring for Pediatric Post-COVID Syndrome: A Bicentric Pilot Study
Existing healthcare infrastructure struggles to meet the complex care required for pediatric Post-COVID Syndrome (pPCS). Telemonitoring offers potential to enhance care access, reduce patient burden, and ensure continuity. This study introduces and evaluates a novel, multimodal telemonitoring concept for pPCS with high translational potential for broader pediatric chronic and post-infectious conditions. Telemonitoring included a patient app, digital sensors (spirometer, smartwatch), Patient Reported Outcome Measures, chat/video consultations (VC), and a medical telemonitoring platform. Patients aged 12-17 years with diagnosed PCS were recruited from two pPCS outpatient university clinics in Bielefeld and Munich, Germany. Monitoring lasted three months. Evaluation focused on feasibility, adherence, acceptance, and usability, using monitoring data, the System Usability Scale (SUS), Technology Usage Inventory (TUI), and a custom survey completed by patients and parents. 30 patients (mean age: 15y ± 1.9; 57% female (17/30); mean Baseline Bell-Score: 36.4) and 30 parents participated. Adherence was high, with an average of 3.4 (smartwatch) to 4.6 (spirometry) measurements/week. Questionnaire response rate was 86% (411/480) and 97% (58/60) of VCs were conducted. SUS scores indicated very high usability (patients: 81.25/100; parents: 75.42/100). TUI results showed low skepticism, and high interest. Telemonitoring supported symptom management independent of in-person visits, despite sensor connectivity issues. This is the first study to demonstrate successful integration of telemonitoring in pPCS, with high adherence and positive feedback from all stakeholders supporting its potential. Despite occasional technical challenges and resource needs, this concept shows promise for broader hybrid telemonitoring care implementation in PCS and other post-infectious syndromes. TRIAL REGISTRATION: German Clinical Trials Register (DRKS), trial registration number: DRKS00029354. Registered 07 February 2023 - Retrospectively registered https://drks.de/search/en/trial/DRKS00029354/entails.
13 May 2026
Read appraisal →JMIR research protocols
Evaluating Patient-Entered Electronic Health Data as a Strategy to Improve Quality of Care in a Diabetes Clinic: Protocol for a Randomized Controlled Trial
BACKGROUND: Quality care for pediatric type 1 diabetes (T1D) requires frequent, multidisciplinary visits. Technological and clinical innovation have led to changes in T1D management, resulting in increasing data exchange required during these visits. Capturing comprehensive personal health and diabetes-related information discretely and integrating it into the clinical workflow is critical for optimal T1D care but is time-consuming. Time spent on data transfer often results in less time for holistic care and can lead to unmet needs for patients, families, and health care providers, as well as increased time pressures in clinic. To address this, the Children's Hospital of Eastern Ontario developed a caregiver proxy-reported questionnaire distributed via the MyChart patient portal, allowing families to input care information ahead of visits with the aim of dedicating more clinic time to personalized care. The launch of this tool, which integrates caregiver-entered information into the physician's documentation workflow, brings the opportunity to systematically evaluate its impact on care quality and efficiency, with potential implications for broader adoption. OBJECTIVE: Our objective is to evaluate the impact of a caregiver proxy-reported, electronic health record-integrated preclinic questionnaire (MyChart questionnaire) on the quality of care in a pediatric diabetes clinic, through measurement of its impact on caregiver-perceived quality of care compared to the standard of care using 2 validated measures of care quality. We also aim to explore the impact of the intervention on glycemic control and visit efficiency. METHODS: We conducted a single-center, parallel-group randomized controlled trial designed for 222 children with T1D. Participants were randomly allocated in a 1:1 ratio to either the intervention (MyChart questionnaire) or standard care. Our primary outcome is caregiver-perceived quality of care, as measured by the Patient's Evaluation of the Quality of Diabetes Care at 8 months, administered with caregivers serving as proxy respondents for patients. Secondary outcomes are the Patient's Evaluation of the Quality of Diabetes Care at 4 months and Perceived Quality of Medical Care at 4 and 8 months. Tertiary outcomes include glycemic control and physician-reported visit efficiency at 4 and 8 months. Analysis of covariance models will be used to assess changes between baseline and postintervention outcomes across treatment groups. RESULTS: Recruitment for this study began in April 2023 and was completed in February 2024, with a total of 139 participants enrolled. Data collection has concluded, and the first results are expected in the spring of 2026. CONCLUSIONS: This study is the first randomized trial to assess the impact of a caregiver proxy-reported, electronic health record-integrated, preclinic questionnaire distributed via a patient portal on caregiver-perceived quality of care in a pediatric care setting. The results will guide changes in health service infrastructure and delivery to enhance comprehensive data capture and improve care quality within and beyond pediatric T1D.
13 May 2026
Read appraisal →PloS one
A modular deep learning architecture for interpretable disease prediction across tabular clinical and biometric datasets
Accurate disease prediction using clinical datasets is essential for improving early diagnosis and clinical decision-support systems; however, many existing deep learning approaches are disease-specific, computationally intensive, and difficult to generalize across heterogeneous biomedical datasets. This study addresses this challenge by proposing a unified and dataset-aware deep learning framework that enables accurate and interpretable disease prediction across diverse clinical datasets. The framework adopts a modular architecture that selects appropriate models based on dataset characteristics such as feature dimensionality, sample size, and class imbalance. It integrates multiple deep learning architectures, including MLP, one-dimensional CNN, FT-Transformer, autoencoder-based classifiers, and ensemble strategies. Robust preprocessing, fold-safe feature selection, and nested cross-validation are incorporated to ensure reliable performance evaluation. The framework is evaluated on three heterogeneous benchmark datasets: the UCI Heart Disease dataset (303 samples, 13 clinical features), the PIMA Indians Diabetes dataset (768 samples, 8 metabolic features), and the Parkinson's disease voice dataset (195 recordings, 22 acoustic features). Experimental results demonstrate competitive predictive performance relative to classical baselines across the diverse tasks. The FT-Transformer + autoencoder ensemble achieved an AUC of 0.8980 (±0.0483) for heart disease prediction, while the CNN + Autoencoder ensemble obtained an AUC of 0.8451 (±0.0270) for diabetes classification. For Parkinson's disease detection, the MLP achieved an AUC of 0.7538 with perfect specificity. Overall, all models achieved AUC values comparable to ML baselines. The study contributes a scalable and interpretable deep learning framework that improves reliability, generalization, and practical applicability for multi-disease prediction in real-world healthcare environments.
13 May 2026
Read appraisal →Journal of medical Internet research
Governing Patient-Facing AI-Generated Video in Digital Health: A Risk-and-Ethics Matrix for Deployment, Monitoring, and Change Control
In this Viewpoint, we argue that patient-facing high-fidelity artificial intelligence (AI)-generated video requires governance that is operational, life cycle based, and embedded in existing institutional review pathways rather than limited to predeployment checks alone. Patient-facing high-fidelity AI-generated video-synthetic or substantially AI-mediated video that presents realistic human likeness, voices, or clinical communication cues-is rapidly entering patient education and clinical communication. We propose a risk-and-ethics matrix that combines residual clinical risk (likelihood × severity after mitigations) with an ethical alignment score that operationalizes autonomy, beneficence, nonmaleficence, and justice to yield actionable dispositions (encourage, permit with oversight, restrict or redesign, or prohibit). The framework links each disposition to dossier-based review, minimum controls, and postdeployment monitoring triggers-focused on measurable outcomes (eg, comprehension, content-attributable follow-up burden, incidents and complaints, and equity gaps) as well as provenance and change control-to support auditable, revisitable decisions over the system life cycle.
13 May 2026
Read appraisal →Scientific reports
Uncovering advanced transfer learning strategies for deep neural networks in natural language processing
In the rapidly evolving landscape of Natural Language Processing (NLP), transfer learning has emerged as a game-changing methodology, fundamentally altering how machine learning models are trained and deployed. The study at hand dives deep into the intricacies of transfer learning techniques, specifically focusing on their application in complex deep learning architectures within the NLP domain. By exploring a variety of architectural designs, fine-tuning methodologies, and alternative training paradigms, we aim to demystify the optimal strategies for harnessing the power of pre-trained models. To quantify the effectiveness of these approaches, we conducted a comprehensive series of experiments targeting key NLP tasks, such as text classification and language generation. Transfer learning significantly accelerated training and improved model accuracy, highlighting its practical advantages in NLP tasks. This dual benefit underscores the immense potential of advanced transfer learning techniques, making them an indispensable tool for future NLP applications. By implementing state-of-the-art transfer learning methodologies, companies can offer faster and more accurate NLP solutions, aligning perfectly with the ethos of "Business Digitalized" and catering to a broad spectrum of client needs in the market.
13 May 2026
Read appraisal →Journal of medical Internet research
Efficacy, User Engagement, and Acceptability of Cognitive Behavioral Therapy-Oriented Psychological Chatbots for Adults With Depressive and/or Anxiety Symptoms: Systematic Review and Meta-Analysis of Randomized Controlled Trials
BACKGROUND: Cognitive behavioral therapy (CBT) is the most examined psychotherapy for depression and anxiety, but delivery faces significant barriers such as limited access, cost, and time constraints. CBT-oriented psychological chatbots offer a promising means of addressing these challenges. Yet, their overall efficacy, user engagement, and acceptability have not been systematically synthesized. OBJECTIVE: This study aimed to evaluate the efficacy, user engagement, and acceptability of CBT-oriented chatbots for adults with depressive and/or anxiety symptoms. METHODS: A systematic search of 9 databases, including PubMed, Cochrane Central Register of Controlled Trials, Embase, Web of Science, PsycINFO, CINAHL, China National Knowledge Infrastructure, WanFang, and VIP Databases, was conducted from inception to February 2026. Eligibility criteria included randomized controlled trials comparing CBT-oriented chatbots with control groups in adults with depressive and/or anxiety symptoms. Risk of bias (ROB) was assessed using the Cochrane ROB tool. Random-effects meta-analyses (Hartung-Knapp-Sidik-Jonkman adjustment) calculated pooled effect sizes (Hedges g), 95% CIs, and 95% prediction intervals (PIs). Heterogeneity was evaluated using the I² statistic, and Galbraith plots were used to identify outliers for subsequent sensitivity analyses. Subgroup and meta-regression analyses examined potential moderators. The certainty of evidence was evaluated using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach. Data on user engagement and acceptability were extracted and synthesized using narrative and quantitative methods where available. RESULTS: Twenty-nine eligible randomized controlled trials were included. CBT-oriented psychological chatbots produced a moderate reduction in depressive symptoms at postintervention (g=-0.55, 95% CI -0.70 to -0.40, 95% PI -1.23 to 0.13) and a small reduction in anxiety symptoms (g=-0.26, 95% CI -0.37 to -0.14, 95% PI -0.67 to 0.15). At follow-up, effects were small for depression (g=-0.32, 95% CI -0.55 to -0.09, 95% PI -0.93 to 0.29) and nonsignificant for anxiety (g=-0.19, 95% CI -0.43 to 0.04, 95% PI -0.84 to 0.46). Subgroup and meta-regression analyses revealed that anxiety outcomes were significantly moderated by clinical profiles-showing distinct advantages for comorbid symptoms-and the proportion of female participants. The CBT-oriented chatbots received an adequate level of engagement that complied with digital intervention standards. Although user satisfaction ratings were generally favorable, technical limitations and repetitive interaction patterns remain to be addressed to enhance overall acceptability. Regarding the limitations of evidence, the overall certainty was rated as very low to low, predominantly driven by high ROB and substantial heterogeneity. CONCLUSIONS: This study innovatively isolates CBT-oriented chatbots from broader digital interventions, providing a precise, methodology-driven evaluation of theoretically grounded therapeutics. This review brings critical evidence to the field that these tools yield significant short-term relief, particularly for comorbid anxiety profiles. In the real world, CBT chatbots offer profound potential as scalable, low-barrier first-line tools. To sustain engagement, future developments must evolve from rigid rule-based scripts toward adaptive, large language model-driven architectures while ensuring clinical safety.
13 May 2026
Read appraisal →Biomedical physics & engineering express
Vitiligo state assessment based on progressive transfer learning and multimodal domain adaptation
Vitiligo is a common skin depigmentation disorder; assessing its state is crucial for the treatment outcome. Collecting multimodal data for vitiligo assessment is complex and costly in clinical practices, and the limited data size restricts the performance of deep learning models. Transfer learning can alleviate the shortage of training data in medical image recognition, however, its applications in vitiligo state assessment are constrained by feature differences between natural and medical images and insufficient generalization to different modalities. To address these challenges, this paper introduces a vitiligo state assessment method based on progressive transfer learning and multimodal domain adaptation. The scheme uses a large set of unlabeled medical images as a bridge to reduce the discrepancies between natural and medical images through multi-step fine-tuning. An adaptive parameter unfreezing strategy is then applied for the accurately labeled target data to improve the adaptability and accuracy of the model. In addition, an integrated multimodal domain adaptation approach based on CycleGAN and hue, saturation, value color space transformation is proposed to reduce the impact of modality differences on transfer learning. Experimental results demonstrate that compared to the conventional transfer learning method, the proposed method improves the accuracy by 2.4% and 3.6% on the clinical and Wood's lamp modality datasets, respectively. This accurate vitiligo state assessment method can be applied to a wide range of multimodal dermatological diseases where labeled data is limited.
13 May 2026
Read appraisal →Scientific reports
Automated phenotyping of ophthalmologic diseases from routine medical records using small language models and the human phenotype ontology (HPO)
Automated phenotyping in ophthalmology requires accurate standardization of clinical terms to facilitate interoperability and research. This study evaluates the suitability of the human phenotype ontology (HPO) for automated extraction of ophthalmic phenotypes from narrative documentation. We developed a locally operated AI pipeline combining text segmentation and negation detection based on a small language model (PHI-4) with a dense retrieval approach using an augmented multilingual HPO catalog. Synonyms were incorporated into the HPO during training on anonymized consecutive physician letters. To validate the pipeline, 175 anterior segment and fundus descriptions from randomly picked medical records were manually annotated with HPO terms as ground truth. Overall, 342 HPO terms were identified manually (on average 2.53 terms per document), with 341 retrieved by the pipeline (on average 2.52 terms per document). Performance metrics showed a median Jaccard similarity of 0.67, precision of 0.83, recall of 0.82, and F1 score of 0.80. These results demonstrate that our AI pipeline effectively extracts standardized HPO terms from free-text ophthalmic findings. Integration of this pipeline into clinical information systems may enhance data interoperability and reduce manual coding workload in ophthalmology practices in the future.
12 May 2026
Read appraisal →Current heart failure reports
Face-to-Face and Internet-based Cognitive Behavioral Therapy for Patients with Heart Failure: an Umbrella Review of Systematic Reviews and Meta-analyses
PURPOSE OF REVIEW: The present study aims to provide an up-to-date umbrella review of existing meta-analyses of randomized control trials (RCT) of cognitive behavioral therapy (CBT) among patients with heart failure (HF). RECENT FINDINGS: The umbrella review covered five meta-analyses published in the past five years with 18 primary studies (conducted between 2010 and 2022) of 1,687 non-duplicated patients aged 51.63 to 77.40. Primary studies originated from the USA, Sweden, Netherlands, China, Iran, and Philippines. Common benefits of CBT included depressive symptoms (89%), quality of life (56%), anxiety symptoms (39%), self-care (33%), 6-minute walk distance (33%), and health status (17%). Evidence varied depending on the timepoint of follow-up assessment and the specific measurement used to evaluate the outcome. No meta-analyses pooled the effect sizes for iCBT and analyzed the specific therapeutic elements in CBT. No meta-regression was performed. Individual participant data meta-analyses with meta-regression should be performed to identify common moderators and factors that can influence treatment adherence. The current research would also benefit from additional RCT evidence focusing specifically on iCBT for patients with HF.
12 May 2026
Read appraisal →Current pain and headache reports
Telemedicine in Pediatric Headache in a Post-COVID-19 World: A Narrative Review and Practical Implementation
PURPOSE OF REVIEW: Pediatric migraine is a leading cause of disability worldwide, yet access to pediatric headache specialists remains limited due to workforce scarcities and geographic disparities. This review examines the evolution of telemedicine in headache care, evaluates current evidence for safety and efficacy, and explores its role in improving access and multidisciplinary management. RECENT FINDINGS: Randomized trials and observational studies demonstrate that telemedicine provides clinical outcomes comparable to in-person visits with high patient satisfaction and reduced logistical burden. Emerging data support its safety in non-acute headache evaluation as well as benefit of supporting multidisciplinary care. Professional societies, including the American Headache Society and the American Academy of Pediatrics, endorse telemedicine as an appropriate modality for headache management. Telemedicine has matured into a sustainable, evidence-based approach for pediatric headache care. When implemented thoughtfully, it expands access, supports multidisciplinary treatment, and maintains safety, positioning it as an essential component of future headache care delivery models.
12 May 2026
Read appraisal →Journal of medical systems
Use of Generative Artificial Intelligence in the Management of Low Back Pain: a Scoping Review
BACKGROUND CONTEXT: Low back pain (LBP) is common and a major cause of disability globally. Generative artificial intelligence (GenAI) such as ChatGPT may have the potential to improve LBP, but it is unknown how GenAI is being used in clinical care settings. PURPOSE: This scoping review aimed to map and synthesise the existing literature on the use of GenAI in the management of LBP across all settings. STUDY DESIGN: This review followed the scoping review methodology of the Joanna Briggs Institute and Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. METHODS: We searched the Cochrane Library, Medline, Scopus, Embase, Web of Science, CINAHL, and preprint platforms for studies published between January 2012 and July 2025, on the use of GenAI in the management of LBP across all healthcare settings. The included studies were categorised according to the primary purpose of GenAI implementation. RESULTS: A total of 31 studies were included. Seventeen studies evaluated GenAI as a potential support tool for diagnosis and treatment, including answering specific questions about LBP (n = 8), generating advice from clinical cases (n = 4), and generating advice from diagnostic imaging (n = 5). Eight studies examined the use of GenAI to provide educational advice for patients. Other uses included research support (n = 5) and clinical documentation support (n = 1). GenAI advice for the management of LBP demonstrated higher agreement when using simpler tasks, more detailed prompts, or newer generation models. Reported errors in GenAI advice included inconsistency with guidelines or clinicians' advice, hallucination, low accuracy for complex questions, sensitivity to prompt design, and challenges with interpretability. CONCLUSION: Thirty-one studies reported the use of GenAI in LBP management, mainly supporting diagnosis and treatment planning, patient education, research, and clinical documentation. Despite authors' claims about promising uses of GenAI, it typically requires further refinement or review by human end-users. Critically, no studies addressed end-user experience, or clinical effectiveness, underscoring the need for robust clinical evaluation beyond the lab-based environment.
12 May 2026
Read appraisal →Journal of medical Internet research
Efficacy of Technology-Based Interventions on the Reduction of Loneliness: Systematic Review and Meta-Analysis
BACKGROUND: Loneliness is a widespread public health concern linked to increased risks of health problems. As populations age, the demand for effective interventions to mitigate loneliness continues to grow. OBJECTIVE: This meta-analysis aimed to examine the effectiveness of technology-based interventions in reducing loneliness. METHODS: A systematic literature search was conducted in Web of Science, PsycInfo, PubMed, Scopus, Google Scholar, Embase, and the Cochrane Library in August 2024. We included randomized controlled trials that examined the effectiveness of technology-based interventions compared with any control group in reducing loneliness across all age groups. Nonrandomized studies, qualitative research, and studies lacking sufficient statistical data for effect size calculation were excluded. After screening 1089 records, 7 studies involving 580 participants met the inclusion criteria. Data were extracted by 3 independent reviewers, with discrepancies resolved by a fourth reviewer. The risk of bias was assessed using the Cochrane risk-of-bias tool. A random-effects model was used to synthesize effect estimates, with standardized mean differences as the primary effect size metric. Heterogeneity was assessed using the Q statistic and I² index, and a prediction interval was calculated to estimate the expected range of true effects. RESULTS: We found a small and statistically nonsignificant overall effect of technology-based interventions on loneliness (pooled standardized mean difference=-0.21, 95% CI -0.59 to 0.17; 95% prediction interval -1.14 to 0.63). Substantial between-study variability was present (I²=57%; τ²=0.08; τ=0.28), and the prediction interval indicated that true effects in future studies may range from substantial reductions to moderate increases in loneliness. Differences between intervention types could not be examined due to the limited number of eligible studies. The Egger test showed significant funnel plot asymmetry (t5=-5.47; P=.003). However, since the trim-and-fill method identified no missing studies, the asymmetry is unlikely to be fully explained by publication bias. CONCLUSIONS: Unlike previous reviews, which focused primarily on older populations or included nonrandomized studies, this meta-analysis provides a rigorous synthesis of only randomized controlled trials across the lifespan. Our findings do not provide evidence for a reliable reduction in loneliness following technology-based interventions. Moderate heterogeneity indicates that effects differ across studies, and the limited number of eligible trials prevented the analysis of potential moderators. By incorporating robust prediction intervals and a broader age demographic, this study offers a more comprehensive view of the variability of intervention outcomes compared with existing literature. The wide prediction interval suggests that intervention effects may vary across settings. In practice, technology-based approaches should be viewed as supportive tools that require careful tailoring rather than universal solutions to loneliness.
12 May 2026
Read appraisal →Journal of medical Internet research
Barriers and Facilitators to Patient Acceptance of Artificial Intelligence in Health Care: Systematic Review
BACKGROUND: Artificial intelligence (AI) in the domain of health care is increasing in prominence. Acceptance is an indispensable prerequisite for the widespread implementation of AI. OBJECTIVE: This study aimed to explore barriers and facilitators influencing patients' acceptance of AI. METHODS: We conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Nine databases, including PubMed, Web of Science, and Embase, were comprehensively searched from inception to December 23, 2025. We included qualitative, quantitative, and mixed methods studies investigating adult patients' attitudes toward medical AI. Two researchers independently screened records, extracted data, and appraised methodological quality using the Mixed Methods Appraisal Tool. Following the Joanna Briggs Institute convergent integrated approach, data synthesis was guided by integrating the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and the Theoretical Domains Framework (TDF). Factors were mapped to behavior change techniques (BCTs) and evaluated for practical feasibility using the Affordability, Practicability, Effectiveness and cost-effectiveness, Acceptability, Side-effects/safety, and Equity criteria. RESULTS: A total of 61 studies met the inclusion criteria out of 7452 search results. Study designs included qualitative (n=20), quantitative (n=35), and mixed methods (n=6). Performance and effort expectancies were the primary determinants of acceptance. Major barriers comprised perceived operational complexity, lack of algorithmic trust, reduced interpersonal interaction, privacy vulnerabilities, and high costs. Facilitators included transparent data governance, interpretability of AI decisions, improved clinician-patient communication, and human-centered design. Education level and disease severity emerged as key moderating variables. Through UTAUT2-TDF mapping, we identified 25 distinct BCTs (6 high, 14 medium, and 5 low feasibility) and formulated 40 actionable intervention strategies. CONCLUSIONS: This study innovatively integrates the UTAUT2 and TDF frameworks to evaluate patient acceptance of medical AI. Unlike existing reviews that predominantly evaluate isolated psychosocial factors or purely technical attributes, this transtheoretical approach differentiates itself by merging technology adoption mechanisms directly with behavioral drivers. Consequently, it contributes to the field by systematically identifying multilevel factors influencing acceptance, including performance expectancy, effort expectancy, and ethical security, and translating these into 40 actionable BCTs. In real-world clinical practice, these findings provide a feasible, prioritized blueprint for clinicians and administrators to design patient-centered interventions, enhancing the clinical integration and long-term effectiveness of medical AI.
12 May 2026
Read appraisal →Physiological measurement
A multimodal fusion network for heart sound abnormality detection and classification
Objective.Accurate physiological assessment of cardiac function from heart sounds remains challenging due to background noise, variable heart rates, and the need for reliable cardiac-cycle segmentation. This study aimed to develop a fully E2E deep learning framework that extracts diagnostic information directly from raw heart sound recordings for cardiac abnormality detection and classification.Approach.We propose HS-MMNet, an E2E multi-modal deep learning framework designed for physiological heart sound analysis. Recordings are preprocessed (normalization and 25-400 Hz bandpass filtering) and divided into fixed-length 2.5 s segments. A Convolution Head with multi-atrous spatial pyramid and channel-spatial attention extracts fine-grained local temporal patterns from the filtered 1-D waveform. A Transformer Head captures long-range spectro-temporal dependencies from Log-Mel spectrograms. These hypotheses are iteratively fused by a novel multi-hypothesis cross-attention module with cyclic query-key-value assignment and a hypothesis-mixing MLP, enabling rich cross-site interaction and effective suppression of noise and non-informative regions. Recording-level classification is obtained via a fully connected layer.Main results.On the PhysioNet/CinC Challenge 2016 dataset, HS-MMNet achieved 94.80% accuracy, 92.10% sensitivity, 96.85% specificity, 87.50% precision, and 89.74%F1-score, outperforming all previously reported methods. On the balanced five-class Yaseen dataset (normal, aortic stenosis, mitral regurgitation, mitral stenosis, mitral valve prolapse), it attained 99.60% macro-averaged precision, recall, andF1-score with only four misclassifications in 1000 recordings, establishing new state-of-the-art (SOTA) benchmarks.Significance.HS-MMNet represents an advance in automated physiological measurement from heart sounds. By eliminating cardiac cycle detection and multi-channel requirements while achieving SOTA diagnostic performance, it provides a practical, scalable solution for accurate cardiovascular screening with primary-care and low-resource settings.
12 May 2026
Read appraisal →Journal of medical Internet research
Why We Need Patients and Community at the Center of AI Health Communication Research
Holderried and colleagues tested whether artificial intelligence (AI)-generated, patient-centered information can help people understand what they need to do after being discharged from a hospital. Participants demonstrated stronger comprehension when they viewed the simplified, patient-centered information rather than a standard letter. This work adds to the available early-phase evidence of AI supporting hospital discharge communication. To meaningfully progress this area of research, we now need to carefully consider how to enhance the design and evaluation of patient-facing AI health communication tools. In this commentary, we argue that equity and consumer engagement remain underrepresented in studies on patient-facing AI health communication tools and describe possible approaches to address this issue.
12 May 2026
Read appraisal →JMIR mHealth and uHealth
Efficacy and Safety of a Telemedicine System in Patients With Gestational Diabetes Mellitus (TELEGLAM): Single-Center, 2-Arm, Randomized, Open-Label, Parallel-Group Study
BACKGROUND: In the management of gestational diabetes mellitus (GDM), the usual medical treatment requires frequent visits for glucose monitoring and insulin dose adjustment, and this imposes significant physical, psychological, and economic burdens on pregnant women. As mobile health platforms become increasingly integrated into diabetes care, telemedicine may help alleviate these burdens; however, evidence evaluating its effectiveness as a replacement for routine in-person GDM care remains limited. OBJECTIVE: This study aims to evaluate the impact of telemedicine on the quality of life and costs for patients with GDM requiring insulin therapy. METHODS: This single-center, 2-arm, randomized, open-label, parallel-group study included patients with GDM who started insulin injection therapy. Participants were randomized to either the telemedicine or standard face-to-face care groups for 10 (SD 2) weeks. The telemedicine intervention used a smartphone-linked platform that enabled the automatic transfer of glucose data from connected glucose meters and facilitated real-time video consultations. Primary end points included costs and patient satisfaction. Costs were assessed using claims data, transportation calculations, and wage-based productivity losses, while patient satisfaction was evaluated through changes in the Problem Areas in Diabetes Survey and Diabetes Therapy-Related Quality of Life questionnaire scores. Secondary outcomes included glycemic control and perinatal outcomes. RESULTS: In total, 38 participants were included, with 18 assigned to the telemedicine group and 20 to the standard care group. Total costs (32,712, 95% CI 15,412-50,013 vs 59,202, 95% CI 42,603-75,800 Japanese yen; $284, 95% CI 134-435 vs $515, 95% CI 370-659, purchasing power parity [PPP]-adjusted; P=.01), direct non-health care costs (922, 95% CI -240 to 2084 vs 2561, 95% CI 1447-3676 yen; $8, 95% CI -2 to 18 vs $22, 95% CI 13 to 32 PPP-adjusted; P=.02), and indirect costs (8981, 95% CI -7119 to 25,082 vs 32,832, 95% CI 17,384-48,279 yen; $78, 95% CI -62 to 218 vs $285, 95% CI 151-420 PPP-adjusted; P=.01) reduced significantly in the telemedicine group compared with the standard care group. The improvements in the Problem Areas in Diabetes Survey (-7.6, 95% CI -13.7 to -1.4; P=.02) and Diabetes Therapy-Related Quality of Life domain 1 (10.5, 95% CI 0.9-20.1; P=.03) scores from the baseline were significantly greater in the telemedicine group than that in the standard care group. Nonetheless, glycemic control and frequency of perinatal complications were comparable between the 2 groups. Consultation time was similar across groups, suggesting no added workload for clinicians. CONCLUSIONS: In this randomized trial, mobile health-enabled telemedicine safely replaced routine in-person visits for patients with GDM requiring insulin therapy. Telemedicine significantly reduced psychological and economic burdens without compromising glycemic or perinatal outcomes, demonstrating its value as a patient-centered and cost-efficient model of care. These findings support the broader implementation of mobile-based telemedicine approaches in GDM management.
12 May 2026
Read appraisal →JMIR medical informatics
Resource Use Patterns in US Telehealth Services: Machine Learning and Clustering Analysis Across 4 Specialties
BACKGROUND: The expansion of telehealth services, particularly during the COVID-19 pandemic, has transformed health care delivery in the United States. Telehealth promises greater access and resource efficiency by reducing wait times and appointment lengths, especially in specialties like psychiatry, behavioral health, bariatrics, and sleep medicine. However, disparities exist in adoption based on demographics, geography, and socioeconomic status, raising concerns about equitable access and optimal resource use. OBJECTIVE: This study aims to evaluate how telehealth impacts health care resource use across 4 specialties by examining 2 key metrics: patient-to-provider ratios and appointment durations. It seeks to understand how factors such as patient demographics, facility characteristics, and social determinants influence telehealth adoption and efficiency using a national dataset spanning from 2018 to 2023. METHODS: We analyzed a deidentified dataset from Epic Cosmos, covering outpatient visits across 48 US states (2018-2023). After data preprocessing and feature engineering, we applied 3 machine learning (ML) models (random forest, extreme gradient boosting, and deep neural networks) to predict resource use. Using the model performing the best, feature importance was assessed using Shapley Additive Explanations values. We then used k-means clustering to group facilities into clusters per specialty. Comparative analyses were conducted to evaluate differences in use among clusters, during and after the pandemic. RESULTS: Telehealth use peaked in 2020 and has remained above prepandemic levels since then. In 2018-2023, telehealth adoption reached 36.9% (4,543,021/12,311,710) in psychiatry, 23.9% (5,321,099/22,264,013) in behavioral health, 21.2% (924,333/4,360,061) in bariatrics, and 16.8% (851,803/5,070,256) in sleep medicine. Telehealth visits were consistently shorter than office visits (mean reduction 12.24 minutes; SD 3.33 minutes; P=.18), while patient-to-provider ratios varied significantly across specialties. Among ML models, extreme gradient boosting regression achieved the best performance (patient-to-provider ratios: R2=0.96-0.99; appointment durations: R2=0.61-0.69). Shapley Additive Explanations analysis identified visit type, telehealth use, facility size, rurality, and Social Vulnerability Index household vulnerability as the strongest predictors. Comparative analyses showed significant differences across clusters (all P<.05). CONCLUSIONS: Telehealth has become a sustainable component of health care, enhancing access and efficiency across both rural and urban areas. However, its impact varies across specialties and regions, highlighting the need for targeted strategies such as staffing support for vulnerable populations, infrastructure investments in rural facilities, and reimbursement models that reflect telehealth's resource use. This study provides robust evidence from ML and clustering analyses, demonstrating how telehealth shapes resource use and offering actionable insights for equitable and sustainable integration.
12 May 2026
Read appraisal →PloS one
Gated subspace alignment with drift compensation for parameter-efficient Class-Incremental Learning
Class-Incremental Learning (CIL) aims to enable models to continuously learn new categories while preserving existing knowledge and avoiding catastrophic forgetting. Although parameter-expansion architectures can alleviate task interference to some extent, the representations of previously learned classes often drift or degrade as the feature subspaces continuously evolve and expand, resulting in decreased recognition performance for old classes. To address this issue, we propose an efficient CIL method-Dynamic Gated Adapter for Subspace Alignment (DGASA). Based on a frozen pre-trained backbone, DGASA introduces lightweight adapters with attention-based gating for each task to construct task-specific subspaces, while dynamically fusing cross-task information via attention mechanisms. In addition, DGASA learns a linear mapping between the old and new subspaces to achieve consistent alignment of old class prototypes in the current subspace without accessing past data. Extensive experiments demonstrate that DGASA significantly improves classification accuracy and resistance to forgetting on multiple benchmark datasets, offering strong generalization and computational efficiency.
12 May 2026
Read appraisal →JMIR medical informatics
Machine Learning-Based Multidimensional Oximetry for Obstructive Sleep Apnea Screening: Development and External Validation
BACKGROUND: Obstructive sleep apnea (OSA) affects nearly one billion people globally and poses a substantial public health threat. Effective and accessible methods for OSA risk identification are urgently needed. OBJECTIVE: This study aims to develop and externally validate a machine learning model derived from multi-parameter pulse oximetry (SpO2) for OSA screening, and to evaluate its performance, interpretability, and robustness across sex and age subgroups. METHODS: Of 4156 screened participants, 2195 underwent polysomnography (internal cohort) and 446 received home sleep apnea testing (external cohort). Eight SpO2-derived parameters, including oxygen desaturation index (ODI), hypoxic burden (HB), and ST90 (percentage of sleep time with SpO2 < 90%), were used to construct models. Six machine learning algorithms were trained, with F1-score as the primary metric and area under the curve as the secondary metric. Model interpretability was assessed using Shapley additive explanations and intrinsic feature importance scores. RESULTS: Nonlinear parameter-risk relationships were observed between oximetry indices and OSA probability. The 4-parameter ODI-HB-MinSpO2-ST90 model achieved optimal performance (F1-score = 0.9516, area under the curve = 0.9879), surpassing all single-parameter models. Shapley additive explanations analysis identified ODI, HB, and MinSpO2 as key predictors. The ODI-HB-MinSpO2-MeanSpO2 configuration demonstrated superior performance in female and younger subgroups, whereas the ODI-HB-MinSpO2-ST90 model remained optimal for male and older participants. Categorical boosting outperformed other algorithms across multiple metrics and remained robust in both subgroup and external validation analyses. CONCLUSIONS: The multi-parameter oximetry model based on the categorical boosting algorithm provides a simple and accurate tool for OSA screening. Sex- and age-stratified strategies can further enhance its clinical applicability.
12 May 2026
Read appraisal →Journal of medical Internet research
The Digital Exposome: A Life Course Framework for Health in the Digital Age
Digital technologies are reshaping human behavior, health care delivery, and population health; however, their cumulative effects across the lifespan remain underexplored. This viewpoint argues that exposures arising from interactions with digital technologies should be formally integrated into exposome science as a distinct, measurable component of the human environment. Our aims are to (1) redefine the digital component of the exposome (the digital exposome) within the broader exposome framework, (2) examine its life course implications for health and equity, and (3) outline a research and policy agenda to enable its systematic measurement and integration into clinical and public health practice. Digital technology-related exposures can confer benefits such as enhanced health monitoring, personalized interventions, improved access to care, and the promotion of healthy behaviors. However, they may also introduce potential risks, including mental health challenges, cognitive and circadian disruptions, sedentary lifestyles, exposure to misinformation, and widening inequities among vulnerable populations. Despite their ubiquity, digital technology-related exposures remain poorly integrated into clinical medicine, epidemiology, or public and global health policies. Drawing on interdisciplinary evidence from exposure science, epidemiology, and digital phenotyping research, we propose a refined conceptual definition of the digital exposome grounded in the classical exposome domains. We propose redefining the digital exposome as the full spectrum of exposures resulting from interactions or proximities with digital technologies and their combined influence on health across the lifespan. This framework conceptualizes digital technology-related exposures as a dynamic set of environmental influences operating through sociotechnical, behavioral, and biological pathways over the life course. To operationalize this framework, we discuss practical approaches using validated behavioral instruments, objective device use logs, ecological momentary assessments, smartphone-based digital phenotyping, and wearable sensing technologies. Systematic measurement, large-scale longitudinal studies, and harmonized exposure metrics are needed to characterize the cumulative health impacts of digital environments more accurately. Emerging tools such as digital markers or biomarkers and digital phenotypes offer promising opportunities to link real-world technology use with physiological and biological outcomes, thereby supporting precision medicine and population health strategies. Ethical governance, privacy safeguards, and equity considerations must be embedded from the start, drawing on emerging exposomethics frameworks. Recognizing the digital exposome as a modifiable determinant of health offers a foundation for evidence-based guidance, prevention strategies, and policy interventions suited to increasingly digital societies. By integrating digital technology-related exposures into exposome science, clinical practice, and public health research, this viewpoint seeks to foster interdisciplinary dialogue, guide future empirical work, and support the development of safer and more equitable digital environments across the lifespan.
12 May 2026
Read appraisal →JMIR human factors
Persuasive Systems Design Features of Smartphone Apps for Psychosis: Systematic Review
BACKGROUND: It is unclear why some smartphone apps designed for people with psychosis are engaging, while others are not. One possible explanation is the apps' persuasive features and the operationalization and implementation of these features. OBJECTIVE: This systematic review set out to quantify and describe the persuasive features used in smartphone apps for psychosis, investigate whether there was any association between persuasive features and attrition or adherence rates. and document the quality of the included apps. METHODS: We searched electronic databases PsycINFO, PubMed, and Google Scholar for eligible papers published between the years of 2013 and 2025. Hand searches of reference lists were completed. Apps were selected if they were designed for people with psychosis and there were published empirical studies investigating the apps. Two reviewers from the review team (TT, JP, DK, and SD) independently screened papers and extracted data on adherence and attrition, as well as coded papers for evidence of persuasive features according to the persuasive systems design (PSD) model. Available data were synthesized descriptively and narratively. We attempted to access apps via app stores or by correspondence with the research team. RESULTS: We found 22 apps for psychosis, with 30 associated published papers. The persuasive features were as follows: personalization (18 apps); reminders (15 apps); suggestions (11 apps); tunneling and self-monitoring (10 apps); reduction (9 apps); liking (8 apps); social role (6 apps); rehearsal, praise, and similarity (5 apps); rewards (4 apps); simulation, real-world feel, and social learning (3 apps); surface credibility and normative influence (2 apps); and trustworthiness, social comparison, and social facilitation (1 app). Expertise, authority, third-party endorsements, verifiability, cooperation, competition, and recognition were present in zero apps. Features in the categories of primary task support and dialogue support were well represented, while social support and system credibility support were underused. It was found that there was no association between the number of persuasive features and attrition; an association between persuasive features and adherence could not be assessed. The quality of the apps could not be judged due to 20 of the 22 apps being inaccessible either through the research papers' authors or through app stores. CONCLUSIONS: Our findings indicate that in psychosis apps there is potential to include a broader range of persuasive features, which might maximize engagement. Psychosis apps may benefit from incorporating more features that leverage the persuasive impact of having users interact (social support) and incorporating features that emphasize system credibility and trustworthiness. Further studies could determine whether an increase in the number of persuasive features will impact app engagement and which features are most impactful in this context.
12 May 2026
Read appraisal →JMIR medical informatics
Cough Audio Recognition for Early Detection of Respiratory Diseases: Algorithm Development and Validation Study
BACKGROUND: Coughing is a common clinical symptom and a protective respiratory reflex closely associated with various respiratory system diseases. The acoustic characteristics of cough sounds are influenced by underlying pathological factors, with distinct acoustic signatures corresponding to different etiologies. Through rigorous analysis of these sounds, rapid identification and preliminary diagnosis of related conditions may be achieved. This approach holds great potential for broad application in mobile health and ubiquitous health platforms. OBJECTIVE: This study aimed to explore the application of acoustic analysis of cough sounds in the diagnosis of respiratory diseases to enhance the diagnostic efficiency of health care professionals. METHODS: In this study, we conducted extensive data collection, including voluntary cough audio recordings from patients diagnosed with respiratory diseases (eg, chronic obstructive pulmonary disease, lung cancer, COVID-19, and pneumonia) and from healthy participants. A total of 2610 audio samples were collected. We incorporated a channel attention mechanism (CAM) into the final convolutional block of each residual block in the ResNet18 neural network, thereby constructing the CAM-ResNet18 neural network model. The recorded cough audio samples were converted into spectrograms to form the input dataset for model training. The CAM-ResNet18 model was trained on the training set of this dataset, with iterative parameter adjustments until convergence was achieved. Finally, spectrograms from the test set were fed into the pretrained model for accurate classification of the cough-related conditions. RESULTS: Experimental results on the collected audio dataset demonstrate that the proposed CAM-ResNet18 model achieves an accuracy of 83.9% and an average F1-score of 82.52% in classifying 5 types of cough sounds. In comparison, the traditional ResNet18 model achieves an accuracy of 78.16% and an average F1-score of 78.29%, indicating a clear performance improvement with the integration of the CAM. CONCLUSIONS: The experimental results validate the effectiveness of the proposed method, highlighting its significant potential for application in clinical diagnosis.
12 May 2026
Read appraisal →Journal of medical Internet research
Effectiveness of mHealth Interventions Aimed at Promoting Physical Activity and Reducing Sedentary Behavior on Work-Related Outcomes Among Workers: Systematic Review
BACKGROUND: Mobile health (mHealth) technologies have gained popularity and may play a key role in promoting health-related behaviors. However, the impact of mHealth interventions on work-related outcomes remains unclear. OBJECTIVE: We aimed to provide an overview of the effectiveness of mHealth interventions designed to encourage physical activity and decrease sedentary behavior on work-related outcomes. METHODS: A search was conducted in MEDLINE, Web of Science Core Collection, the Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews, and Ichushi-Web for all publications up to September 12, 2025, without language or date restrictions. We included studies which (1) investigated the impact of mHealth interventions promoting physical activity and reducing sedentary behavior on work-related outcomes such as absenteeism, presenteeism, work productivity, work performance, and workability; (2) were designed as a randomized controlled trial or nonrandomized study of interventions; (3) were conducted among workers; and (4) were published as full-text original articles in Japanese or English. Two researchers independently conducted the screening of titles and abstracts, followed by a full-text review to confirm study eligibility and extract the data. We assessed the risk of bias using the Cochrane Collaboration Risk of Bias tool for randomized trials (RoB 2) and the Risk Of Bias In Nonrandomized Studies of Interventions (ROBINS-I). Findings were narratively synthesized. RESULTS: In total, 4022 records were identified from databases and other sources. After removing duplicates, 3631 studies were screened, and 17 studies (12 randomized controlled trials and 5 nonrandomized studies of interventions) met the inclusion criteria. Of the 17 eligible studies with 18,805 participants, 12 showed a favorable effect direction on ≥1 work-related outcome, but the majority exhibited a high or critical risk of bias, raising concerns about the quality of the research. There was considerable heterogeneity across studies. CONCLUSIONS: Scientific evidence suggests that health promotion efforts that incorporate mHealth components to encourage physical activity and reduce sedentary behavior may have a favorable impact on work-related outcomes. However, the low methodological quality of the studies and their high heterogeneity due to the participant characteristics, intervention content, use of diverse work-related assessment tools, and different follow-up periods make it difficult to draw definitive conclusions about the effectiveness of the interventions. This review is the first to comprehensively synthesize evidence on the impact of mHealth interventions on work-related outcomes, extending prior reviews that primarily focused on traditional workplace-based interventions without mHealth components. This review also contributes to the field by offering guidance for future studies, including the potential value of megastudy designs that evaluate multiple interventions within the same population over the same period on the same outcomes. As a practical implication, these findings suggest that mHealth technologies may serve as a complementary strategy for supporting occupational health and productivity.
12 May 2026
Read appraisal →Journal of medical Internet research
Automated Approaches of Text Simplification of Patient Education Materials: Scoping Review
BACKGROUND: Patient education materials (PEMs) often exceed the American Medical Association's (AMA) recommended sixth-grade reading grade level (RGL). While artificial intelligence (AI) offers potential for automated text simplification, concerns persist regarding linguistic quality, content fidelity, and the understandability of simplified PEMs by laypeople. OBJECTIVE: This scoping review maps existing evidence on automated language processing technologies for simplifying PEMs for laypeople. METHODS: Following the Joanna Briggs Institute (JBI) methodology and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guideline, 5 bibliographic databases (Ovid MEDLINE, Embase, CINAHL, PsycInfo, and IEEE Xplore) were systematically searched from 2019 to May 2025, supplemented by reference screening and gray literature searches. Eligible sources were peer-reviewed empirical studies published in English that examined large language models (LLMs), AI-supported writing assistants, AI-based conversational agents, or AI-supported tools designed for automatic text simplification of PEMs. Targeted outcomes included linguistic quality (ie, linguistic comprehensibility, linguistic correctness) and content fidelity (ie, factual accuracy, factual completeness) of simplified PEMs. Excluded sources comprised rule-based systems, manual text simplification, non-laypeople target groups, and technology-focused performance metrics. Results were synthesized via thematic analysis across the domains of targeted outcomes. In accordance with JBI methodology, a risk-of-bias assessment was not performed. RESULTS: A total of 31 eligible studies met the inclusion criteria, examining various LLMs, including OpenAI's GPT series, Gemini, Bard, Claude, Copilot, and Llama. Specifically, GPT-4.0 achieved the most consistent improvements in standardized readability metrics (eg, the Flesch-Kincaid Grade Level [FKGL]). However, achieving predefined target RGLs remained challenging across all LLMs, particularly at lower RGLs. Findings on content fidelity were inconsistent: despite high content similarity scores, content accuracy was often compromised. CONCLUSIONS: This is the first scoping review to comprehensively synthesize evidence on automated technologies for text simplification in PEMs. The review identified 2 critical validation gaps. First, no study examined the linguistic correctness (eg, grammar and typographical errors) of automatically simplified PEMs. Second, and most notably, the understandability of the simplified PEMs was assessed exclusively by experts, with no empirical evaluation involving laypeople. Although LLMs effectively reduce text complexity as measured by objective readability metrics, reliance on these formulas represents a critical limitation, as they serve merely as structural proxies. Improvements in readability do not guarantee the maintenance of content accuracy or laypeople's understandability. Current evidence is further limited by the lack of systematic prompt quality evaluation and the predominant focus on English-language PEMs in US contexts, restricting generalizability. This review provides a foundation for future research by highlighting the need for validated evaluation frameworks that encompass layperson testing and content verification. For clinical practice, LLMs should currently serve as assistive tools, with mandatory expert review remaining essential to verify content fidelity before disseminating LLM-simplified PEMs to laypeople.
12 May 2026
Read appraisal →Scandinavian journal of primary health care
Identifying factors influencing acceptance of artificial intelligence among general practitioners in Danish general practice: a cross-sectional web-based survey study
BACKGROUND: Despite rapid advances in artificial intelligence (AI), its adoption in Danish general practice remains limited and decentralized, relying on individual general practitioners' (GPs') decisions. This study aimed to evaluate Danish GPs' acceptance of AI and the importance of factors derived from established technology-acceptance models. METHODS: A cross-sectional web survey was conducted from mid-September 2024 to March 2025, using a culturally adapted version of a previously validated tool. The survey included 42 items: nine background items and 33 items covering 11 factors, including Medical and Non-Medical Performance Expectancy, Effort Expectancy, Social Influence (Medical and Patient), Facilitating Conditions, Perceived Trust, Anxiety, Professional Identity, Innovativeness and Behavioral Intention. Descriptive statistics, subgroup comparisons and Cronbach's alpha were provided. RESULTS: A total of 109 GPs and residents responded, with 84.4% (n = 92) completing the survey. Attitudes toward AI were generally neutral to positive, with mean scores above neutral in seven of the 11 factors. The most positive factors measured were Medical and Non-Medical Performance Expectancy. Scores related to Behavioral Intention were also high. Lower scores appeared in Perceived Trust, Facilitating Conditions and Anxiety. Older GPs (60 years and above) reported lower scores on Non-Medical Performance Expectancy and Social Influence (Medical); GPs in smaller cities showed more positive attitudes across several factors. Cronbach's alpha indicated good internal consistency on all but two scales. CONCLUSION: In summary, Danish GPs demonstrate a strong intention to adopt AI. However, issues persist as significant barriers regarding Anxiety, Perceived Trust and Facilitating Conditions. Implementation strategies considering context and clinician demographics are recommended.
11 May 2026
Read appraisal →JMIR formative research
Feasibility of Integrating Wearable Devices and Ecological Momentary Assessment for Real-Time Environmental Exposure Estimation: Proof-of-Concept Study
BACKGROUND: Environmental exposures such as heat and air pollution are critical determinants of health, yet traditional assessment methods rely on stationary monitors or residential address proxies that fail to capture the exposures that individuals experience throughout the day. OBJECTIVE: This pilot study aimed to assess the feasibility of integrating ecological momentary assessment (EMA), wearable devices, and continuous GPS tracking to capture real-time environmental exposures and to explore associations with concurrent health outcomes. METHODS: In total, 7 young adults (aged approximately 16 to 24 years; 5/7, 71% female) wore Fitbit Charge 6 watches from July 2025 to August 2025 (mean 28.1, SD 1.1 days), recording sleep quality and duration, resting heart rate, breathing rate, heart rate variability, and physical activity. GPS location measured at up to 5-minute intervals (mean 19.7, SD 25.8 measurements per day) was linked to ambient temperature, humidity, and air pollution data (particulate matter <2.5 um or <10 um in diameter, nitrogen dioxide, sulfur dioxide, ozone, and carbon monoxide) derived from monitoring stations, satellites, and climate models using data-integration algorithms accessed via an application programming interface. EMA surveys administered 3 times per day captured participants' emotional states and location (inside or outside). Feasibility targets were ≥3 GPS measurements per day, ≥1 survey completed per day, and complete sleep data on ≥50% of days. We examined exploratory bivariate correlations between environmental exposures, physiological measures, and self-reported mood, adjusting for multiple comparisons using false discovery rate correction. RESULTS: Of the 7 participants, 5 (71%) met predefined feasibility targets. Mean compliance included 565 (SD 457) GPS coordinates per participant, 1.4 (SD 0.2) EMA surveys per day, and complete Fitbit sleep data on 64% (SD 27%) of days. Surveys identified barriers to compliance, including perceived complexity of the system and forgetting to put the Fitbit watch back on after removing it. Exploratory correlations (6/7, 86% of participants with complete Fitbit data) revealed associations between nitrogen dioxide and heat exposure and reduced heart rate variability (a marker of parasympathetic tone), and between air pollutants (sulfur dioxide) and increased negative emotions. Heat exposure showed a paradoxical pattern of lower self-reported sadness but reduced heart rate variability with higher levels of heat exposure. Given the small sample size, these correlations should be considered preliminary and hypothesis generating rather than definitive findings. CONCLUSIONS: This study demonstrates that the multimodal integration of wearable devices, GPS tracking, and EMA is feasible for capturing real-time environmental exposures and concurrent health outcomes in young adults. This approach addresses critical exposure misclassification issues in environmental health research that relies on residential addresses as proxies. Preliminary patterns suggest complex relationships between environmental exposures and both physiological and emotional outcomes, warranting further investigation in larger, more diverse samples. This approach could inform future personalized environmental health interventions.
11 May 2026
Read appraisal →International journal of health care quality assurance
Understanding trust in mandatory telemedicine use during COVID-19: a modified health belief model approach
PURPOSE: This study explores the determinants of trust in telemedicine when its use was mandated during the COVID-19 pandemic in South Korea. It extends the health belief model by incorporating two service related constructs: service quality and perceived cost. DESIGN/METHODOLOGY/APPROACH: An online survey was conducted with 288 individuals who were confirmed COVID-19 patients and required to use telemedicine services during isolation. Partial least squares structural equation modeling was applied to examine the relationships between health beliefs including perceived severity, perceived susceptibility and self-efficacy, service perceptions such as service quality and perceived cost, trust in telemedicine and behavioral intention to use telemedicine services. FINDINGS: The results showed that self-efficacy, service quality and perceived cost positively influenced trust in telemedicine. In contrast, perceived severity and perceived susceptibility did not have a meaningful impact. Trust was found to be a key factor in shaping patients' intention to continue using telemedicine services. Overall, the proposed model was supported, highlighting the importance of confidence and service related perceptions in building trust under mandatory usage conditions. ORIGINALITY/VALUE: This study provides empirical evidence on trust formation under mandatory telemedicine use, a scenario rarely explored in the literature. It demonstrates that trust is primarily driven by users' self-efficacy and service perceptions rather than by health risk perceptions, offering practical guidance for enhancing remote care engagement during policy enforced use conditions.
11 May 2026
Read appraisal →Biomaterials science
Hydrogel-based wearable and implantable biosensors in health monitoring.
Hydrogel-based wearable and implantable biosensors have quickly established themselves as a paradigm-shifting platform for continuous health monitoring. They effectively reconcile the mechanical and chemical discrepancies between conventional rigid electronics and delicate biological tissues. This success is largely attributed to the hydrogel material's inherent advantages: high water content, a precisely tunable elastic modulus, and versatile functional chemistry. Collectively, these features provide structural, biological, and processing benefits that facilitate truly seamless bioelectronic integration. This Review systematically summarizes the latest advancements in hydrogel biosensors, placing emphasis on their distinctive material attributes. These include the synergistic ionic-electronic conduction, sensitive stimuli responsiveness, advanced antifouling surface chemistry, and exceptional tissue-mimicking compliance. We further delve into the fundamental physical and chemical sensing mechanisms, supported by representative applications spanning from the non-invasive, real-time analysis of sweat, tears, saliva, and interstitial fluids to the more complex domains of subcutaneous, cardiac, and neural implants. Finally, we candidly address the crucial hurdles that remain, such as long-term hydration stability, signal fidelity drift, and adverse immune responses. Concurrently, we highlight pioneering strategies to overcome these issues, including the adoption of zwitterionic designs for enhanced biocompatibility, nanocomposite reinforcement for mechanical robustness, and the utilization of transient biodegradability. By critically elucidating the intricate relationships governing hydrogel structure, chemistry, and bioelectronic function, this Review aims to chart the research trajectory toward the next generation of durable, self-healing, and fully biointegrated sensing systems.
11 May 2026
Read appraisal →American journal of speech-language pathology
Effect of the Fake Phone Call App on Patient Adherence Behavior and Motivation in Conversation Training Therapy: A Randomized Crossover Trial
PURPOSE: The Fake Phone Call app was previously developed and feasibility-tested for conversational target voice practice in resonant voice therapy. The purpose of the present study was to examine its effect on adherence and motivation for conversational practice in conversation training therapy (CTT) as well as its usability and utility in CTT. METHOD: Eleven patients referred for voice therapy served as their own control in a randomized crossover trial comparing 1 week of app-supported homework practice with 1 week of standard-of-care homework completion in CTT. Dependent variables included adherence and motivation variables. In addition, usability and utility as well as outcomes were examined at study completion. RESULTS: App support significantly increased practice frequency, practice fidelity, and goal commitment for practice. Usability and utility were good. Exploratory investigation revealed that patients randomized to receive the app condition first and the standard-of-care condition second had greater gains on all study variables and significantly greater self-reported generalization at treatment completion. CONCLUSION: Fake Phone Call support significantly improves adherence and motivation for CTT, with additional benefits when provided earlier in therapy.
11 May 2026
Read appraisal →JMIR formative research
At-Home Sleep Electroencephalography Assessment in Young and Older Adults Using a Novel Wireless Soft Electronics Sleep Monitoring System: Experimental Study
BACKGROUND: Sleep quality declines with age and is a known contributor to multiple chronic health conditions, including Alzheimer disease. Emerging evidence suggests that certain electroencephalography (EEG) neural signatures measured during sleep may be predictive of cognitive decline in older adults. Sleep EEG signals are traditionally measured using bulky, rigid, and uncomfortable equipment in an unfamiliar laboratory setting, which can negatively impact sleep signals. Due to these limitations, sleep EEG data acquisition is typically limited to a single night. OBJECTIVE: This study aimed to validate our recently developed portable, skin-like EEG monitoring patch for 7 nights in the home environment in a pilot sample of young and older adults by evaluating usability and acceptance, and replicating age-related differences in sleep architecture observed in the polysomnography literature. METHODS: Eighteen young adults and 18 cognitively unimpaired older adults without sleep disorders were enrolled (data from 11 young adults and 12 older adults were included in the analyses) in a 7-night study during which they wore novel, gel-free, wireless, ultrathin, skin-conforming, sleep monitoring, fabric-based patches. These patches were self-applied to the forehead and face for optimal usability and comfort. The patches incorporate laser-cut mesh electrodes with low-profile electronics (including a rechargeable battery and amplifier) and transmit EEG signals to a participant-controlled, Bluetooth-enabled, tablet-based data acquisition app. An automated algorithm was used to stage sleep and assess microarchitecture features from the EEG commonly impacted for each participant. Averages across nights were computed for these sleep features for each participant. RESULTS: Young and older adults reported that the sleep patch was easy to use and comfortable to wear. There was no loss of signal power over 7 nights of wear across participants (retained-data signal-to-noise ratio over the 7-d period: young adult, mean 20.69, SD 12.78, maximum 52.13, minimum 5.19; older adult, mean 22.10, SD 9.39, maximum 49.96, minimum 13.79). Most datasets not retained were lost due to poor reference electrode adhesion on the nose (75/101, 74% of lost datasets in young adults and 57/88, 65% in older adults). Trained sleep technologists verified that the retained datasets were of sufficient quality to be scored without difficulty. Expected age-group differences in sleep features were observed, including age-related reductions in stage N3 sleep (young adult, mean 18.55, SD 6.70; older adult, mean 10.40, SD 6.43; Mann-Whitney U=42.0; P=.01) and reduced sleep spindle density (young adult, mean 2.92, SD 2.24; older adult, mean 0.94, SD 1.33; Mann-Whitney U=45.0; P=.006). CONCLUSIONS: This study demonstrates that our novel, comfortable, wearable patch can reliably measure physiological sleep data over multiple nights at home in adults across the lifespan, thereby making multinight sleep assessment in cognitive aging studies and clinical research more accessible than traditional polysomnography. In future studies, the small, lightweight system, which is highly scalable, can be shipped inexpensively to participants' homes, making this technology and research accessible to individuals who may have difficulty traveling or who are hesitant to travel to a laboratory or clinic.
11 May 2026
Read appraisal →JMIR research protocols
Investigating the Implementation and Impact of AI-Assisted Fall Prevention in Hospitals: Protocol for a Multicenter, Multimethod Observational Study in Sweden (SAFE)
BACKGROUND: Artificial intelligence (AI) has the potential to enhance patient safety, particularly in the prevention of in-hospital falls. Recent advances in sensor-based AI systems allow for the analysis of complex, multimodal data to generate real-time alerts, enabling health care professionals to intervene before a fall occurs. By shifting from reactive responses to proactive risk management, these technologies may enable reductions in fall incidence and improvements in care outcomes. As a result, hospitals across Europe are increasingly adopting such systems. Nevertheless, empirical evidence concerning their routine implementation remains limited, particularly concerning their impact on patient safety, clinical workflows, and the usage of health care resources. Addressing these gaps is essential for effective and sustainable integration into hospital care. OBJECTIVE: This paper outlines the protocol for the multicenter, multimethod project SAFE (Safe AI-Assisted Fall Prevention Through Evidence), which investigates the implementation and impact of AI-assisted fall prevention in Swedish hospitals. METHODS: The research project is a collaboration between Halmstad University and hospitals in the Västra Götaland Region (VGR) and will, during 2026-2028, investigate an ongoing large-scale AI system implementation in VGR hospitals, covering up to 2400 patient beds. Using surveys, interviews, observations, and a retrospective study, it will track the implementation and impact over time. Two learning laboratories involving patients, their relatives, and health care professionals will be conducted to codevelop strategies for the implementation of AI-assisted fall prevention. RESULTS: The project will provide evidence-based insights and practical guidance on AI-assisted fall prevention. The findings will be relevant not only to patients, health care professionals, and hospital organizations, but also to policymakers and stakeholders involved in the digital transformation of health care. CONCLUSIONS: Although VGR serves as the primary research setting, the project's results will inform future similar initiatives in Sweden and offer transferable lessons for other health care systems internationally. This study will contribute to the evidence base on AI-assisted fall prevention in health care, supporting the responsible and scalable integration of such systems across diverse health care environments.
11 May 2026
Read appraisal →Biomedical physics & engineering express
Enhanced ResU-Net for brain tumor segmentation using EfficientNetB0, channel attention, and ASPP
Accurate and efficient segmentation of brain tumors is critical for diagnosis, treatment planning, and monitoring in clinical practice. In this study, we present an enhanced ResU-Net architecture for automatic brain tumor segmentation, integrating an EfficientNetB0 encoder, a channel attention mechanism, and an atrous spatial pyramid pooling (ASPP) module. The EfficientNetB0 encoder leverages pre-trained features to improve feature extraction efficiency, while the channel attention mechanism enhances the model's focus on tumor-relevant features. ASPP enables multi-scale contextual learning, which is crucial for handling tumors of varying sizes and shapes. The proposed model was evaluated on two benchmark datasets: The Cancer Genome Atlas Low Grade Glioma and brain tumor segmentation (BraTS-2020). Experimental results demonstrate that our method consistently outperforms the baseline ResU-Net and its EfficientNet variant, achieving dice similarity coefficient of 0.903 and 0.851, and HD95 scores of 9.43 and 3.54 for whole tumor and tumor core (TC) regions on the BraTS 2020 dataset, respectively. Compared to state-of-the-art methods, our approach shows competitive performance, particularly in whole tumor and TC segmentation. These results indicate that combining a powerful encoder with attention mechanisms and ASPP can significantly enhance BraTS performance. The proposed approach holds promise for further optimization and application in other medical image segmentation tasks.
11 May 2026
Read appraisal →Chinese medical journal
Artificial intelligence in atopic dermatitis: Current applications and future perspectives.
Atopic dermatitis (AD) is a prevalent and burdensome chronic skin disease, with profound effects on patient quality of life. Advancing its diagnosis and management remains a pressing clinical need. Artificial intelligence holds growing promise in addressing this challenge. This review outlines the current landscape of AD and explores the expanding role of AI in its care-from improving diagnostic accuracy and objective severity scoring to predicting therapeutic outcomes and personalizing treatment strategies. While these technologies are poised to transform dermatology practice, their successful integration will depend on overcoming technical and practical barriers through collaborative, interdisciplinary efforts. Further research and thoughtful implementation are essential to translate AI's potential into better, more precise patient care.
11 May 2026
Read appraisal →Journal of medical Internet research
Effects of Stand-Alone Digital Lifestyle Interventions on Weight-Related Outcomes in Adults With Overweight or Obesity: Systematic Review and Meta-Analysis of Randomized Controlled Trials
BACKGROUND: Obesity is a major global health concern, and scalable digital solutions are urgently needed. While digital lifestyle interventions (DLSIs) have shown promise, prior meta-analyses often included hybrid formats with human support, limiting insights into the effectiveness of fully digital interventions. OBJECTIVE: This study aimed to evaluate the independent effects of standalone DLSIs-defined as interventions delivered exclusively via digital platforms without in-person or adjunctive support-on anthropometric and dietary outcomes in adults with overweight or obesity. METHODS: We searched MEDLINE, Embase, PsycINFO, Web of Science, and the Cochrane Library from inception through March 4, 2026. Eligible studies were randomized controlled trials (RCTs) evaluating stand-alone DLSIs in adults with overweight or obesity. Interventions were included if they targeted diet or physical activity exclusively through digital platforms. We included fully automated, asynchronous, or one-to-many synchronous systems without individualized support. Studies involving hybrid interventions, including one-to-one synchronous human interaction, nonadult populations, or non-RCT designs, were excluded. Two independent reviewers performed study selection and data extraction. Risk of bias was assessed using the Cochrane Risk of Bias 2.0 tool (Cochrane Bias Methods Group). Meta-analysis used a random-effects model with the Hartung-Knapp-Sidik-Jonkman method, and heterogeneity was assessed using I2 statistics. The certainty of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation approach. RESULTS: A total of 19 RCTs involving 3556 participants were included. Stand-alone DLSIs significantly improved anthropometric outcomes compared to controls (standardized mean difference 0.26, 95% CI 0.14-0.38; 95% prediction interval [PI] -0.16 to 0.68; P<.001; 19 studies; n=3556; I2=56.1%), corresponding to an additional weight loss ranging from 2.62 kg to 6.55 kg, depending on the baseline body weight. Significant improvements were also found in dietary outcomes (standardized mean difference 0.26, 95% CI 0.04-0.48; 95% PI -0.29 to 0.81; P=.008; 8 studies; n=1365; I2=57.5%). Subgroup analyses for anthropometric outcomes revealed significant differences only by control group type (P<.001), with waitlist controls showing the largest effect. For dietary outcomes, no significant subgroup differences were found (P>.05). While most studies showed a low risk of bias, substantial statistical heterogeneity was observed in some outcomes. Consequently, the certainty of evidence for both outcomes was rated as moderate. CONCLUSIONS: This review is innovative as it is the first to isolate the pure efficacy of stand-alone DLSIs by excluding synchronous human support. Our findings provide moderate-certainty evidence that these tools are effective for weight management and dietary improvement without human intervention. While stand-alone DLSIs offer a highly scalable, cost-effective first-step intervention, the PIs included zero, and substantial heterogeneity was observed, suggesting that benefits may vary across settings. Future research should identify user characteristics that maximize engagement with unguided digital tools.
11 May 2026
Read appraisal →Journal of medical Internet research
Rheumatic Digital Twin: Proposed Machine Learning-Based Multimodal Framework to Inform Clinical Decision-Making
Rheumatic diseases are chronic, immune-mediated conditions characterized by significant heterogeneity in presentation and disease course. However, current clinical approaches often rely on snapshot-based assessments that fail to capture the complex longitudinal evolution of these conditions. To address these limitations and support the implementation of precision medicine, we present the design for the Rheumatic Digital Twin, a novel, modular conceptual framework intended to integrate heterogeneous multimodal data, ranging from electronic health records and clinical notes to imaging and omics, into a dynamic, computational representation of the patient journey. Our theoretical architecture addresses challenges related to data silos and variable availability of data modalities through a multistage approach that envisions the use of domain-specific foundation models to independently process distinct data modalities. To effectively model the temporal progression inherent in chronic diseases, the proposed design utilizes Transformer architectures, leveraging self-attention mechanisms to treat patient events, such as lab results or medication changes, as sequential data tokens. We describe how these unimodal representations would subsequently be fused via joint embedding techniques to construct a shared, multimodal representational space. Envisioned to function analogously to a recommender system, the Rheumatic Digital Twin framework is modeled to map patients into a latent space where proximity reflects clinical and biological similarity. By identifying "nearest neighbors," historical patients with comparable trajectories, the system aims to enable in silico cohorting, theoretically allowing clinicians to forecast key clinical events, predict treatment responses, and identify likely disease courses based on the outcomes of similar peers.
11 May 2026
Read appraisal →BMJ global health
Transforming reproductive healthcare delivery through telemedicine services expansion: evidence from a mixed-methods pilot evaluation in rural Ghana
BACKGROUND: In 2021, Ghana's National Comprehensive Abortion Care Standards and Protocols recognised telemedicine as an approved delivery model for early medical abortion (EMA). Following a successful pilot in Accra, MSI Reproductive Choices Ghana expanded their telemedicine model to more rural areas and broadened their package of sexual and reproductive health (SRH) services to include fertility evaluation. This study evaluates the expanded model. METHODS: A mixed-methods evaluation (January 2024 to March 2025) drew on routine clinical data, courier and call centre tracking, client feedback surveys, and qualitative, individual in-depth interviews with telemedicine clients to assess clinical safety, service utilisation and user experience. Quantitative analysis included descriptive statistics and unadjusted logistic and ordinal regression. Qualitative interviews with clients explored feasibility, acceptability and implementation challenges. A deductive, thematic analysis was conducted with the support of Delve qualitative research software and guided by the Consolidated Framework for Implementation Research. RESULTS: A total of 2721 clients accessed telemedicine services, with 70.0% receiving EMA and 29.5% short-term contraceptive methods. Among EMA clients reached for follow-up, 96.9% reported complete abortion with no further intervention. Satisfaction was high: 89.4% would recommend the service, and 78.2% would use it again. Qualitative findings highlighted that clients valued privacy, convenience and autonomy, particularly in stigmatised or crowded spaces. Barriers to accessing services included unclear eligibility criteria, limited digital access and misperceptions that the service was for abortion only. Most clients contacted the service between 4 and 6 weeks' gestation. Post-abortion contraception uptake was 24.8%, with no significant variation by age or location. Nearly half of clients reported having no other way to access care. CONCLUSIONS: Telemedicine is a feasible, safe and acceptable model for SRH delivery in Ghana. Findings will inform national scale-up and strategies to strengthen equity, communication and service integration.
11 May 2026
Read appraisal →JMIR formative research
Older Adults' Experiences Navigating Setup of Digital Health Technology: Implementation Report
BACKGROUND: Digital health and connected technologies may support better health outcomes among older adults, including those with multiple chronic conditions or low engagement in health behaviors. However, initial experiences with technology, including during unboxing, setup, and first use, can influence emotional reactions and perceptions and can ultimately determine sustained, meaningful use. Older adults with low technology experience or poor health may be particularly vulnerable to frustration, stress, or abandonment of devices when early interactions are negative. OBJECTIVE: The purpose of this implementation study was to closely observe the initial engagements with a telehealth treatment app and connected blood pressure monitor (BPM) among a group of older adults with low prior technology use and reported low health behavior engagement. The goal was to identify setup "pain points" that may influence initial impressions and intention to use the technology over time. METHODS: A total of 24 older adults (aged ≥65 years) were recruited for a 4-week trial of a telehealth app. Participants were provided with a box containing a tablet preloaded with the app, paper instructions, and a BPM and cuff. Researchers first conducted in-home ethnographic interviews with participants to observe the unboxing and setup process, documenting experiences with reading instructions, using the BPM, and engaging with customer support. Weekly check-in calls and a final exit interview captured ongoing experiences and likelihood of continued use. Interview recordings were transcribed and independently coded, guided by the unified theory of acceptance and use of technology. RESULTS: Most of the sample were White (20/24, 83%) and female (14/24, 58%). Negative experiences with the app's customer support were the top challenge for participants, with representatives providing confusing steps or conflicting terminology. Other common challenges were understanding instructions, connecting to Bluetooth, and correctly using the BPM. While 67% (16/24) of the participants indicated that they were likely or very likely to continue to use the app after the study ended at the end of week 1, this number dropped to 54% (13/24) by the end of the 4 weeks. Participants who reported lower technology self-efficacy at the beginning of the study also experienced frustration, anxiety, and embarrassment as friction with the setup process continued. CONCLUSIONS: First impressions of digital health apps play a critical role in influencing older adults' emotions and perceptions regarding the technology and may impact the likelihood of longer-term engagement. Those with lower technology self-efficacy are particularly susceptible to experiencing negative emotions such as frustration, stress, or shame. Mobile health apps and interventions targeting older adults should incorporate simplified instructions with clear, consistent terminology and well-trained customer support staff to improve the onboarding experience.
11 May 2026
Read appraisal →Journal of medical Internet research
Characterizing Physical Activity Trajectories Preceding Incident Major Depressive Disorder Diagnosis With Consumer Wearable Devices in the All of Us Research Program: Retrospective Nested Case-Control Study
BACKGROUND: Low physical activity (PA) is a well-established risk factor for major depressive disorder (MDD). However, the temporal dynamics of PA preceding an incident clinical diagnosis of MDD remain poorly characterized, particularly using long-term, objective measures collected in real-world settings. OBJECTIVE: This study aimed to characterize trajectories of wearable-measured PA during the year preceding incident MDD diagnosis and identify the timing of within-person changes. METHODS: We conducted a retrospective nested case-control study using linked electronic health record and wearable (Fitbit) data from the All of Us Research Program. Adults with at least 6 months of valid Fitbit PA data in the 12 months preceding diagnosis were included. Incident MDD cases were identified based on a first electronic health record-recorded diagnosis and matched to MDD-free controls on age, sex, BMI, and calendar time of diagnosis, with up to 4 controls per case. Daily steps and moderate to vigorous PA (MVPA) were aggregated into monthly averages. Linear mixed-effects models were used to compare prediagnostic PA trajectories between cases and controls over a retrospective time scale from -12 to 0 months. Among cases, within-person contrasts were used to identify when PA levels first showed statistically significant deviations relative to levels observed 12 months before diagnosis. Exploratory analyses assessed heterogeneity by demographic factors. RESULTS: The analytic cohort included 4104 participants (n=829, 20.2% incident MDD cases and n=3275, 79.8% matched controls; n=3355, 81.7% women; median age 48.4, IQR 36.3-61.3 years). Compared with controls, individuals who developed MDD exhibited consistently lower overall PA and significant downward trajectories in both daily steps and MVPA during the year preceding diagnosis (global trajectory tests; P<.001 for both outcomes). Differences widened progressively over time, indicating accelerating declines as diagnosis approached. Among cases, statistically significant changes in daily step counts emerged approximately 4 months before diagnosis (-145, 95% CI -253 to -37 steps vs month -12; P=.02) and reached -428 (95% CI -531 to -326) steps at diagnosis (P<.001). Declines in MVPA emerged approximately 5 months before diagnosis (-2.48, 95% CI -4.32 to -0.64 minutes; P=.02) and reached -5.61 (95% CI -7.35 to -3.86) minutes at diagnosis (P<.001). Furthermore, exploratory analyses suggested heterogeneity in prediagnostic trajectories across demographic subgroups, including steeper declines among men, more pronounced reductions in activity intensity at older ages, and persistently lower activity levels with flatter trajectories among individuals with obesity. CONCLUSIONS: Unlike prior studies lacking objective PA assessment before MDD diagnosis, this study linked wearable and clinical data to characterize long-term prediagnostic trajectories in real-world settings. We observed sustained within-person declines emerging 4 to 5 months before diagnosis, providing insights into temporal dynamics preceding clinical recognition. These findings suggest that wearable-based monitoring may offer scalable early signals for risk stratification, prevention, and intervention for MDD.
11 May 2026
Read appraisal →Palliative & supportive care
Relational-temporal dignity at the end of life: Ethical foundations for the DiRePal model in the algorithmic age
OBJECTIVES: The increasing presence of artificial intelligence (AI), electronic patient-reported outcomes (ePROMs), and digital infrastructures in palliative care is transforming how clinical encounters are organized and how suffering is interpreted. These technological shifts heighten the risk of relational compression and a reduction of dignity to measurable outputs. This paper proposes the DiRePal model (Relational-Temporal Dignity in Palliative Care) as a philosophical framework to re-examine dignity beyond coherent narrative identity or autonomy-centered ethics, emphasizing relational presence, temporal sensitivity, and structural conditions of care. METHODS: A philosophical-ethical analysis informed by narrative identity (P. Ricoeur), ethics of alterity (E. Levinas), capabilities theory (M. Nussbaum), and care ethics (J. Tronto). Critical readings of dignity frameworks, AI ethics, and digital health literature were synthesized to develop a relational-temporal account of dignity and 2 operational concepts: the temporal dignity indicator and the architecture of prudence. RESULTS: While digital tools can enhance communication and support anticipatory care, they also risk reducing patients to data profiles, narrowing listening practices, and eroding opportunities for narrative, silence, and relational presence. The DiRePal model reframes dignity as a fluctuating, co-constructed achievement that depends on temporal attentiveness, ethical listening, institutional conditions, and prudent integration of AI and ePROMs. It further expands dignity to include post-biographical dimensions such as memory, grievability, and digital legacy. SIGNIFICANCE OF RESULTS: End-of-life care in the algorithmic age requires an ethics that recognizes dignity as relational, temporal, and structurally mediated. The DiRePal model offers clinicians and institutions a conceptual grammar to resist technological reductionism, protect time for presence, and safeguard the narrative and post-biographical continuity of persons whose voices may be fragmented, vulnerable, or digitally extended.
11 May 2026
Read appraisal →JMIR formative research
The Role of Trust in Text Messaging for Promoting Patient Portal Activation Among Low-Income Patients: Quality Improvement Project
BACKGROUND: The increasing reliance on patient portals for electronic health records has widened the digital health care access gap, particularly among low-income and Medicaid-insured populations. However, resources exist to assist low-income patients with portal enrollment; in obtaining a free smartphone; and, in New York, in obtaining low-cost internet. Automated bidirectional SMS text messaging offers a scalable and cost-effective strategy for identifying low-income patients' digital health needs and eligibility for resources by using screening questions and providing tailored information on how to access available resources. OBJECTIVE: This study aimed to increase portal access among low-income patients using automated bidirectional SMS text messaging and assess its feasibility and acceptability. METHODS: This quality improvement initiative involved sending automated, bidirectional SMS text messages in English to 12,381 Medicaid-insured and/or low-income patients from a primary care practice. Messages assessed patients' digital health needs and provided adaptive, personalized resources and assistance for enrolling in the patient portal and for accessing digital technology. We assessed response rates and follow-up portal enrollment rates. We surveyed participants regarding the acceptability, appropriateness, and usability of the SMS text messaging intervention, as well as their subsequent use of the patient portal. We performed descriptive statistics and a binomial probability test. RESULTS: In total, 9.2% (1140/12,381) of patients responded to the SMS text messages, with 3.9% (481/12,381) opting out and 5.3% (659/12,381) actively engaging. Among respondents, 71.1% (469/659) completed the follow-up survey. Respondents were predominantly female (336/469, 71.6%), with ages ranging from 18 to 65 years or older. Most respondents rated the message's clarity (420/469, 89.6%), its usefulness (400/469, 85.2%), and the demonstration of care by their health team (350/469, 74.6%) favorably. Concerns regarding privacy (61/469, 13%) and trustworthiness (71/469, 15%) were noted. Notably, 71% of initially unenrolled patients activated their patient portals after the intervention (P=.007), exceeding the hypothesized expectations. CONCLUSIONS: Automated bidirectional SMS text messaging had mixed effects on promoting patient portal use among low-income patients. Response rates to SMS text messages were low when delivered from an unknown phone number. Among responders, most reported that these messages were useful and that they would recommend them to others. Research is needed to determine optimal strategies for introducing the program and vendor phone numbers to patients to improve engagement.
11 May 2026
Read appraisal →PloS one
FairCareNLP: An AI-driven patient review analyzer for healthcare.
OBJECTIVE: To develop and evaluate an automatic patient review analyzer that applies advanced Natural Language Processing (NLP) and machine learning methods to improve the efficiency, fairness, and accuracy of healthcare feedback analysis. MATERIALS AND METHODS: We designed a multi-component pipeline incorporating sentiment analysis, key theme extraction, clinical Named Entity Recognition (NER), and fairness modules. Bias mitigation was addressed through the integration of three complementary approaches: adversarial debiasing, Hard Debiasing, and Iterative Null-space Projection (INLP). Multiple BERT-based models (DistilBERT, BioBERT, RoBERTa-base, BERT-base-uncased) were trained and evaluated under varying hyperparameters and fairness/adversarial loss configurations. Model performance was assessed using accuracy, F1, recall, precision, AUC, Equalized Odds (EOD), and Word Embedding Association Test (WEAT) metrics. RESULTS: Adversarial loss ([Formula: see text]) consistently decreased model performance across accuracy, F1, precision, and recall. In contrast, Hard Debiasing and INLP improved WEAT scores while preserving or enhancing other metrics, with INLP yielding the best overall performance. Specifically, INLP with fairness loss improved EOD by 14%, gender WEAT scores by 15%, and achieved slight gains for ethnicity and socioeconomic WEAT scores. The best model achieved accuracy of 0.856, F1 score of 0.812, recall of 0.798, and precision of 0.829. The key theme analysis module identified 82% of expert-labeled themes, though 21% of patient comments lacked expert labels for valence or related attributes. DISCUSSION: Our results demonstrate the trade-offs between fairness and performance in bias mitigation strategies. While adversarial debiasing reduced predictive accuracy, INLP and Hard Debiasing improved fairness without significant degradation in task performance. Gender bias was easier to mitigate than multi-class sensitive attributes such as ethnicity and income. This difference indicates a need for fairness techniques designed for multi-class sensitive attributes. CONCLUSION: This work presents an NLP pipeline for patient feedback analysis with multiple debiasing strategies. This pipeline improves the fairness and accuracy of insights from unstructured patient reviews and supports inclusive patient-centered care.
11 May 2026
Read appraisal →JMIR human factors
Identifying Skill and Usability Barriers to Digital Health Tool Use Among Older Adult Patients in US Safety Net Clinics: Mixed Methods Study
BACKGROUND: Despite their benefits, digital health tools often face adoption barriers because of the digital divide. Identifying the fundamental user skills required to effectively navigate these tools and the usability barriers is essential to addressing disparities in use. OBJECTIVE: This study aimed to identify the skill and usability barriers to using digital health tools. METHODS: This study included English-, Spanish-, or Cantonese-speaking patients, aged ≥50 years, who received care at an urban safety net health system in the United States. Participants completed a survey examining sociodemographic characteristics and digital health tool use and were observed and video recorded as they navigated four digital health care tasks: (1) launch a video visit, (2) visit a health website through a URL, (3) log in to the patient portal, and (4) sign up for a patient portal account. Participants who could not independently perform the tasks received additional support. Tasks were conducted in English, while instructions and additional assistance were provided in each participant's preferred language. Video recordings were thematically coded to identify the fundamental skills needed for effective digital tool use and usability barriers in the design of digital tools. We examined whether task independence was associated with participant demographics and thematic categories using Kruskal-Wallis, χ2, and Fisher exact tests. RESULTS: In total, 74% (34/46), 52% (31/60), 71% (44/62), and 70% (43/61) of participants (N=64) independently completed digital tasks 1, 2, 3, and 4, respectively. Older age, minoritized races and ethnicities, non-English language preference, lower educational attainment, access to cellular data only or no internet access, and lack of a portal account were associated with a higher likelihood of requiring assistance or being unsuccessful at completing each task (P<.001, except for older age [P=.004]). The qualitative coding of video recordings identified 3, 4, and 6 categories of typing, navigation, and human-computer interaction (HCI) skills, respectively, as fundamental skills required to independently complete digital tasks. χ2 and Fisher exact tests indicated significant associations between most typing, navigation, and HCI categories and independent task completion. We coded usability barriers as one of 6 learnability challenges or 3 operability challenges. CONCLUSIONS: This study identified that independent use of digital health tools requires fundamental typing, navigation, or HCI skills as well as high usability of digital tools. The inclusion of 4 different digital tasks added specificity to the type of skills and usability considerations necessary to ensure accessibility of digital health tools to diverse older adults. This study underscores the need for vendors to cocreate digital health tools with historically excluded end users in mind. As health care systems expand digital tool adoption, they must distinguish fundamental skill gaps from usability barriers, as each may require different intervention strategies.
10 May 2026
Read appraisal →JMIR mHealth and uHealth
mHealth-Based Gamification Interventions to Promote Health Among Older Adults: Scoping Review
BACKGROUND: Healthy aging has emerged as a global priority. However, older adults' participation in health promotion programs remains low, and traditional health promotion models have achieved limited success in fostering sustained engagement among this population. Mobile health (mHealth)-based gamification interventions offer a promising way to address these challenges. However, no published reviews support or oppose the use of mHealth-based gamification interventions as health promotion strategies in older adults. OBJECTIVE: The study aimed to identify mHealth interventions using gamification to promote health among older adults. METHODS: Our scoping review was conducted following the Joanna Briggs Institute recommendations for scoping reviews and Arksey and O'Malley's framework. The process followed PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines and PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Literature Search Extension) checklist. A comprehensive literature search was conducted across 8 databases: PubMed, Scopus, Web of Science, Embase, Cochrane Library, CINAHL, PsycARTICLES, and IEEE Xplore Digital Library, from their inception to December 10, 2025. Two reviewers independently screened titles, abstracts, and full texts via Rayyan, with disagreements resolved by a third reviewer. RESULTS: This scoping review identified 11 studies. Only 1 article was published before 2022. The interventions were found to improve enjoyment and motivation (n=5), cognitive function (n=3), physical activity (n=2), and digital literacy (n=2). Individual studies also reported improvements in mental health (n=1) and adherence (n=1), a reduction in suicidal ideation (n=1), improvements in physical function (n=1), the promotion of social engagement (n=1), and the identification of mild cognitive impairment (n=1). Game elements used were ranked by frequency as progress, challenges, goals, levels, reward, sensation, storytelling or narration, leaderboard, surprise, and avatar. No research was found to use the game element of "social sharing." mHealth types included augmented and virtual reality-based training systems, wearable devices, mobile phones, tablets, and Windows platforms and devices. Notably, only 4 studies applied theoretical frameworks, and 3 omitted the concrete approach to gamification. CONCLUSIONS: As the first scoping review to identify and map mHealth-based gamification interventions for older adults, this study highlights their potential as an innovative approach to health promotion. By systematically synthesizing evidence regarding intervention designs, gamification strategies, and preliminary health outcomes, it establishes a foundation for future inquiry. However, this review is limited by the small number of included studies, precluding broad generalizations. Future research should assess long-term impacts, integrate theoretical frameworks, establish reporting guidelines, design personalized social-interactive interventions, and expand to broader health domains. Ultimately, these insights provide targeted guidance for developing age-appropriate digital health solutions, contributing to the realization of active aging.
10 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Telemedicine adoption, pandemic-related fear, and treatment adherence in cancer care during COVID-19: a prospective cohort study
PURPOSE: To evaluate the impact of telemedicine adoption and pandemic-related fear on treatment adherence, healthcare utilization, and supportive care processes among patients undergoing cancer therapy during the COVID-19 pandemic. METHODS: In this prospective cohort study, we followed 149 cancer patients over two years to evaluate telemedicine utilization, treatment modifications, and COVID-19-related fear. We employed validated psychometric tools, including the COVID-19 Fear Scale. RESULTS: Telemedicine adoption (24.8% of patients) was associated with fewer hospital visits (7.3 ± 3.2 vs 9.4 ± 4.1, p = 0.004) and reduced emergency room utilization (16.2% vs 46.4%, p < 0.001). Treatment modifications occurred in 55.9% of patients, predicted by COVID-19 infection (OR = 2.8, 95% CI:1.7-4.6) and neutropenia (OR = 2.1, 95% CI:1.3-3.4). High fear scores were linked to increased mortality (8.1% vs 2.7%, p = 0.032), disease progression (37.8% vs 21.4%, p = 0.018), lower treatment adherence (75.7% vs 87.5%, p = 0.041), and higher hospitalization rates (51.3% vs 24.1%, p < 0.001). CONCLUSION: This study demonstrates that telemedicine can reduce acute care utilization while treatment modifications and high pandemic-related fear significantly worsen cancer outcomes. These findings highlight the need for oncology teams to proactively integrate remote care strategies and targeted psychological support to maintain treatment adherence and mitigate adverse outcomes during health crises.
10 May 2026
Read appraisal →Current urology reports
Artificial Intelligence in Urologic Documentation: A Review of Emerging Capabilities and the Ongoing Need for Human Oversight
PURPOSE OF REVIEW: Clinical documentation continues to expand in volume and complexity, spanning outpatient encounters, inpatient summaries, patient-portal communications, and educational materials. These growing demands contribute to clinician burden and reduce time available for direct patient care. Artificial intelligence (AI) has emerged as a potential strategy to streamline documentation workflows. This review evaluates current AI applications in clinical documentation, with illustrative examples from urologic practice. RECENT FINDINGS: Ambient AI scribes can capture the bulk of outpatient encounters and generate structured draft notes that clinicians edit rather than write de novo. Large language models have shown promise in assisting with inpatient documentation and discharge summaries, often producing drafts that are coherent, readable, and shorter than physician-authored text. AI tools can also simplify patient education materials and translate dense radiology reports into accessible language. Across these domains, however, studies consistently demonstrate that AI-generated content remains vulnerable to factual errors, omissions, hallucinations, and misaligned emphasis, reinforcing the need for clinician oversight. Overall, emerging evidence supports a complementary relationship between clinicians and AI. Used as supervised drafting aids rather than autonomous authors, AI tools have the potential to ease documentation burden and create more time for direct patient care without diminishing the clinician's role in shaping the medical record.
10 May 2026
Read appraisal →Diagnostic and interventional radiology (Ankara, Turkey)
Foundation models for radiology: fundamentals, applications, opportunities, challenges, risks, and prospects.
Foundation models (FMs) represent a significant evolution in artificial intelligence (AI), impacting diverse fields. Within radiology, this evolution offers greater adaptability, multimodal integration, and improved generalizability compared with traditional narrow AI. Utilizing large-scale pre-training and efficient fine-tuning, FMs can support diverse applications, including image interpretation, report generation, integrative diagnostics combining imaging with clinical/laboratory data, and synthetic data creation, holding significant promise for advancements in precision medicine. However, clinical translation of FMs faces several substantial challenges. Key concerns include the inherent opacity of model decision-making processes, environmental and social sustainability issues, risks to data privacy, complex ethical considerations, such as bias and fairness, and navigating the uncertainty of regulatory frameworks. Moreover, rigorous validation is essential to address inherent stochasticity and the risk of hallucination. This international collaborative effort provides a comprehensive overview of the fundamentals, applications, opportunities, challenges, and prospects of FMs, aiming to guide their responsible and effective adoption in radiology and healthcare.
10 May 2026
Read appraisal →European journal of pediatrics
Metaphorical perceptions of secondary school students regarding the concept of generative artificial intelligence
UNLABELLED: The rapid integration of generative artificial intelligence (GenAI) tools, such as ChatGPT, into educational contexts has raised important questions regarding how adolescents conceptualize and make sense of these technologies. Understanding students' perceptions is essential for developing age-appropriate, ethical, and pedagogically sound approaches to AI use in secondary education. This descriptive qualitative study employed a phenomenological approach and metaphor analysis to explore secondary school students' perceptions of generative artificial intelligence. The study sample consisted of 332 students aged 14-18 years from four secondary schools in Türkiye. Data were collected using an open-ended prompt ("Generative artificial intelligence is like … because …") and analyzed through content analysis. Metaphors were categorized based on shared semantic and conceptual features, and inter-rater reliability was established using Cohen's kappa (κ = 0.92). Analysis revealed ten metaphor categories clustered under five overarching themes: generative artificial intelligence as (1) a source of knowledge, (2) a teaching and guiding entity, (3) a supportive and assisting tool, (4) a reflection of human intelligence, and (5) a dual-purpose (beneficial-risky) technology. Students most frequently conceptualized GenAI as a comprehensive knowledge source (e.g., book, encyclopedia) and as a human-like cognitive entity (e.g., brain, wise person). At the same time, metaphors reflecting ethical awareness and potential risks, such as misuse and overreliance, were also identified. The findings indicate that secondary school students hold multifaceted and nuanced perceptions of generative artificial intelligence, encompassing both educational opportunities and ethical concerns. These results highlight the importance of integrating AI literacy into secondary education in ways that promote critical thinking, responsible use, and awareness of GenAI's limitations alongside its potential benefits. CONCLUSION: It was determined that secondary school students perceive generative artificial intelligence ambivalently as both a useful tool and a source of ethical and emotional concern, highlighting the need for developmentally appropriate artificial intelligence literacy approaches. WHAT IS KNOWN: • GenAI tools such as ChatGPT are increasingly integrated into educational contexts and have the potential to support personalized learning, information access, and student engagement. • Existing research has primarily focused on educators' perspectives or higher education settings, while studies examining adolescents' perceptions of GenAI remain limited. WHAT IS NEW: • This study provides empirical evidence on secondary school students' metaphorical perceptions of generative artificial intelligence within a K-12 context. • Findings reveal that adolescents conceptualize GenAI in multifaceted ways, including as a knowledge source, teaching and guiding entity, supportive tool, reflection of human intelligence, and a dual-purpose (beneficial-risky) technology.
10 May 2026
Read appraisal →Journal of biopharmaceutical statistics
Revolutionizing cardiovascular disease classification through machine learning and statistical methods
BACKGROUND: Cardiovascular diseases (CVDs) include abnormal conditions of the heart, diseased blood vessels, structural problems of the heart, and blood clots. Traditionally, CVD has been diagnosed by clinical experts, physicians, and medical specialists, which is expensive, time-consuming, and requires expert intervention. On the other hand, cost-effective digital diagnosis of CVD is now possible because of the emergence of machine learning (ML) and statistical techniques. METHOD: In this research, extensive studies were carried out to classify CVD via 19 promising ML models. To evaluate the performance and rank the ML models for CVD classification, two benchmark CVD datasets are considered from well-known sources, such as Kaggle and the UCI repository. The results are analysed considering individual datasets and their combination to assess the efficiency and reliability of ML models on the basis of various performance measures, such as precision, kappa, accuracy, recall, and the F1 score. Since some of the ML models are stochastic, we repeated the simulation 50 times for each dataset using each model and applied nonparametric statistical tests to draw decisive conclusions. RESULTS: The nonparametric Friedman - Nemenyi hypothesis test suggests that the Extra Tree Classifier provides statistically superior accuracy and precision compared with all other models. However, the Extreme Gradient Boost (XGBoost) classifier provides statistically superior recall, kappa, and F1 scores compared with those of all the other models. Additionally, the XGBRF classifier achieves a statistically second-best rank in terms of the recall measures.
10 May 2026
Read appraisal →Annals of emergency medicine
Ambient Artificial Intelligence Scribe Adoption and Documentation Time in the Emergency Department
STUDY OBJECTIVES: To describe real-world adoption of an ambient artificial intelligence (AI) scribe in the emergency department (ED) and compare documentation time and note characteristics between ambient and standard encounters using electronic health record audit logs. METHODS: We performed a retrospective observational study of adult ED encounters at a tertiary academic medical center. Attending physicians could optionally use an ambient AI scribe to generate notes from patient-clinician conversations. We included single-attending encounters in core ED zones and excluded visits with human scribes. Electronic health record audit logs provided documentation of time during and after the shift, total electronic health record time, and note length. We summarized adoption by physician, zone, and acuity and compared medians between ambient and standard encounters. RESULTS: Among 8,740 eligible encounters, 976 (11.2%) used ambient AI. Thirty-five of 92 attendings (38%) used the tool, and a small group of high-frequency users accounted for most ambient encounters. Ambient use clustered in telemedicine and vertical-care zones (chair-based ambulatory care) and in lower-acuity patients, as well as those not requiring interpreters. Median on-shift documentation time was 2:45 min for ambient encounters versus 3:50 min for standard encounters (difference -1:05; -28%). Median total electronic health record time was 8:39 min versus 10:21 min (-16%), and ambient notes were shorter overall. CONCLUSION: Early ED implementation of ambient AI scribes showed low but highly skewed adoption, with physicians favoring lower acuity, noninterpreted encounters. When used, ambient AI was associated with shorter on-shift documentation time, total electronic health record time, and note length.
10 May 2026
Read appraisal →Carbohydrate polymers
Multifunctional starch-based eutectogels with high mechanical properties, adhesion, self-healing, fatigue resistance and degradability for flexible wearable sensing
Flexible wearable sensors have developed rapidly due to the urgent demands for human motion monitoring. Eutectogel-based soft materials have exhibited great potential for wearable sensing applications. However, it is still a challenge to develop multifunctional eutectogels with excellent mechanical properties, adhesion, self-healing, fatigue resistance and degradability. In this work, a multifunctional starch-based eutectogel was developed by a one-pot and in-situ photo-polymerization method. The eutectogel had excellent stretchability (2710%), high strength (5.43 MPa), toughness (94.38 MJ/m3), Young's modulus (10.23 MPa), adhesion, fatigue resistance (1000 cycles), degradability and a certain degree of self-healing ability. These properties were closely correlated to the synergistic effect among the amylopectin, polyacrylic acid and choline chloride via hydrogen-bond and electrostatic interactions. The mechanical properties of eutectogel can be adjusted by changing the starch content. A wearable sensor based on the eutectogel was realized accurate and stable monitoring of human joint motions. This study addressed the limitations of the dehydration and single function of the traditional hydrogels, and provides a novel way for the development of the multifunctional bio-based gels for next-generation flexible wearable devices.
9 May 2026
Read appraisal →Medical image analysis
Context-enriched contrastive auto-encoder with topology learning for medical hyperspectral image classification to diagnose tumors
Deep learning has emerged as a highly effective approach for the automatic classification of medical hyperspectral images (MedHSIs), facilitating the accurate diagnosis of diverse tumors. Most of current methods suffer from the challenges in the separability of latent representation and the preservation of topological structure. To remedy such deficiencies, we propose a novel context-enriched contrastive auto-encoder with topology learning (CCAET) based MedHSI classification for tumor diagnosis. Concretely, we initially employ an auto-encoder (AE) network as the foundational framework to achieve latent representation and data reconstruction. Subsequently, to enhance the separability of the latent representation, we integrate a context-enriched contrastive loss into the AE framework, where both individual pixels and their contextual information are used to redefine contrastive learning, so that small distances between intra-class features and large distances between inter-class features are achieved. Further, we construct a topology-preserved loss by incorporating a graph based approach into the foundational framework, which aims to maintain the topological structure during latent representation learning. Furthermore, we devise a gradient descent-based optimization scheme to optimize the overall loss of the proposed CCAET method, so as to obtain the final latent representation of MedHSI. Finally, we implement soft-max-based label prediction to obtain the classification results. Different from most existing methods, the CCAET approach considers both the separability of latent representations and the preservation of topological structure to enhance MedHSI classification performance. Extensive experimental evidence has been presented to substantiate that the proposed CCAET method outperforms several state-of-the-art techniques in terms of MedHSI classification based tumor diagnosis.
9 May 2026
Read appraisal →Artificial intelligence in medicine
Learning with less: A survey of deep learning in medical imaging under varying supervision levels
The rapid evolution of deep learning has significantly advanced the field of medical image analysis. However, despite these achievements, further progress is hindered by the scarcity of large, well-annotated datasets. To overcome this limitation, recent years have seen a growing focus on developing methods that leverage varying levels of supervision as alternatives to fully supervised learning. Instead of relying solely on complete annotations, these approaches employ partial annotations or other supervision strategies to train deep learning models. This paper presents a systematic review of deep learning methods for medical image analysis under different levels of supervision. To this end, we categorize these methods based on the level of supervision they rely on, encompassing categories such as no supervision, inexact supervision, incomplete supervision, inaccurate supervision, and only limited supervision. We further divide these categories into finer subcategories. For example, we categorize inexact supervision into multiple instance learning and learning with weak annotations. Similarly, we categorize incomplete supervision into semi-supervised learning, active learning, and domain-adaptive learning and so on. Furthermore, we systematically summarize commonly used datasets for different types of supervision in deep learning for medical image analysis and explore future research directions to conclude this survey.
9 May 2026
Read appraisal →Epilepsy research
Machine learning in epilepsy
Epilepsy is a complex neurological disorder characterized by pathological processes that unfold across multiple biological scales, from cellular excitability and synaptic integration to large-scale network dynamics observable in electroencephalographic (EEG) recordings. While traditional analytical approaches have provided valuable insights, they often fail to capture high-dimensional and nonlinear structure of contemporary electrophysiological and clinical datasets. Consequently, machine learning (ML) has emerged as a powerful analytical framework in epilepsy research, although its rapid adoption has revealed a growing gap between algorithmic performance and biological interpretability. This review examines ML methods operating across three analytically distinct yet interconnected levels: (i) unsupervised learning for cellular-level phenotyping using high-dimensional electrophysiological data; (ii) supervised learning for EEG-based seizure detection and prediction; and (iii) multiscale modeling frameworks integrating neuronal and network dynamics. Rather than providing an exhaustive catalog of algorithms, we focus on inferential assumptions underlying ML applications, the methodological pitfalls constraining generalization and clinical relevance, and how ML-derived representations can be interpreted within established neurophysiological theory. We highlight that unsupervised ML facilitates identification of latent excitability phenotypes and trajectories obscured in traditional univariate analyses, while supervised ML has substantially advanced automated seizure detection and prediction, despite persistent challenges related to data leakage, class imbalance, and ambiguous preictal labeling. We argue that the most promising direction lies in embedding ML within multiscale mechanistic models, where data-driven inference facilitates parameter estimation and hypothesis generation rather than black-box prediction. By prioritizing interpretability, rigorous validation, and cross-scale integration, ML-enhanced multiscale frameworks offer a path toward clinically actionable models of epilepsy.
9 May 2026
Read appraisal →Scientific reports
From intelligent models to clinical tools: the evolving landscape of AI in medical imaging
Integration of Artificial Intelligence (AI), particularly deep learning, into medical imaging represents a profound shift in diagnostic medicine, moving from purely descriptive analysis to advanced predictive and prescriptive analytics. This Collection explores the rapid advancement of AI-driven tools in their specific fields such as oncology, cardiology, ophthalmology and so on, highlighting their potential to improve diagnostic accuracy, workflow efficiency, and personalized treatment planning. However, significant challenges remain, including the heterogeneity of medical image data, the "black box" nature of some intelligent models, and the critical hurdles of clinical integration and validation. The research presented here addresses these frontiers, showcasing innovations in algorithm development, explainable AI, and translational application. This Editorial synthesizes the contributions and outlines the essential collaborative pathway-uniting computer scientists, clinicians, and regulatory bodies-required to translate algorithmic promise into robust, trustworthy, and equitable clinical tools that genuinely improve patient care.
9 May 2026
Read appraisal →Medical image analysis
Diversity-driven MG-MAE: Multi-granularity representation learning for non-salient object segmentation
Masked Autoencoders (MAEs) have grown increasingly prominent as a powerful self-supervised learning paradigm. They are capable of effectively leveraging inherent image prior information and are gaining traction in the field of medical image analysis. However, their application to feature representations of the non-salient objects, such as microvasculature, accessory organs, and early-stage tumors-is fundamentally limited by dimensional collapse problem, which diminishes feature diversity critical for non-salient structure discrimination. To address this, we propose a Multi-Granularity Masked Autoencoder (MG-MAE) framework for feature diversity learning: (1) We extend the conventional MAE into a multi-granularity framework, a global branch reconstructs global pixels, with a local branch recovering Histogram of Oriented Gradients (HOG) features, enabling hierarchical representation of both coarse-grained and fine-grained patterns; (2) Critically, in the local branch, a diversity-enhanced loss function incorporating Nuclear Norm Maximization (NNM) constraint to explicitly mitigate feature space collapse through orthogonal embedding regularization; and (3) A Dynamic Weight Adjustment (DWA) strategy that dynamically prioritizes hard-to-reconstruct regions via entropy-driven gradient modulation. Comprehensive evaluations across five clinical benchmarks-CCTA139, BTCV, LiTS, ACDC, and MSD Pancreas Tumour datasets-demonstrate that MG-MAE achieves statistically significant improvements in Dice Similarity Coefficient (DSC) scores for non-salient object segmentation, outperforming state-of-the-art methods. The code is available at https://github.com/zhangbbin/mgmae.
9 May 2026
Read appraisal →Journal of biomedical informatics
Model utility and explainability in federated learning - A case study in healthcare using fundus oculi datasets
OBJECTIVE: Introduce a case study for Federated Learning (FL) in healthcare, addressing challenges posed by patient privacy and limited large-scale datasets. Our goal is to assess the features learned by FL methods in a simulated, diverse setting that emphasizes realistic data heterogeneity, and to analyze the learned representations for their medical relevance using both local and global explainability techniques. METHODS: Six fundus oculi datasets were combined to simulate a diverse federated learning environment, representing heterogeneous data conditions. We evaluated three established FL methods against centrally trained models, assessing both predictive performance and the learned representations. Specifically, explainability techniques were employed to examine the features learned by the models, and local explanations were evaluated against attention maps annotated by ophthalmologists. Robustness against common biases in fundus datasets was also assessed. RESULTS: Our study found improvements in model utility (up to 9.97%) with FL methods compared to isolated training. Analysis of learned representations revealed that federated models predominantly learn the vertical cup-to-disc ratio, a crucial feature for glaucoma diagnosis, and demonstrated robustness against common biases. High agreement was observed between local explanations and ophthalmologist-annotated attention maps. CONCLUSION: This study demonstrates the benefits of FL systems in a healthcare scenario, providing a case study for evaluating federated systems beyond idealized benchmarks. Our findings highlight the potential of FL to not only improve model utility in privacy-sensitive medical domains but also to learn medically relevant features instead of spurious correlations.
9 May 2026
Read appraisal →Neural networks : the official journal of the International Neural Network Society
MGML: A plug-and-play meta-guided multi-modal learning framework for incomplete multimodal brain tumor segmentation
Leveraging multimodal information from Magnetic Resonance Imaging (MRI) plays a vital role in lesion segmentation, especially for brain tumors. However, in clinical practice, multimodal MRI data are often incomplete, making it challenging to fully utilize the available information. Therefore, maximizing the utilization of this incomplete multimodal information presents a crucial research challenge. We present a novel meta-guided multi-modal learning (MGML) framework that comprises two components: meta-parameterized adaptive modality fusion and consistency regularization module. The meta-parameterized adaptive modality fusion (Meta-AMF) enables the model to effectively integrate information from multiple modalities under varying input conditions. By generating adaptive soft-label supervision signals based on the available modalities, Meta-AMF explicitly promotes more coherent multimodal fusion. In addition, the consistency regularization module enhances segmentation performance and implicitly reinforces the robustness and generalization of the overall framework. Notably, our approach does not alter the original model architecture and can be conveniently integrated into the training pipeline for end-to-end model optimization. We conducted extensive experiments on the public BraTS2020 and BraTS2023 datasets. Compared to multiple state-of-the-art methods from previous years, our method achieved superior performance. On BraTS2020, for the average Dice scores across fifteen missing modality combinations, building upon the baseline, our method obtained scores of 87.55, 79.36, and 62.67 for the whole tumor (WT), the tumor core (TC), and the enhancing tumor (ET), respectively. We have made our source code publicly available at https://github.com/worldlikerr/MGML.
9 May 2026
Read appraisal →Journal of diabetes science and technology
Adherence Monitor for Measurement of Removable Cast Walker Wear-Time: Multiple Sensors and Predictive Analytics Improve Accuracy
BACKGROUND: Treatment of diabetes and its complications is a primary health care expense. Up to 25% of people with diabetes will develop diabetic foot ulcers (DFUs). Removable cast walker (RCW) boots commonly prescribed for DFU treatment, promote healing, and provide offloading and wound protection. Patient RCW removal for hygiene and wound care can lead to decreased adherence and treatment effectiveness. This study evaluated a new system for wear-time adherence measurement using multiple sensor types. METHODS: An electronic wear-time monitor was developed, which included internal and external temperature sensors, an accelerometer, and capacitive proximity foot and ankle sensors. Time-stamped and date-stamped data were saved once per minute for up to 22 days. Ten healthy volunteer subjects were recruited to wear an RCW for two weeks while keeping a diary of don/doff times. Sensor data were then compared with volunteers' wear diaries using confusion matrix predictive analytics. RESULTS: Algorithms were developed for data processing. Correlation coefficients between algorithms and diaries were calculated for individual and multiple sensor combinations. Differential temperature and accelerometer algorithms were significantly better at predicting subject wear-time than individual temperature sensor algorithms (P = .009, P = .001, respectively). Foot proximity had significantly better correlation with subject diaries than temperature (P = .024), and acceleration algorithms (P = .005). Multi-sensor analysis showed high correlation (.96) with wear-time from subject diaries. CONCLUSIONS: Removable cast walker wear-time can be accurately determined using an electronic data recorder and multiple sensors. Wear-time measurement accuracy can be improved using algorithms that operate on data from multiple sensors that use a variety of sensor technologies.
9 May 2026
Read appraisal →International journal of nursing studies
The potential of digital health technologies in saving nursing resources: A scoping review
BACKGROUND: The global nursing shortage crisis presents a significant challenge to healthcare systems. Digital health technologies, such as communication tools, automation systems, monitoring devices, and information platforms have been proposed as one solution to alleviate the issue by optimizing nursing resources. However, a comprehensive overview of the use and potential of these technologies in optimizing nurses' work and resources is still lacking. OBJECTIVES: The aim of this review is to provide an overview of (a) the digital health technologies used in nursing that may have potential to save nursing resources, (b) which indicators are used to measure the effectiveness of these technologies, and (c) which technologies are found to be effective in terms of saving nursing resources. METHODS: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews in accordance with the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis. The databases PubMed, CINAHL, and Web of Science were searched. Studies were included if they were peer-reviewed, quantitative, addressed nursing professionals, and considered digital health technologies that specifically aimed to save nursing resources. Relevant data were extracted and synthesized narratively, using an emerging framework. RESULTS: A total of 115 studies were included in this review. Digital health technologies fell into four categories: communication, automation, monitoring, and information technologies. All showed potential for saving resources, with monitoring technologies most often reported as having high potential. Indicators used to measure effectiveness focused on temporal, workforce, and workload resources, and related outcomes including quality of care, patient safety, and cost efficiency. Workforce resources were the most frequently optimized, followed by improvements in patient safety and quality of care. While positive effects were predominant, some studies reported adverse and insignificant effects, indicating variability in effectiveness. CONCLUSIONS: Digital health technologies offer promising opportunities to alleviate nursing resource shortages, but their potential seem to vary by type. Monitoring technologies showed the most consistent benefits, while communication and information technologies had mixed effects and automation technologies require further research to clarify their potential. Given the wide range of indicators used to measure potential resource savings, defining common, standardized indicators is essential for systematically assessing the impact of digital health technologies on nursing work and resources. This would enable comparability across studies, strengthen evidence-based decision-making and guide implementation strategies.
9 May 2026
Read appraisal →Journal of diabetes science and technology
Explainable Machine-Learning Models to Predict Weekly Risk of Hyperglycemia, Hypoglycemia, and Glycemic Variability in Patients With Type 1 Diabetes Based on Continuous Glucose Monitoring
BACKGROUND AND OBJECTIVE: The aim of this study was to develop and validate explainable prediction models based on continuous glucose monitoring (CGM) and baseline data to identify a week-to-week risk of CGM key metrics (hyperglycemia, hypoglycemia, glycemic variability). By having a weekly prediction of CGM key metrics, it is possible for the patient or health care personnel to take immediate preemptive action. METHODS: We analyzed, trained, and internally tested three prediction models (Logistic regression, XGBoost, and TabNet) using CGM data from 187 type 1 diabetes patients with long-term CGM monitoring. A binary classification approach combined with feature engineering deployed on the CGM signals was used to predict hyperglycemia, hypoglycemia, and glycemic variability based on consensus targets (time above range ≥5%, time below range ≥4%, coefficient of variation ≥36%). The models were validated in two independent cohorts with a total of 223 additional patients of varying ages. RESULTS: A total of 46 593 weeks of CGM data were included in the analysis. For the best model (XGBoost), the area under the receiver operating characteristic curve (ROC-AUC) was 0.9 [95% confidence interval (CI) = 0.89-0.91], 0.89 [95% CI = 0.88-0.9], and 0.8 [95% CI = 0.79-0.81] for predicting hyperglycemia, hypoglycemia, and glycemic variability in the interval validation, respectively. The validation test showed good generalizability of the models with ROC-AUC of 0.88 to 0.95, 0.84 to 0.89, and 0.80 to 0.82 for predicting the glycemic outcomes. CONCLUSION: Prediction models based on real-world CGM data can be used to predict the risk of unstable glycemic control in the forthcoming week. The models showed good performance in both internal and external validation cohorts.
9 May 2026
Read appraisal →Neural networks : the official journal of the International Neural Network Society
Alleviating noise memorization for adversarially robust few-shot learning
Few-Shot Learning (FSL) enables models to learn from just a few examples of new classes by leveraging knowledge from base classes. While FSL has made significant strides, its vulnerability to adversarial attacks-especially with limited data-has been overlooked. To address this, adversarial training is often used to build more robust models. However, we found that this approach can lead the model to memorize adversarial noise, which harms its ability to generalize. Hard labels exacerbate this issue by pushing the model toward perfect accuracy on adversarial examples, while also making it less robust to small changes in weights. To solve these problems, we propose Alleviation of Noise Memorization (ANM), a method that includes Adaptive Label Smoothing for more flexible supervision and Robust Weight Learning to enhance model stability. Our extensive experiments show that ANM effectively reduces noise memorization and improves generalization, outperforming current benchmarks.
6 May 2026
Read appraisal →Journal of affective disorders
Gaborformer: A method for depression detection through hybrid acoustic feature extraction and fusion
Depression, a complex and heterogeneous mental disorder, poses significant challenges for timely and effective detection, highlighting the need for advanced methodologies. Current technologies often fail to fully leverage deep learning's potential, potentially overlooking critical speech features. To address this, we propose Gaborformer, a novel framework for depression detection through hybrid acoustic feature extraction and fusion. Our approach introduces classified iterative neighborhood component analysis (CINCA) for feature selection, reducing dimensionality and extracting highly correlated depressive speech features. We design GaborNet, integrating learnable Gabor filters with CNN to fuse features, and incorporate a Conformer model to capture depression-related characteristics from high-dimensional signals. Extensive validation on the DAIC-WOZ and MODMA datasets demonstrates the efficacy of our approach, significantly outperforming previous studies. Our analysis underscores the importance of features such as MFCC and LSP in speech-based depression detection (SDD). Code is available at https://github.com/staygoldboy/gaborformer.
6 May 2026
Read appraisal →Annals of laboratory medicine
Evaluation of the Performance of Advanced Large Language Models in Laboratory Medicine Using Residency Examinations
BACKGROUND: Recent advancements in large language models (LLMs) have accelerated their integration into clinical domains, including laboratory medicine. The performance of LLMs in answering board-level laboratory medicine questions has not been comprehensively evaluated. Given the importance of diagnostic accuracy in this field, rigorous and objective evaluations of LLM capabilities are essential. METHODS: We assessed 12 LLMs from OpenAI, Anthropic, and Google using 320 Korean Residency Examination questions (2021-2024) spanning six laboratory medicine subspecialties. Standardized prompts were provided via their application programming interfaces under deterministic settings (temperature=0). Questions were administered thrice to assess response reproducibility. Outputs were compared with validated answers and analyzed for accuracy, reasoning quality, and error typology. RESULTS: Google's Gemini 2.0 Pro achieved the highest accuracy (80.0%), followed by OpenAI's GPT-4.5 (77.2%) and Anthropic's Claude 3.7 Sonnet (74.1%). Accuracy decreased as the difficulty of questions increased (78.0% for easy vs. 45.1% for challenging). Subspecialty performance varied. Al models underperformed on questions on transfusion medicine (mean accuracy: 38.8%), primarily because of limitations in domain-specific and regional knowledge representations. Incorrect answers primarily resulted from reasoning errors. Reproducibility exceeded 95% for most models; however, some residual non-determinism appeared even with greedy decoding (temperature=0). CONCLUSIONS: LLMs demonstrated substantial potential for integration into laboratory medicine, particularly in clinical chemistry and immunology. Performance inconsistencies (particularly for high-difficulty questions) and knowledge gaps (notably for transfusion medicine) highlight the necessity for further development-potentially including domain-specific fine-tuning and retrieval-augmented generation integration-and robust expert oversight before clinical application.
6 May 2026
Read appraisal →Psychiatry research
Physiological correlates of emotional dysregulation in youth with psychopathology: an exploratory study using wearable technology
The failure to regulate one's own emotions, defined as Emotional Dysregulation (ED), has become a diagnostic challenge in the last decades. Peripheral indexes of functioning of the autonomic nervous system (ANS) activity emerged as reliable transdiagnostic biomarkers of psychopathology. The present study aimed to evaluate the feasibility of wearable sensors in capturing ANS correlates of affective reactivity to emotionally salient videoclips in a stimulation protocol conducted in a clinical sample of youths. Thirty subjects (mean age: 13.93 ± 11.74) were recruited and presented with videoclips showing emotional content (joy, fear, anger, sadness, and anger). Before (Baseline), during (Task), and after (Recovery), the registration, electrocardiogram (ECG), and electrodermal response (EDR) were recorded using wearable technologies. Several indices related to the activation of sympathetic and parasympathetic nervous system were extracted. These indices were compared among the different phases and emotions and correlated with clinical measures. Preliminary results showed that some emotions are more able to elicit ANS response than others, and there is a correlation between physiological activation and clinical profile. These preliminary findings could help to explore the mechanisms of ED in psychopathology and can be useful to guide behavioral and pharmacological treatment. This study supports the success of wearable-based protocols for monitoring emotional reactivity in youth with psychopathology.
6 May 2026
Read appraisal →Microscopy research and technique
Deep Learning Integration in Optical Microscopy: Advancements and Applications
Optical microscopy is a cornerstone imaging technique in biomedical research, enabling visualization of subcellular structures beyond the resolution limit of the human eye. However, conventional optical microscopy faces challenges such as optical aberrations, diffraction-limited resolution, low signal-to-noise ratio (SNR), and poor contrast. The exponential growth of bioimaging data further underscores the need for advanced computational tools. Deep learning (DL) is a subset of machine learning that has emerged as a transformative approach to address these limitations, offering enhanced precision, reduced manual intervention, and diminished reliance on domain-specific expertise for image reconstruction, enhancement, and analysis. This review explores the integration of DL into optical microscopy, focusing on key applications including image classification, segmentation, and computational reconstruction. We examine prominent DL architectures such as convolutional neural networks (CNNs), U-Nets, residual networks (ResNets), and generative adversarial networks (GANs)-and their role in advancing diverse microscopy modalities. These frameworks enhance image quality, improve quantitative analysis, and democratize access to high-performance microscopy. Additionally, we discuss persisting challenges, including the demand for large, annotated datasets, dynamic sample variability, model interpretability, and potential data biases. Collectively, DL is poised to revolutionize optical microscopy, shaping its future developments in biomedical imaging.
6 May 2026
Read appraisal →Artificial intelligence in medicine
TADynFed: Dynamic modality-adaptive federated learning with tissue-aware disentanglement for cross-disease analysis
Federated learning (FL) enables collaborative medical image analysis across decentralized institutions while preserving data privacy. However, real-world deployment faces critical challenges: modality heterogeneity, where clients possess incomplete or varying medical image modalities, and cross-disease generalization, requiring models to adapt to unseen pathologies with anatomical consistency. Existing methods like FedAvg, FedProx, IOP-FL, and PntTranForFL assume uniform modality availability and static client participation, leading to poor performance under realistic clinical constraints. We propose TADynFed, a novel framework for the Heterogeneous Federated Learning (HFL) paradigm, which addresses both data and modality heterogeneity through, a tissue-aware disentanglement strategy that decouples modality-tailored and modality-shared features, a dynamic prototype memory bank for missing modality compensation, an adaptive aggregation mechanism that accounts for client reliability and tenure. We evaluate TADynFed using multi-disease MRI datasets including BraTS21 along with cross-domain imaging data from CheXpert (chest X-ray) and Hep-2 (microscopy), simulating a 13-client FL environment. The proposed framework achieves an average mDice score of 66.03%, significantly outperforming baseline methods such as PointTransformerFL at 58.60% and FedAvg at 53.06%. It also attains the lowest boundary alignment error with an ASD of 1.85 mm and HD95 of 8.70 mm, surpassing existing approaches by notable margins. In terms of calibration stability, TADynFed records an ECE of 0.09, indicating superior confidence reliability. Furthermore, it demonstrates high communication efficiency with only 76 MB of data exchanged per round, compared to 95-105 MB in other frameworks. These results validate TADynFed's ability to maintain high segmentation accuracy, boundary precision, and calibration stability while minimizing bandwidth usage. Outperforming existing frameworks by significant margins, TADynFed demonstrates robust boundary alignment, superior calibration, and efficient communication. It also exhibits strong cross-disease transferability without retraining. By integrating structured representation decomposition, prototype-guided fusion, and client-adaptive learning, TADynFed establishes a new benchmark in realistic, heterogeneous, and mix-modal federated medical imaging systems. Code supporting this study TADynFed.
5 May 2026
Read appraisal →Journal of the European Academy of Dermatology and Venereology : JEADV
A deep learning-clinical nomogram hybrid for predicting sentinel lymph node metastasis in melanoma
BACKGROUND: Melanoma is a life-threatening skin malignancy, with sentinel lymph node metastasis (SLNM) serving as a critical prognostic factor. While machine and deep learning models using histopathology have focused on melanoma diagnosis, limited efforts have aimed to predict SLNM. OBJECTIVES: This study aimed to develop a collaborative machine and deep learning model that integrates histopathological patches and clinical data to predict SLNM in patients with invasive melanoma, thereby aiding decision-making regarding sentinel lymph node biopsy. METHODS: A total of 78 invasive cutaneous melanoma cases (43 SLNM-positive and 35 SLNM-negative) from the University of Yamanashi Hospital (2007-2022) were divided into training (n = 60) and test (n = 18) datasets. A clustering-constrained attention multiple-instance learning (CLAM) model was developed using haematoxylin and eosin-stained whole-slide images. Among the four CLAM variants, the model using a ResNet50trunc encoder trained on whole-tissue regions (CLAM-R50) demonstrated the best performance and was further integrated with a clinical nomogram. Hybrid modelling was performed using logistic regression, a support vector machine and gradient boosting. RESULTS: CLAM-R50 achieved an area under the receiver operating characteristic curve (AUROC) of 0.875, while the clinical nomogram achieved an AUROC of 0.826 on the test set. The gradient boosting hybrid model, termed the Melanoma Indicative Scorer for Sentinel Lymph node Evaluation (MISSLE), demonstrated the highest AUROC of 0.950 (95% bootstrapped confidence interval (CI): 0.933-1.00) on the test set. CLAM-R50 attention maps identified pale cytoplasm and plasma cell-like tumour cells as predictors of positive SLNM, whereas dense melanin deposition and lymphocyte-like tumour cells predicted negative SLNM. CONCLUSIONS: MISSLE integrates histopathological and clinical data to predict SLNM with high AUROC, surpassing the previous reports and offering a tool for clinical decision-making. Limitations include the single-centre design and predominantly Japanese cohort, limiting generalizability. The code and models are publicly available for external validation.
5 May 2026
Read appraisal →Journal of applied gerontology : the official journal of the Southern Gerontological Society
Anomaly Detection Technologies for Dementia Care: Monitoring Goals, Sensor Applications, and Trade-offs in Home-Based Solutions-A Narrative Review
Anomaly detection technologies are increasingly used to monitor people living with dementia (PLwD) in home settings, addressing critical behaviors such as wandering, sleep disturbances, and agitation. This narrative review examines technologies used for detecting behavioral anomalies, the activities they monitor, and the trade-offs between their benefits and limitations. A systematic search across MEDLINE, IEEE Xplore, ACM Digital Library, and Web of Science identified 78 studies, categorized through thematic analysis. Three primary motivations emerged: early diagnosis, safety monitoring, and reducing caregiver stress while promoting autonomy. Technologies include GPS tracking, wearables, environmental sensors, and smart home systems, each with benefits like real-time alerts and non-intrusive monitoring but also challenges such as user compliance, false positives, and privacy concerns. While these systems enhance safety and autonomy, improving sensor accuracy, integrating AI for personalized interventions, and addressing ethical concerns are essential for long-term effectiveness and supporting the well-being of both PLwD and caregivers.
5 May 2026
Read appraisal →Science advances
SkinECG: An orthogonal remote powering wearable skin-like sensor
The Internet of Things (IoT) era witnessed a surge in wearable devices for the human body, each requiring a power source that supports long-term operation. However, batteries are depletable and require replacement, while the effectiveness of energy-harvesting techniques is location specific. We developed the SkinECG system, an orthogonal remote powering skin-like electrocardiogram (ECG) sensor. It is powered by body-coupled powering (BCP) using power acquired optimally via suitable energy harvesters at different locations on the human body, solving the location mismatch between the energy-harvesting source and sensor. The remote Orthogonal Energy Harvesting Network (O-EHN) effectively transfers power from multiple energy-harvesting sources without internode interference and cancellation. We demonstrated the 0.71-mm-thin, flexible SkinECG system that conforms to the human chest and detects ECG signals without any cumbersome batteries or directly connected power for long-term health monitoring.
5 May 2026
Read appraisal →Journal of the American Society of Nephrology : JASN
Streamlining the Histopathologic Workflow in Diabetic Kidney Disease with Artificial Intelligence
KEY POINTS: Artificial intelligence models effectively generalized across studies and animal models and reduced translational gaps when applied to human biopsies. Artificial intelligence assistance reduced study evaluation turnaround times by up to 90% versus manual whole slide imaging scoring, matching expert-level performance. Self-supervised learning captured diabetic kidney disease-relevant features and mitigated expert-specific bias. BACKGROUND: Assessment of pathology end points in animal models of diabetic kidney disease is time-consuming and prone to expert bias. In addition, the sparsity of human kidney biopsy data hinders the development of translational models from animals to humans. METHODS: We developed an artificial intelligence (AI)-driven workflow to streamline histopathologic assessments in animal models of diabetic nephropathy. Our approach ( 1 ) detected glomeruli in whole slide images, ( 2 ) enabled fast expert scoring through an annotation tool, and ( 3 ) automated scoring. By leveraging unlabeled preclinical data for self-supervised learning, we enhanced AI scoring performance, reduced expert bias, and enabled the translation of AI scoring from animal models to human biopsies. To translate AI models from preclinical studies to human biopsies, we introduced a method that adjusted the feature extractor to human-specific features during inference without the need for annotated examples. RESULTS: Our annotation tool streamlined glomerular scoring, reducing turnaround time by 80%. Supervised AI models outperformed expert agreement and further reduced turnaround time by 90%, demonstrating generalization across studies involving both the same and different animal models. Without supervision, the self-supervised model achieved a κ value of 0.78, effectively identifying glomerular changes without guidance. Incorporating self-supervised learning into supervised training improved performance to κ=0.84 and reduced bias compared with individual experts ( P < 0.001). Our translational approach achieved a κ value of 0.63 on human glomeruli, although the model was trained exclusively on mouse glomeruli scores, reducing the translational gap by 45%. CONCLUSIONS: In this study, we accelerated and enhanced pathology readouts in a real-life pharmaceutical industry setting. We show that AI-assisted scoring reduced pathologists' workload and expedited study assessments. Self-supervised learning captured intrinsic properties of kidney morphology without expert annotation and reduced expert bias and translational discrepancies, greatly facilitating translational activities in drug development for patients with diabetic kidney disease.
5 May 2026
Read appraisal →The Journal of nursing education
Enhancing Nursing Students' Interdisciplinary Competence via ARCS-Based Digital Health Care Design
BACKGROUND: Digital literacy is increasingly essential in modern nursing education, yet many students lack confidence and prior exposure to digital tools. METHOD: A 6-week project-based course, guided by the ARCS motivational model comprising Attention, Relevance, Confidence, and Satisfaction, engaged 40 first-year nursing students in designing health care applications using the Figma digital platform. The instructional process was organized into three progressive phases: motivational induction, design implementation, and final presentation. RESULTS: Students demonstrated a significant increase in learning motivation (t = 15.784, p < .001) and rated Figma's usability as excellent (System Usability Scale = 84.87). Reflections indicated enhanced digital confidence, interdisciplinary collaboration, and the ability to translate nursing knowledge into practical applications. CONCLUSION: Integrating motivational strategies with project-based learning and an intuitive digital platform effectively promoted student engagement, skill acquisition, and meaningful learning. This model offers a scalable approach for embedding digital competence into nursing curricula.
5 May 2026
Read appraisal →Science advances
An AI-driven, wearable, conformal ring system for real-time and user-independent sign language interpretation.
Sign language translation systems have long aimed to bridge the communication between signers and nonsigners. However, preliminary systems rely on glove-type wearables or wired sensor arrays, which constrain hand movement, reduce comfort, and require nonpersonalized sensor positions that limit adaptability across users. Here, we introduce a wirelessly connected, ring-type sign language translator (WRSLT) designed to overcome these limitations by enabling full finger mobility through independent sensor rings and multilink communication. The system supports static and dynamic gesture detection using selected fingers via quantitative relevance analysis and achieves robust user-independent performance without per-user calibration. WRSLT demonstrated high recognition accuracy on large-scale datasets comprising 100 American Sign Language and 100 International Sign Language words, achieving 88.3 and 88.5% accuracy, respectively, under unseen-user conditions (i.e., test users not included in model training). Furthermore, a custom sequential word detection framework enables sentence-level translation from continuous signing input without requiring separate training on entire sentence structures.
5 May 2026
Read appraisal →Journal of advanced research
Flexible wearable medical devices: from material innovations and data processing to intelligent healthcare applications
BACKGROUND: Flexible wearable medical devices drive healthcare transformation via non-invasive, real-time physiological monitoring and personalized management. Traditional rigid devices lack long-term comfort, while chronic disease care and telemedicine demand reliable, patient-centered solutions. Advances in materials (carbon nanomaterials, liquid metals, hydrogels) enable stretchable, biocompatible substrates adapting to bodily movements. Innovative sensing (PPG, EEG, sweat-based detection) tracks blood pressure, glucose, and neural signals accurately. Key challenges include insufficient sensor flexibility, lagging supervision, data security issues, and large-scale production difficulties, requiring interdisciplinary collaboration. AIM OF REVIEW: This review integrates cutting-edge advances in flexible wearable medical devices, covering material innovations, sensing breakthroughs, power management, and AI-driven data analysis. It evaluates strategies to enhance biocompatibility of carbon composites, liquid metals, and hydrogels, analyzing improvements in device sensitivity, biocompatibility, and real-world applicability. It critically discusses machine learning's role and limitations in chronic disease management, telemedicine, and sports optimization. Key unresolved challenges include lagging regulations, privacy risks in multi-source data fusion, mismatched sensing development, and large-scale production difficulties. The aim is to provide a scalable, secure, and clinically translatable roadmap for future research. KEY SCIENTIFIC CONCEPTS OF REVIEW: Core technologies involve carbon nanomaterials (e.g., graphene), liquid metals (structurally optimized for balanced conductivity and flexibility), and hydrogels (preferred as flexible substrates for mimicking natural soft tissues' mechanics and biocompatibility). Sensing technologies (PPG, EEG, sweat-based detection) enable non-invasive, real-time monitoring of vital parameters like blood pressure, glucose, and neural signals. AI decodes complex physiological data for predictive diagnosis, while IoT/5G integration advances telemedicine. Current limitations include poor sensor flexibility, lagging regulations, and large-scale production hurdles, contrasting with trends like advanced sensing, new materials, and hybrid diagnostic-therapeutic systems-highlighting flexible wearables' potential in intelligent healthcare.
5 May 2026
Read appraisal →Experimental eye research
Automated segmentation of pterygium lesions using multiscale deep learning networks
Pterygium is an eye condition that needs to be identified at an early stage so that its progression can be mitigated in order to avoid the possible threat of visual impairment. One of the important low-level modules in determining the severity level of the pterygium automatically is to measure the extent of fibrovascular tissue encroachment onto the corneal and pupil regions. Therefore, it is important to develop a semantic segmentation method to map and quantify the lesion tissues accurately, especially through the deep learning technique. As the pterygium condition progresses from the trace stage to the severe stage, the lesions will generally become bigger but maintain more or less the same shape, which looks like a wedge. Taking inspiration from this unique pattern of pterygium lesions, this work aims to explore multiscale deep learning networks to capture the variable scales of the lesion features effectively. All the proposed multiscale modules are embedded at the bottleneck layer of the base UNet architecture, whereby the feature map size is the smallest to reduce the potential increment in the computational burden. Apart from that, the total number of convolutional channels is fixed to 1024, whereby the multiscale module replaces the first convolutional set at the UNet's bottleneck layer. Two main multiscale modules have been explored, which are spatial pyramid pooling (SPP) and atrous spatial pyramid pooling (ASPP) modules. Furthermore, each of these modules has been expanded by analyzing the equal-flow (EF) and waterfall-flow (WF) patterns in constructing the parallel paths that represent the multiscale features. According to the simulation results, the best segmentation network is UNet, which has been embedded with three parallel paths of the EF-ASPP module that produces the lowest Hausdorff distance of 16.75 pixels. As a result, the severity level of pterygium can be better predicted through accurate extraction of the lesion map. This work can be further improved by analyzing different network architectures of the multiscale module, especially networks that focuses on the larger feature maps size, but surely with a trade-off in computational burden.
5 May 2026
Read appraisal →Microvascular research
OPU-NET-DADENA: Optimized deep learning ensemble with u-net segmentation for early detection of diabetic retinopathy
One of the most significant microvascular complications of diabetes mellitus (DM) is diabetic retinopathy (DR). In the early stages, patients with DR may not exhibit any noticeable symptoms. It is a diabetic disorder that damages retinal blood vessels in the eyes. At first, there are no symptoms or sporadic visual issues. When it worsens, it affects both eyes and can lead to partial or total blindness. A person who already has DM is therefore constantly at a higher risk of developing the condition. Early identification can prevent the possibility of total and irreversible blindness. Therefore, needs an efficient and early diagnosis system. So, this paper proposes a new deep-learning methodology in a specific Deep Siamese DenseNet for early detection of DR. The proposed method is accomplished through various steps, such as Data Collection, Preprocessing, Augmentation, Segmentation, Feature Extraction, Feature Selection, and Detection. An adaptive histogram equalization approach named Contrast Limited Adaptive Histogram Equalization (CLAHE) is used to preprocess input images, which reduces amplification of noise. Then, the segmentation is done by the Optimized U-NETs. Next, features of the contrasted retinal images are extracted by residual attention EfficientNet (RA-EfficientNet). Then, the optimal features are selected by a hybrid Reptile Search Algorithm (RORS). Finally, the deep learning methodology includes DarkNet, DenseNet 201, and NasNetMobile used to detect diabetic retinopathy at an early stage. The model is implemented in MATLAB and evaluated using accuracy, precision, F-score, specificity, sensitivity, MCC, NPV, FPR, and FNR. The proposed approach achieves 99.33% accuracy and 98.32% precision, outperforming previous methods that reported accuracies of 95-97% and precisions of 94-96%, demonstrating its effectiveness for reliable early detection of DR.
5 May 2026
Read appraisal →Journal of diabetes science and technology
Plantar Thermogram Analysis Using Deep Learning for Diabetic Foot Risk Classification
AIMS: Thermography is a noninvasive method to identify patients at risk of diabetic foot ulcers. In this study, we employed thermography and deep learning to stratify patients with diabetes at risk of developing foot ulcers. METHODS: We prospectively recorded clinical data and plantar thermograms for adult patients with diabetes who underwent diabetic foot screening. A total of 153 thermal images were analyzed using a deep learning algorithm to determine the risk of diabetic foot ulcers. The neural network was trained using a balanced dataset consisting of 98 thermal images (49 normal and 49 abnormal), with 80% allocated for training and 20% for validation. The trained model was then validated on a separate testing dataset consisting of 55 thermal images (42 normal and 13 abnormal). The neural network was trained to prioritize higher sensitivity in identifying at-risk feet for screening purposes. RESULTS: Participants had a mean age of 63.1 ± 12.6 years (52.3% female), and 62.1% had been diagnosed with diabetes for more than 10 years. The average body mass index was 27.5 ± 5.6 kg/m2. Of the thermal images, 91 were classified as category 0 and 62 as categories 1 to 3, according to the diabetic foot risk classification system of the International Working Group on the Diabetic Foot. Using five-fold cross-validation, the neural network model achieved an overall accuracy of 71.8 ± 4.9%, a sensitivity of 81.2 ± 10.0%, and a specificity of 64.0 ± 7.4%. Additionally, the Matthews correlation coefficient was 0.46 ± 0.08%Conclusions:These results suggest that thermography combined with deep learning could be developed for screening purposes to stratify patients at risk of developing diabetic foot ulcers.
4 May 2026
Read appraisal →Social science & medicine (1982)
Imaginaries of digital health: Unpacking the platformization of mental health through users' practices
The platformization of healthcare is reshaping the organization of mental health services, redefining access, relationships, and meanings of care. This paper examines how users experience and negotiate the platformization of mental health through the case of Unobravo, an Italian digital platform offering online psychotherapy. Drawing on Science and Technology Studies and digital health literature, the study explores how platform imaginaries of efficiency, accessibility, and personalization are enacted and contested in everyday practices. Adopting a mixed-methods design, the research combines qualitative interviews (N = 12) with a quantitative survey (N = 3870) to analyse users' narratives and patterns of engagement. Findings reveal a persistent tension between the promise of algorithmic efficiency and the realities of situated practice. While users value the flexibility and accessibility provided by digital infrastructures, they also encounter infrastructural misalignments, such as unstable connections and opaque algorithmic matching processes. These challenges are addressed through practices of domestication that reconfigure the therapeutic setting, integrating care into mobile and domestic environments. By analysing user practices, the paper investigates how mental health is domesticated through digital platforms and shaped by platform imaginaries, reconfiguring the therapeutic setting as a datafied and everyday space.
4 May 2026
Read appraisal →IEEE transactions on bio-medical engineering
Multi-Stage Respiratory Sound Analysis: Confidence-Driven Wheeze and Crackle Detection
OBJECTIVE: Accurate detection of adventitious respiratory sounds, such as wheezes and crackles, is essential for diagnosing and managing respiratory conditions. This study introduces a multi-stage, confidence-driven framework for automated pediatric auscultation analysis, performing a three-way classification of normal, wheeze, and crackle sounds to improve diagnostic accuracy. METHODS: We develop a comprehensive pipeline integrating anomaly-specific segment selection, segment-level classification, and confidence-based fusion. Our contrastive variational recurrent neural network (CVRNN) enhances feature extraction, while a confidence-weighted aggregation strategy refines final predictions. The system is validated using a diverse pediatric dataset from 742 subjects (aged 1-59 months) from seven countries. RESULTS: The multi-level framework is evaluated across three stages. The anomaly-specific segment selection achieves 98.47 $\%$ recall, identifying adventitious regions. Next, segment-level classifiers improve sensitivity, achieving balanced accuracies of 72.15 $\%$ (wheeze) and 68.1 $\%$ (crackle). This performance surpasses state of the art systems on the same dataset and demonstrates enhanced balanced performance in detecting both crackle and wheeze sounds, which present different challenges to automated systems given their markedly different acoustic profiles. Finally, the confidence-driven fusion outperforms traditional aggregation methods, yielding a final three-way classification of 62.12 $\%$. CONCLUSION: Our confidence-based multi-stage approach enhances automated respiratory sound classification by prioritizing high-certainty segment predictions, aligning with expert physician annotations. SIGNIFICANCE: This framework advances computer-aided respiratory diagnostics, improving early detection and monitoring of pediatric respiratory conditions. By integrating expert-inspired segmentation with machine learning-driven confidence estimation, it has potential to enhance clinical workflows and screening for pulmonary diseases, particularly in resource-limited settings.
4 May 2026
Read appraisal →Artificial intelligence in medicine
CL-MHAD: Contrastive Learning-based Multi-Hypergraph Aggregation and Diffusion model for prescription recommendation
Multiple syndrome-based prescription recommendations are significant for personalized diagnosis and treatment in Traditional Chinese Medicine (TCM). However, it remains a challenge to effectively extract and fuse multi-dimensional knowledge in herbs for accurate syndrome-based prescription recommendations. To address it, we propose CL-MHAD, a contrastive learning-based multi-hypergraph aggregation and diffusion model for prescription recommendation. Focusing on prescriptions, herbs, properties, and dosages, this model presents a multi-view hypergraph reconstruction mechanism, which employs a consistent Top-K neighbor strategy to generate affinity and feature matrices. Subsequently, a diffusion-enhanced method is proposed, utilizing multi-step propagation and weighted mapping to capture high-order relationships. Then, we propose a cross-view contrastive learning strategy to enhance discrimination and present a topology-aware random walk augmentation approach to alleviate data sparsity. An integrated loss function combining classification, contrastive, and augmentation losses is designed to optimize model performance. We evaluate CL-MHAD in a real-world clinical setting using Gastrointestinal Diseases (GID) as a case study. Extensive experiments, including comparative evaluations, ablation studies, and parameter sensitivity analyses, show that CL-MHAD achieves overall superior performance compared with representative baselines across multiple evaluation metrics. Specifically, the model achieves performance gains of 1.27% to 24.67% over representative baselines across all datasets. We further evaluate the model through dosage-syndrome interaction and data-sparsity robustness tests, with results demonstrating the effectiveness of our weighted fusion strategy and the model's superior stability. This study offers an effective solution and a promising paradigm for accurate syndrome-based prescription recommendations in TCM.
4 May 2026
Read appraisal →International journal of neural systems
Data-related Ablation for Reinforcing Deep Learning in Explaining Complex Phenomena
Deep Learning (DL) models excel at automatically learning intricate patterns within complex data, but their black box nature undermines human trust. To address this, current validation strategies typically focus on the model itself, modifying its architecture to assess the role and importance of the components. However, this model-centric view overlooks the critical learning substrate, which is represented by the data, implicitly assuming that it accurately represents the target phenomenon. This implicit trust in data means that evaluation may fail to detect whether high performance stems from exploiting biases or data quirks rather than learning relevant patterns. We present a novel data-related ablation as a complement to the traditional architectural ablation. Using this framework for Electroencephalography (EEG) signals of Emotional Recognition (ER) and Motor Execution (ME) as a case study, we show that seemingly high-accuracy models often rely heavily on process-irrelevant features, maintaining performance even when key information is eliminated. This shows that a standard, data-independent evaluation can be misleading about whether a model truly captured the intended process; the proposed approach helps distinguish robust learning from leaning on incidental characteristics. Therefore, incorporating data-related ablation is essential for developing reliable and generalizable DL models in fields that rely on data derived from complex and often not completely known phenomena.
4 May 2026
Read appraisal →European radiology
Predicting molecular types of adult-type diffuse gliomas based on MRI reports with large language models
OBJECTIVES: To evaluate the performance of large language models (LLMs) in predicting molecular types of adult-type diffuse gliomas according to the 2021 WHO classification using MRI radiology reports. MATERIALS AND METHODS: This retrospective study included 2169 patients diagnosed with adult-type diffuse gliomas (294 oligodendrogliomas, 295 IDH-mutant astrocytomas, and 1580 IDH-wildtype glioblastomas) between July 2005 and March 2024 from four hospitals in Asia and Europe. Seven proprietary and open-source LLMs were assessed: GPT-4o-mini, GPT-4.1-mini, Llama 3.1 8B, Llama 3.1 70B, Qwen2.5 7B, Deepseek-r1 8B, and Mistal 7B. The performance of LLMs in classifying molecular types was compared based on the provision of relevant knowledge of glioma imaging findings (knowledge-based vs. naïve prompt). The impact of radiologists' subspecialization in neuro-oncology, report quality, and reporting language on LLMs' performance was also evaluated. RESULTS: LLMs achieved significantly higher (naïve vs. knowledge-based; GPT-4o-mini, 77.0% vs. 79.1%, p < 0.001; Qwen2.5 7B, 75.9% vs. 79.5%, p < 0.001; Deepseek-r1 8B, 66.0% vs. 73.2%, p < 0.001) or comparable accuracy (GPT-4.1-mini, 78.7% vs. 78.6%; Llama 3.1 70B, 78.0% vs. 78.1%; Mistral 7B, 58.4% vs. 57.4%) using knowledge-based prompt compared to naïve prompt, except for Llama 3.1 8B (65.4% vs. 44.6%, p < 0.001). Differences in accuracy were more pronounced in smaller-sized LLMs. Additionally, the accuracy was significantly higher with reports by neuro-oncology specialists and high-quality reports in all LLMs (p < 0.001). CONCLUSIONS: LLMs may provide preoperative information on the tumor types of adult-type diffuse gliomas from MRI reports by providing relevant knowledge in the prompt. Informative and descriptive reports could further enhance LLMs' performance. KEY POINTS: Question Our study aimed to evaluate large language models' (LLMs) ability to efficiently predict molecular types of adult-type diffuse gliomas according to the 2021 WHO classification. Findings Larger models generally showed better accuracy and were less sensitive to domain-specific knowledge. Their performance improved when using high-quality, longer reports or reports by neuro-oncology specialists. Clinical relevance These findings highlight the potential role of LLMs in predicting glioma molecular types, underscoring the importance of informative and descriptive reports in enhancing their performance.
4 May 2026
Read appraisal →Journal of medical Internet research
Digital Health Interventions for Family Members of ICU Patients: Current Evidence and Future Directions
A recent systematic review and meta-analysis of randomized trials evaluating digital health interventions for family members of intensive care unit (ICU) patients found no significant improvements in anxiety, depression, posttraumatic stress, quality of life, or communication quality. Rather than concluding that digital approaches are inherently ineffective, we argue that these null findings reflect identifiable and remediable limitations in intervention design, outcome measurement, and trial methodology. In this commentary, we examine four structural barriers that currently constrain the evidence base and outline the conditions that next-generation trials must meet to adequately address the questions raised by this review.
4 May 2026
Read appraisal →Psychiatry research
A machine learning approach to predicting clinical trajectories in bipolar disorder
BACKGROUND: Bipolar disorder (BD) is characterized by significant heterogeneity in clinical presentation, age of onset, and diagnostic delays. Identifying clinical trajectories and subtypes is a central aim of precision psychiatry, with the goal of improving risk stratification and characterization of illness courses, enabling early recognition of high-risk patients and facilitating timely interventions that improve long-term outcomes. METHODS: This is a dual-site retrospective cross-sectional secondary analysis of 203 clinical datasets from BD patients to develop machine learning models: Random Forest, Weighted Logistic Regression (LR), and XGBoost, supplemented by statistical modelling, to predict age of onset timing (early/late onset) and illness severity. Survival analysis (Kaplan-Meier and Cox proportional hazards) investigated factors influencing diagnostic delay, while clustering identified patient subtypes. RESULTS: Single-split Weighted LR (AUC = 0.733) and nested cross-validation XGBoost (AUC = 0.729) achieved superior performance in predicting onset timing. Cluster analysis identified three distinct phenotypes: baseline low complexity, late-onset familial-severe, and an early-onset comorbid group. Survival analysis revealed that the time to diagnosis was independent of clinical risk profiles (Log-Rank p = 0.34), with the most severe cases experiencing significant diagnostic latency. CONCLUSIONS: Machine learning accurately predicts BD clinical trajectories using routinely collected data in an African cohort. Diagnostic delay did not differ significantly across clinical risk or severity strata, indicating that the measured variables did not explain variation in time to diagnosis.
4 May 2026
Read appraisal →Scientific reports
Hybrid deep learning and YAMNet features for asthma diagnosis from respiratory sounds
A prompt and precise diagnosis is necessary to lessen the worldwide burden of asthma, a common chronic respiratory condition. Spirometry is one of the resource-intensive conventional methods that frequently misclassifies cases because of overlapping symptoms with other respiratory illnesses. The Asthma Detection Dataset v2, which is openly accessible and comprises actual audio recordings of respiratory noises, is used in this work to demonstrate a hybrid deep learning architecture for automated asthma detection. After applying thorough preprocessing, which includes denoising, segmentation, and augmentation, the suggested pipeline employs a dual-path feature extraction technique that combines pretrained YAMNet embeddings with handmade acoustic descriptors (MFCCs, chromagram, ZCR, and spectral features). Atrous Spatial Pyramid Pooling (ASPP) and Squeeze-and-Excitation (SE) modules are used to further improve these embeddings, and a Multi-Layer Perceptron is used for classification. The proposed model achieved high performance, with an accuracy of 98.6% ± 0.14, an F1-score of 97.5% ± 0.14, and a macro-AUC of 99.1% ± 0.14 using stratified 5-fold cross-validation. SHAP-based interpretability and visualization (waveforms, spectrograms, t-SNE) verified that the model records clinically significant auditory biomarkers such as crackles and wheezes. These results indicate the potential of the proposed model as an explainable and scalable computer-aided asthma diagnostic tool.
3 May 2026
Read appraisal →PloS one
Auditing the impact of social media's policy shift on anti-vaccine discourse: A large language model-driven empirical study
The sudden termination of X's (formerly Twitter) misinformation policy on November 23, 2022, provides an opportunity to assess the effects of lifting content moderation restrictions on vaccine-related discourse. This study examines changes in the prevalence, thematic composition, and engagement of anti-vaccine discourse following X's policy shift, analyzing tweets from a seven-day period before and after the policy termination (November 16-30, 2022), excluding the announcement date itself from regression analyses. Using GPT-4o for stance classification, thematic categorization, and stance consistency assessment, with validation through external benchmarks and cross-annotator agreement, we find that anti-vaccine tweets increased significantly post-policy (OR = 1.60, 95% CI: 1.50-1.72), particularly via retweets, suggesting content amplification. Sensitivity analyses excluding highly retweeted content revealed that the policy change was also associated with increased creation of new anti-vaccine content. Thematically, health concerns over vaccination became more prominent, while conspiracy-related and anti-mandate narratives declined in relative prevalence. Stance consistency in quote tweets increased, indicating reinforced ideological alignment in anti-vaccine discourse. These results suggest that content moderation policies may constrain both the volume and amplification of anti-vaccine content, with policy removal associated with rapid shifts in discourse patterns.
3 May 2026
Read appraisal →JMIR aging
The Effectiveness of Digital Interventions to Increase Preventive Care Uptake in Older Adults: Systematic Review
BACKGROUND: Older adults face increasing health risks associated with aging and chronic disease; yet, uptake of recommended clinical preventive services remains low. Digital health interventions have the potential to enhance access and engagement, but their effectiveness in older adult populations remains unclear. OBJECTIVE: This systematic review aimed to examine the range and types of digital clinical preventive service interventions and assess their impact on preventive care uptake among older adults. METHODS: We conducted a systematic review of peer-reviewed research literature published since 2014. Eligible studies included experimental and quasi-experimental designs evaluating digital interventions targeting community-dwelling adults aged 60 years and older. Interventions focused on high-priority preventive services, including cancer screening and adult immunizations. Data were extracted using a standardized form and synthesized narratively due to heterogeneity in study designs and outcomes. RESULTS: In total, 24 studies involving over 1.3 million participants from 11 countries were included. Interventions used a range of digital tools, including telephone calls, SMS text messages, patient portals, and video-based education. While some digital and automated interventions demonstrated modest improvements in preventive services uptake, results were mixed. Interventions incorporating personalized elements (eg, tailored telephone counseling or in-person education) were generally more effective than generic, automated communications. Few studies reported on digital literacy support or intervention reach, and engagement with digital platforms was often low. CONCLUSIONS: Digital interventions can support modest improvements in preventive services uptake among older adults, particularly when personalized or combined with human interaction. However, assumptions of digital fluency and limited reporting on engagement constrain generalizability. Future research should prioritize inclusive design, detailed reporting, and strategies that address digital equity to better support older adult populations.
3 May 2026
Read appraisal →Journal of medical Internet research
Application of Digital Health Technologies in Cardiac Rehabilitation for Patients With Coronary Heart Disease: Scoping Review
BACKGROUND: The high mortality and recurrence rates associated with coronary heart disease (CHD) impose substantial health care costs and economic burdens globally. Identifying effective interventions to improve patient outcomes is paramount. Digital health technologies (DHTs) offer novel solutions to overcome the challenge of low participation rates in traditional cardiac rehabilitation (CR). OBJECTIVE: This review aims to systematically map the scope of application, intervention objectives, and evaluation metrics of DHTs in CR for patients with CHD, thereby providing a structured evidence base for future research and practice. METHODS: This scoping review adheres to the Joanna Briggs Institute's methodology and is reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A systematic search was conducted across 5 major databases, PubMed, Web of Science, Embase, Cochrane Library, and EBSCO, covering the period from inception to February 2026. Inclusion criteria were developed based on the participants, concept, and context framework. Studies focused on the application of various DHTs within CR settings for patients with CHD. Eligible literature comprised randomized controlled trials, quasi-randomized controlled trials, and longitudinal before-and-after studies published in peer-reviewed journals. Two researchers (XZ and ZL) independently conducted literature screening and data extraction. Findings were presented through a comprehensive narrative synthesis and evidence gap maps. RESULTS: A total of 43 studies were included, predominantly randomized controlled trials (n=40). Findings revealed (1) diverse technological formats, categorized into 3 main types: digital health tools, real-time remote support, and asynchronous communication. Multitechnology combined interventions have become the mainstream model (36/43, 83.7%). (2) Intervention objectives were multifaceted, consolidating into 4 dimensions: motivation and guidance, knowledge and skills, monitoring and security, and social and group dynamics. (3) Evaluation metrics were multidimensional, encompassing clinical physiological indicators, health behaviors, patient-reported outcomes, service use rates, and technological feasibility. DHTs demonstrated positive effects in improving short-term physiological function and health behaviors; however, evidence remains insufficient regarding their impact on long-term clinical outcomes such as reducing adverse events. CONCLUSIONS: The innovation of this scoping review lies in integrating highly heterogeneous evidence to reveal the field's evolution from isolated tools toward systematic, integrated solutions. Research confirms that DHTs effectively overcome temporal and spatial constraints, enhancing rehabilitation accessibility and engagement. They serve as crucial strategic tools for bridging geographical disparities in health care resources and advancing equity in cardiovascular health services. However, the evidence base remains limited, including insufficient long-term efficacy data and inadequate exploration of vulnerable populations such as older people and those with low digital literacy. Future research urgently requires large-scale, long-term follow-up clinical trials, alongside enhanced studies on adaptability for specific populations and considerations of health equity. This will propel digital CR toward greater scientific rigor, universal applicability, and precision.
3 May 2026
Read appraisal →ACS applied materials & interfaces
Fingerprint-Inspired Recyclable Electronic Skin for Non-Contact Urinary Incontinence Monitoring and Sensing
The development of high-performance flexible electronics faces a fundamental challenge: balancing sophisticated multimodal sensing and end-of-life environmental sustainability. Drawing inspiration from the human fingerprint, we present a microelectronic-printing-based arch-shaped sensor that resolves this conflict by integrating dual-mode (contact/non-contact) sensing with inherently recyclable components. The device, fabricated through high-resolution printing of liquid metal circuits (with line widths below 200 μm) on functionally tailored substrates, exhibits a sensitivity of 3.07 kPa-1 and a response time under 60 ms. It operates across a broad pressure range (0-15 kPa) in contact mode and senses proximity up to 5 cm. This robust performance enables an integrated wireless system for encrypted communication, gesture-trajectory tracking, and dynamic gesture recognition with 92.67% classification accuracy. Notably, we demonstrate a closed-loop lifecycle for the key functional materials, achieving 86.2% recovery and direct reuse of the liquid metal conductor. This work provides a versatile design strategy that simultaneously meets the demands for performance, functionality, and sustainability, offering a practical platform for future green and intelligent wearable technologies.
3 May 2026
Read appraisal →Journal of medical Internet research
Online Community Support for Stroke Survivors and Caregivers: Scoping Review
BACKGROUND: Previous studies found that online communities are critical in supporting stroke survivors and caregivers for stroke recovery. However, it is unclear how such online communities are designed, or could be designed. OBJECTIVE: This review aims to identify the key design elements of an online community to support stroke survivors and caregivers, that is, the actors, types of community support, and supporting technologies. METHODS: We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We included journal articles related to online community support for stroke. Editorials, registers, opinion pieces, letters, and conference papers were excluded. Online databases PubMed/MEDLINE, Scopus, Web of Science, ScienceDirect, and ProQuest were searched for articles published from January 2015 to June 2025. Articles were screened based on the title, abstract, and full text using Covidence software. After screening and full-text review, we read the selected articles in detail to analyze and synthesize information on key actors, types of support, and technologies used to support stroke survivors and caregivers. RESULTS: A total of 77 articles were included. These articles discussed digital support technologies (52 articles), community functions and roles (18 articles), online stroke community systems (6 articles), and the stroke ecosystem (1 article). Our review found that the online community of support for stroke survivors and caregivers includes the caregivers themselves (46 articles), health workers (24 articles), and the local community/society (14 articles). Online communities mainly provide informational support, including giving advice (28 articles) and tangible aids (29 articles), followed by social support to create a sense of belonging (26 articles). Technologies discussed included mobile health (25 articles), web-based systems (12 articles), virtual/augmented reality (8 articles), sensors/wearable technology (8 articles), video-guided exercise apps (4 articles), and telehealth/telerehabilitation/teleconsultation/telestroke (4 articles). Only one examined how cultural differences influence technology. CONCLUSIONS: Although technologies are essential in online communities of support for stroke survivors and caregivers, this review shows a lack of studies that analyze the use and role of technologies in such online communities. This could be because the key actors of the online communities are the caregivers, who mainly seek social support and therefore do not require sophisticated technology. Nevertheless, technologies such as telerehabilitation and video-guided exercise apps could be important for other actors, including the local community and health workers, to enable them to support stroke survivors and their caregivers.
3 May 2026
Read appraisal →JMIR medical education
Health Professional Students' Use of Generative Artificial Intelligence During Clinical Placements: Cross-Sectional Online Survey Study
BACKGROUND: Generative artificial intelligence (GenAI) is rapidly expanding in higher education and clinical practice. However, its use during clinical placements, where cognitive demands and responsibility for patient care increase, remains insufficiently documented. OBJECTIVE: This study aimed to characterize self-reported GenAI use during clinical placements, perceived benefits and risks, and related training and governance needs. METHODS: We conducted a cross-sectional online survey at a French university (July 17 to September 30, 2025). Eligible participants were students in medicine, pharmacy, nursing, midwifery, or physiotherapy who were currently in, or had completed within the past 18 months, a clinical placement. A 61-item questionnaire (comprising closed- and open-ended items) assessed GenAI use, task patterns, perceived benefits or risks, and training or governance needs. A composite index classified self-perceived GenAI maturity as minimal, limited, moderate, or high. Group comparisons used χ2 tests; maturity gradients used trend tests. RESULTS: A total of 388 students responded (n=308, 79.4% women), mainly nursing students (n=217, 55.9%). Overall, 204 (52.6%) students reported using GenAI during clinical placements. Use differed across disciplines (χ24=10.71; P=.03), with lower uptake in midwifery (6/23, 26%; odds ratio 0.30, 95% CI 0.11-0.77). Adoption increased markedly with self-perceived maturity (minimal: 2/22, 9% vs high: 22/29, 76%; trend P<.001). Among the 204 users, the most commonly reported uses were information retrieval (n=159, 77.9%), bibliographic search (n=152, 74.5%), and translation or rephrasing (n=145, 71.1%); patient-facing activities were less frequently reported (eg, patient-document drafting or communication preparation: n=78, 38.2%). Although most users reported never entering direct patient identifiers, 48 (23.5%) reported at least 1 disclosure of patient-identifying information, and 96 (47.1%) reported processing real medical content perceived as anonymized. The most endorsed perceived benefits among the 388 students were documentation support (n=315, 81.2%) and improved access to information (n=266, 68.5%). The most endorsed risks were dependency (n=353, 90.9%), skill erosion (n=329, 84.8%), and confidentiality breaches (n=339, 87.4%). Training needs were highest for ethics or regulatory training (294/378, 77.7%) and a best-practice clinical guide (292/373, 78.3%). CONCLUSIONS: GenAI is already used by a substantial proportion of French students in health professions during clinical placements, predominantly for information and documentation support rather than patient-facing activities. Self-perceived readiness is strongly associated with adoption. Reported disclosures and concurrent concerns about dependency, skill erosion, and confidentiality support the need for structured curricula and clear governance frameworks to enable responsible, patient-centered integration of GenAI into clinical education.
3 May 2026
Read appraisal →JMIR medical informatics
Leveraging MedlinePlus to Improve Health Information Access Among Patients and Caregivers: Systematic Literature Review
BACKGROUND: MedlinePlus, developed by the National Library of Medicine (NLM) in the United States, is one of the most widely used, authoritative, consumer-grade health information resources on the web. Although extensively used and discussed in scholarly work for health literacy and patient education, it is unclear how MedlinePlus has been integrated into clinical care or embedded within health informatics applications. OBJECTIVE: This study aimed to understand how MedlinePlus has supported patients and caregivers by increasing access to health information for clinical care and illness management. The insights on this topic will inform the design and development of patient-facing digital health intervention tools for improved health communication, decision engagement, informed decision-making, and health outcomes. METHODS: We conducted a systematic literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. First, we developed a comprehensive literature search strategy, searched 9 citation databases, and aggregated and deduplicated search results before importing them into Covidence for manual screening using predefined inclusion and exclusion criteria. Second, reviewers independently assessed all studies at the title-abstract and full-text levels, resolving discrepancies through ongoing discussions. Third, we applied the PICO (problem/population, intervention, comparison, and outcome) and the Collaborative Chronic Care Model as guiding frameworks for data extraction and analysis. All included studies underwent quality assessment using the Mixed Methods Appraisal Tool. RESULTS: In total, 28 studies reported in 27 sources met our inclusion criteria. We categorized the extracted data into 4 areas. First, regarding bibliometrics, the studies were reported between 2004 and 2024, with 2010 having the highest number of studies. Of these studies, 25 were conducted in the United States, 2 were conducted in Iran, and 1 was conducted in Argentina. Health informatics journals and conference proceedings, as well as library science journals, were prominent publishing venues. The NLM funded half of the studies. Second, regarding participants, most studies focused on outpatients. Other participant roles included physicians, nurses, hospital staff, pharmacists, and librarians. Fewer than half of the studies addressed the social determinants of health. Third, regarding intervention, most studies implemented MedlinePlus information interventions within clinical settings. Other interventions occurred in community pharmacies, community organizations, libraries, online health platforms, or patient portals. Fourth, regarding outcome, only 4 studies assessed clinical outcomes, and the findings were mixed and inconsistent. However, 24 of 28 studies reported positive nonclinical outcomes, including improved attitudes toward and satisfaction with MedlinePlus and enhancements in patients' information-seeking behaviors, confidence, and willingness to engage in decision-making, physician-patient communication, self-management, and self-efficacy. CONCLUSIONS: This systematic literature review is the first comprehensive examination of how MedlinePlus has been integrated into clinical care, supporting patients and caregivers with enhanced access to health information. Our findings offer evidence and insights through the Collaborative Chronic Care Model lens and can guide the development of digital health interventions to improve patient health.
3 May 2026
Read appraisal →PloS one
Enhanced graph attention network by integrating Long Short-Term Memory for artificial emotion representation in multi-modality datasets
Emotion representation is a critical aspect of artificial intelligence, particularly in human-computer interaction and affective computing. Emotion recognition from multi-modal data remains challenging due to the complex semantic relationships between textual, audio, and visual features. This study proposes a hybrid model combining Enhanced Graph Attention Networks and Bidirectional Long Short-Term Memory to address this challenge. First, E-GAT captures structural dependencies between emotional features by constructing a semantic graph from text embeddings. Second, Bi-LSTM models temporal dynamics of sequential data, enabling effective integration of contextual information. We evaluated the model on three benchmark datasets: SemEval-2018 (text-only), RAVDESS (audio-visual), and CMU-MOSEI (multi-modal). Experimental results show that the proposed model achieves state-of-the-art performance: 58.5% accuracy and 68.7% F1-score on SemEval-2018, outperforming baseline models. On multi-modal datasets, it achieves 78.9% accuracy (RAVDESS) and 82.3% accuracy (CMU-MOSEI), demonstrating robust cross-modal generalization. This work advances emotion recognition by providing a unified framework for both text-only and multi-modal scenarios, with applications in human-computer interaction and mental health monitoring.
3 May 2026
Read appraisal →Journal of medical Internet research
Rapid Deployment of Telemedicine in HIV Care: Mixed Methods Study of Providers' Attitudes and Perceptions
BACKGROUND: The COVID-19 pandemic prompted an abrupt transition to telemedicine for HIV care, necessitating the exploration of provider attitudes and experiences to understand its ongoing viability and impact. OBJECTIVE: This study aimed to assess providers' attitudes toward and experiences with telemedicine at HIV clinics in the Chicago area during the COVID-19 pandemic at 2 time points and at 2 clinical sites. METHODS: We conducted a convergent mixed methods study to evaluate and explore providers' attitudes toward and experiences with telemedicine at HIV clinics in the Chicago area during the COVID-19 pandemic, applying the RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework and the updated Consolidated Framework for Implementation Research. This study assessed HIV providers via surveys at 2 time points, capturing responses on the acceptability, appropriateness, feasibility, and maintenance of telemedicine. Semistructured key informant interviews were conducted on a random selection of 10 participants to explore perceived experiences with telemedicine. RESULTS: Among emailed providers, 43 of 83 (51.8%) and 27 of 82 (32.9%) responded to survey 1 and survey 2, respectively. The first survey recorded telemedicine usage at 75%, which decreased to 58% by the second survey. Overall, the majority of respondents agreed with statements that assessed telemedicine as appropriate, acceptable, and feasible. There were overall few statistical differences in responses between sites, although more providers at the community site indicated at least some potential value in telemedicine when compared to providers at the university hospital (100% agree or strongly agree vs 82.3%; P=.04) in survey 1. In survey 1, providers with more than 10 years of experience were less likely to report telemedicine helped them see more patients (31.6% agree or strongly agree vs 70.6% for providers with ≤10 y of experience; P=.008). Fewer experienced providers felt they could talk about private issues with their patients during telemedicine (57.9% of more experienced vs 88.3% of less experienced providers; P=.03). Key informant interviews provided qualitative insights into telemedicine integration, revealing mixed sentiments; providers appreciated the flexibility and accessibility that telemedicine offered but preferred in-person visits for their thoroughness, especially for physical examinations and laboratory tests. Despite initial barriers such as technical challenges and patient preferences for in-person visits, telemedicine was deemed feasible for maintaining communication and care quality. Providers highlighted the need for better technological support and ongoing training to optimize telemedicine usage. CONCLUSIONS: Our study underscores telemedicine as a sustainable adjunct to traditional HIV care, emphasizing the importance of addressing technological and training barriers to enhance its efficacy.
3 May 2026
Read appraisal →JMIR formative research
Comparing Perceptions of ChatGPT Use in Health Attitude Contexts Among Users and Nonusers: Cross-Sectional Study
BACKGROUND: In light of the growing use of artificial intelligence (AI) in health care, individuals' access to and use of health information are transforming. ChatGPT, an AI chatbot, provides immediate responses to health queries, with the potential to influence health-related attitudes, thereby raising concerns related to privacy, reliability, and security. OBJECTIVE: This study aimed to investigate the perceived usefulness, risks, anxiety, and social influence of ChatGPT on health attitudes among users and nonusers in Saudi Arabia. METHODS: A cross-sectional study was conducted using an online survey based on a validated tool. In total, 337 participants aged 18 years and older responded to questions assessing their perceptions of ChatGPT on health-related attitudes. RESULTS: Data showed that 76.1% (194/255) of the respondents used ChatGPT, with the majority being younger and more highly educated. The main uses for health-related purposes were health education (43/194, 22.2%) and physical activity guidance (31/194, 16%). The analysis showed that users considered ChatGPT useful for health-related decisions, with 45.9% (89/194) finding it easy to learn and use, but concerns about privacy (106/194, 54.7%) and reliability (87/194, 44.9%) remained. Among nonusers, security risks (39/61, 63.9%) were the major barrier to using AI-based tools for health purposes, and 68.9% (42/61) found such tools attractive and engaging. There were no statistically significant differences between users and nonusers across all examined sociodemographic characteristics (P>.05). CONCLUSIONS: The study established the potential of ChatGPT in improving health decision-making and revealed cultural, privacy, and trust issues that may affect its implementation. These findings underscore the importance of strengthening the security of AI-based applications to enhance public acceptability of related health policies and to support the safe integration of tools such as ChatGPT into the health care system.
3 May 2026
Read appraisal →JMIR mHealth and uHealth
Alcohol Use Trajectories During the First 72 Weeks of WHOOP Wearable Platform Membership: Observational Cohort Study
Among 30,000 new wearable device users (11.6 million person-days), self-reported alcohol consumption significantly declined over 72 weeks, with the daily probability of drinking decreasing by 5.8 percentage points (P<.001) and reductions across age and biological sex.
2 May 2026
Read appraisal →JMIR mental health
The Effectiveness and Mechanisms of Action of App-Based Interventions for Improving Mental Health and Workplace Well-Being: Randomized Controlled Trial
BACKGROUND: Depression is the most common mental health disorder worldwide and frequently leads to workplace absence. As face-to-face treatment can be difficult to access, app-based interventions are a popular solution, although their effectiveness in working populations and their mechanisms of action are unclear. Deficits in executive function may contribute to the onset and maintenance of depression, and executive function training is proposed to improve symptoms by enhancing executive function. Responders to cognitive behavioral therapy (CBT) show improvements in executive function, suggesting that this may be one mechanism of action. OBJECTIVE: This study investigated the effectiveness of app-based interventions (executive function or CBT-based) for reducing depressive and anxiety symptoms and improving workplace well-being, and assessed whether changes in executive function mediated improvements. METHODS: A total of 228 participants (147 female participants) with mild-to-moderate symptoms of depression and anxiety were recruited online and randomly assigned to a waitlist control group, an executive function training group (NeuroNation app, Synaptikon GmbH), or a self-guided CBT group (Moodfit app, Roble Ridge LLC) for a 4-week intervention period. Participants assigned to the active intervention groups were asked to use their apps a minimum of 21 times during the intervention. Participants completed measures of depressive symptoms, anxiety symptoms, and workplace well-being, and a working memory task at baseline, postintervention, and follow-up (12 weeks). RESULTS: Executive function training reduced anxiety (β=-2.79; P=.004) and depressive (β=-2.77; P=.02) symptoms at follow-up but not at postintervention, and it did not affect workplace well-being. There were no reductions in depressive or anxiety symptoms in the self-guided CBT group, though workplace well-being was improved at postintervention (β=3.72; P=.02) and follow-up (β=4.46; P=.02). Improvements in executive function did not mediate intervention-related changes in symptoms or workplace well-being. Self-reported adherence rates were high (executive function training: 48/54, 89%; self-guided CBT: 52/54, 96%), although attrition was high at follow-up (58% missing). CONCLUSIONS: These results suggest that app-based executive function training may be effective at managing symptoms of anxiety and depression in a working population, while self-guided CBT apps may improve workplace well-being. However, improving executive function did not appear to be a mechanism of action of either intervention.
2 May 2026
Read appraisal →PloS one
Deep feature optimization using fusion of multiple self-supervised learning approaches and filter-based feature selection for lung cancer histopathology classification
One of the most deadly illnesses in the world is lung cancer, and increasing survival rates require early detection. Lung cancer diagnostics from the imaging modalities is always subjective, and this paves the way for deep learning assisted computer aided techniques. Still, the accuracy of such a technique is the major concern. This research work attempts to enhance lung cancer diagnostics from histopathological images using a previously unaddressed combination of multiple self-supervised learning techniques, filter-based feature selection, and Vision Graph Convolutional Networks. The main contribution lies in optimized feature fusion, and it brings complementary strengths of three different self-supervised learning approaches- contrastive alignment, redundancy reduction, and semantic grouping. The first step involves extracting the key features from histopathological images using a custom Convolutional Neural Network. Three complementary self-supervised learning methods - Deep Cluster, Bootstrap Your Own Latent, and Simple Framework for Contrastive Learning of Visual Representations - are then used to refine each of these features separately. Following optimization, the improved features are combined, and the most significant features from the combined set are found and preserved using a filter-based feature selection technique called Minimum Redundancy Maximum Relevance. Vision Graph Convolutional Neural Network is used as a classifier. Initial experiments are carried out on the 691 histopathological images extracted from the publicly available LungHist700 dataset. This dataset contains histopathological images under three categories: 151 normal subjects, 280 lung adenocarcinoma subjects, and 260 lung squamous cell carcinoma subjects. The proposed approach provided a balanced accuracy of 97% while the plain Vision Graph Convolution Neural Network offers only an 86% balanced accuracy score. Further validation is performed using two more publicly available datasets, namely LC25000 and TCGA UT datasets. The experimental results demonstrate the enhanced lung cancer prediction performance of the proposed approach in all three datasets.
2 May 2026
Read appraisal →JMIR public health and surveillance
Integrating AI Into Governmental Public Health Decision Making: Challenges, Considerations, and a Path Forward
Public health emergencies such as pandemics, natural disasters, and epidemics may require rapid, high-stakes decisions often made by elected officials with limited public health training. Artificial intelligence (AI) holds significant promise to enhance the quality, transparency, and timeliness of governmental decision-making during such crises. This paper examines the potential of AI as a decision-support tool for elected officials while identifying key technical, logistical, ethical, and policy challenges. Technical considerations include model accuracy, data representativeness, and privacy protection, while ethical imperatives center on fairness, transparency, and accountability to prevent amplification of existing health disparities. The paper further explores workforce development needs, emphasizing AI literacy and cross-sector collaboration to enable informed use of AI insights. This viewpoint presents a novel AI Decision Support Lifecycle framework specifically designed for governmental public health emergency response, mapping six phases from problem definition through post-emergency evaluation. We provide stakeholder-specific recommendations for model developers, health agencies, and elected officials, and illustrate practical application through a detailed case example and use cases. Drawing on empirical evidence regarding digital health technologies and AI governance, we emphasize that technology deployment alone is insufficient. Successful implementation requires complementary investments in organizational capacity, data infrastructure, workforce training, community engagement, and continuous evaluation. AI integration also requires robust governance frameworks, continuous model evaluation, and alignment with existing crisis management structures. Policy recommendations highlight the importance of ethical AI frameworks, risk assessments, and public engagement to foster trust. Ultimately, AI can strengthen public health decision-making if developed and implemented responsibly within transparent and equitable systems.
2 May 2026
Read appraisal →Turkish journal of ophthalmology
Large Language Models in Ophthalmology: A Bibliographic Analysis
This study evaluated the distribution of research on the use of large language models (LLMs) in ophthalmology through a bibliographic analysis of articles retrieved from PubMed through November 2024. Studies were categorized into four main areas of LLM application: clinical decision-making (further divided according to subspecialties), education, patient interactions, and miscellaneous applications. Descriptive statistics were used to analyze the distribution of studies by ophthalmic subspecialty, geographical region, journal quality, and author characteristics, including gender and scholarly impact (h-index and i10-index). The findings revealed that clinical decision-making was the most common application (43.7%), with the majority of studies in this subgroup focusing on the retina (39.5%). Geographically, most of the research originated from North America (48.3%), followed by Asia (29.9%) and Europe (20.7%). Most studies were published in high-impact journals (Q1 journals: 74.7%), particularly for those related to clinical decision-making in retina (80.0%), glaucoma (100%), and multiple subspecialties (87.5%). Gender disparities were evident across all author roles, with female authors accounting for only 29.9% of first authors, 25.3% of last authors, and 26.4% of corresponding authors. The results suggest a need for greater diversity in terms of gender and geographic representation in LLM research in ophthalmology to promote inclusive progress in the field. Bu çalışma, PubMed’de Kasım 2024’e kadar yayımlanmış göz hastalıkları alanında büyük dil modellerinin (BDM) kullanımı ile ilgili araştırmaların bibliyografik analizini amaçlamaktadır. Çalışmalar, klinik karar desteği (yandallara göre ayrılmış), eğitim, hasta etkileşimleri ve diğer uygulamalar olmak üzere BDM yöntemlerinin dört ana uygulama alanına göre kategorize edilmiştir. Tanımlayıcı istatistikler kullanılarak çalışmaların, oftalmoloji alt uzmanlıkları, coğrafi bölge, dergi kalitesi ve yazar özellikleri (cinsiyet ve bilimsel etki, h-endeksi ve i10-endeksi dahil) açısından dağılımı analiz edilmiştir. Bulgularımız, klinik karar desteğinin en yaygın uygulama olduğunu (%43,7) ve bu alt grup içerisinde çalışmaların çoğunun retina üzerine odaklandığını (%39,5) ortaya koymuştur. Coğrafi olarak, araştırmaların çoğu Kuzey Amerika’dan (%48,3) yayımlanmakta olup sırasıyla Asya (%29,9) ve Avrupa (%20,7) Kuzey Amerika’yı izlemektedir. Çoğu çalışma, özellikle de klinik karar desteği alt grubunda, retina (%80,0), glokom (%100) ve birden fazla alt uzmanlık dalını kapsayan çalışmalar (%87,5), yüksek etki faktörüne sahip dergilerde yayımlanmıştır (Q1 dergileri: %74,7). Cinsiyet dağılımındaki eşitsizlik, tüm yazar rollerinde belirgin olarak gözlemlenmiştir. Çalışmalarda kadın yazarlar, ilk yazarların yalnızca %29,9’unu, son yazarların %25,3’ünü ve sorumlu yazarların %26,4’ünü oluşturmuştur. Sonuçlar, göz hastalıkları alanındaki BDM araştırmalarında cinsiyet ve coğrafi temsilde daha fazla çeşitliliğe ihtiyaç duyulduğunu ve bu çeşitliliğin alandaki kapsayıcı ilerlemeyi teşvik edeceğini göstermektedir.
2 May 2026
Read appraisal →Psychological medicine
Treatment effect modifiers of virtual reality-based versus standard cognitive behavioral therapy for paranoia in schizophrenia spectrum disorders: an exploratory moderator analysis of clinical and demographic characteristics in the FaceYourFears trial
BACKGROUND: This exploratory study examined baseline characteristics modifying treatment effects on paranoia in individuals diagnosed with schizophrenia spectrum disorders following a 10-session virtual reality-based cognitive behavioral therapy for psychosis (VR-CBTp) or standard CBTp. METHODS: All participants in the FaceYourFears trial were included (n=254; CBTp n=128; VR-CBTp n=126). General linear and logistic regression models examined baseline variables associated with end-of-treatment paranoia. In covariate analyses, regression coefficients quantified associations across treatments. In moderation analyses, interaction terms (randomization x moderator) were tested, with corresponding regression coefficients estimated and assessed at the 25th (low), 50th (medium), and 75th (high) percentiles of continuous variables. RESULTS: Across treatments, higher baseline avolition, safety behavior, delusion severity, and cognitive biases were associated with end-of-treatment paranoia. Moderation analyses revealed interactions for avolition, delusion severity, and negative other-beliefs. Although avolition and delusion severity were associated with poorer outcomes overall, individuals with high avolition and those with moderate-to-high delusion severity improved more in VR-CBTp than CBTp, whereas participants with lower delusion severity showed better outcomes in CBTp. No demographic (age, gender, education, and occupation) or other clinical characteristics (diagnosis, paranoia, social anxiety, depression, anhedonia, total negative symptoms, functioning, core beliefs, or interpersonal trauma) were significantly associated with outcome. CONCLUSIONS: This exploratory study generates hypotheses for future research, including VR-CBTp's potential to engage individuals with high avolition. Given the modest effects and largely nonsignificant findings, both CBTp and VR-CBTp appear suitable for a wide range of individuals with paranoia, highlighting the importance of considering patient preferences.
2 May 2026
Read appraisal →Scientific reports
Intelligent virtual agents in psychotherapy: a safety evaluation across high-risk mental health scenarios
The growing burden of mental illness and limited access to evidence-based psychotherapy have increased interest in artificial intelligence (AI)-driven conversational agents as potential supports for mental health care. In this exploratory pilot study, we examined the safety and feasibility of an intelligent virtual agent (IVA) designed to simulate psychotherapeutic interactions, with a focus on high-risk situations involving suicidality and substance use. Two licensed psychotherapists engaged in scripted interactions with the IVA across 12 predefined scenarios addressing suicidality and substance abuse. The IVA was powered by GPT-4omni and embedded in a Unity-based avatar. After each interaction, testers evaluated acceptance, usability, and human-robot interaction. Two independent psychotherapists rated the IVA's responses using a structured scale assessing guideline adherence, risk recognition, help provision, de-escalation, and empathy. No real patients were involved; all interactions were simulated for safety testing purposes. The IVA showed preliminary indications of good usability and generally empathic responses. However, problematic responses occurred in 29% of conversations, with 12.5% rated as highly critical. Responses rated as "critical" or "highly critical" referred to outputs that failed to provide adequate support, showed insufficient risk recognition, or included ethically problematic suggestions. Key concerns included inadequate recognition of risk, normalization of substance use, and insufficient referral to crisis resources, particularly in scenarios involving underage alcohol access and suicide-related inquiries. In this small, expert-based pilot safety evaluation, the findings suggest that although AI-based agents may improve access to mental health support, rigorous safety evaluation, clinical oversight, and robust safeguards are essential prior to clinical deployment. No clinical conclusions can be drawn from this simulated study.
1 May 2026
Read appraisal →Scandinavian journal of primary health care
Overdiagnosis in atrial fibrillation screening with wearables
PURPOSE: Wearable technologies are increasingly used to screen for atrial fibrillation (AF), often outside formal clinical pathways. While this holds promise for early detection, the impact on patient outcomes remains unclear. We aimed to examine the hypothesis whether intermittent and continuous AF screening using wearable-like technologies leads to overdiagnosis in asymptomatic, high-risk individuals. METHODS: This hypothesis-generating analysis draws on available RCTs screening asymptomatic, high-risk individuals for AF using wearable-like ECG technologies. It is not a systematic review, as the field remains too early-stage for formal evidence synthesis. Eligible studies included either intermittent (≥10 recordings) or continuous monitoring for ≥1 week, reflecting typical smartwatch use. Primary outcomes included AF diagnoses, anticoagulant use, and adverse events. Overdiagnosis was inferred when excess AF detection did not translate into improved clinical outcomes. Analyses used Mantel-Haenszel random-effects models. RESULTS: Continuous screening nearly tripled AF diagnoses (OR 2.84; 95% CI, 1.61-5.02) and doubled anticoagulant use compared to controls. Intermittent screening showed a non-significant 1.8-fold increase. Neither method significantly reduced adverse outcomes. A pooled analysis revealed a marginal reduction in negative events (OR 0.94, 95% CI 0.90-0.99). Overdiagnosis estimates varied by method, ranging from 8-29% based on persistent differences in cumulative incidence after the screening period has ended and 47-65% based on increased diagnoses and treatment during the screening phase that are not followed by improved patient-relevant outcomes. CONCLUSION: Wearable-enabled screening for AF in high-risk patients increases diagnosis and treatment without clear benefit in clinical outcomes. These findings support the hypothesis of overdiagnosis and findings highlight the need for cautious integration of consumer technologies into primary care. Further trials with extended follow-up in low-risk populations are needed to clarify when AF detection improves outcomes versus when it leads to unnecessary medicalisation and harm.
30 Apr 2026
Read appraisal →JMIR formative research
Retrieval-Augmented Generation Versus GPT-4o for Patient-Facing Gynecological Cancer Information: Quality Evaluation
Retrieval-augmented generation improved overall quality scores for patient-facing gynecological cancer information mainly through better source attribution.
28 Apr 2026
Read appraisal →PloS one
Unpacking the dual psychological paths of employee-AI collaboration on creativity: The role of proactive behavior
The deep integration of artificial intelligence (AI) into organizational settings has significantly transformed employees' work patterns, underscoring the need to investigate the mechanisms through which employee-AI collaboration influences creativity. Grounded in social cognitive theory, this study examines how employee-AI collaboration affects creativity via internal (self-efficacy) and external (performance pressure) mechanisms, and explores the moderating role of proactive behavior. Using convenience sampling, 733 valid responses were collected from six technology-driven enterprises. The theoretical model was tested through confirmatory factor analysis and regression analyses using AMOS and the Process macro for SPSS. Our results reveal that employee-AI collaboration is positively associated with creativity through two statistically supported indirect pathways: parallel indirect effects via self-efficacy (internal pathway) and performance pressure (external pathway), and a sequential indirect association in which higher self-efficacy is related to higher performance pressure, which in turn is positively related to creativity. Moreover, proactive behavior appears to moderate these relationships, such that the association between employee-AI collaboration and self-efficacy is more pronounced at higher levels of proactive behavior, whereas the association between employee-AI collaboration and performance pressure is less pronounced at higher levels of proactive behavior, yielding a pattern consistent with moderated indirect effects on creativity through both routes. These findings provide theoretical insights into the complex psychological processes underlying creativity in AI-enabled work environments and offer practical implications for optimizing AI applications and developing targeted interventions to foster employee creativity.
28 Apr 2026
Read appraisal →Biomedizinische Technik. Biomedical engineering
Blood pressure estimation using single photoplethysmography signal based on ResNet-BiGRU
OBJECTIVES: Conventional cuff-based blood pressure (BP) measurement provides only intermittent readings, whereas photoplethysmography (PPG)-based methods enable continuous and noninvasive monitoring. This study aims to develop a deep learning framework for accurate, cuffless BP estimation using a single PPG signal. METHODS: A hybrid deep neural network, termed ResNet-BiGRU, was developed by integrating residual convolutional blocks with bidirectional gated recurrent units to jointly capture morphological and temporal features. The UCI Cuff-Less Blood Pressure Estimation Dataset (a subset of MIMIC-II), which contains synchronized PPG and arterial blood pressure (ABP) signals from 942 subjects, was used for model training and validation. After applying a 0.5-8 Hz bandpass filter and segmenting into 5 s windows, the data were split 80/20 for training and validation. External evaluation was conducted using the VitalDB dataset, which provides synchronized PPG and ABP recordings from surgical patients under diverse physiological conditions. RESULTS: The model achieved mean absolute errors (MAE) of 4.78 mmHg for systolic BP (SBP) and 2.98 mmHg for diastolic BP (DBP) on UCI, and 8.15 mmHg for SBP and 4.59 mmHg for DBP on VitalDB. CONCLUSIONS: The ResNet-BiGRU model demonstrates accurate, robust, and generalizable cuffless BP estimation, showing strong potential for wearable health monitoring applications.
28 Apr 2026
Read appraisal →Biomedical physics & engineering express
BiFUSE-UNet: a novel dual-encoder enhanced TransUNet for polyp segmentation
Polyps are abnormal protrusions that develop on the mucosal surface of the human body, arising from hyperproliferation or aberrant differentiation of mucosal tissue. While some polyps carry a risk of malignant transformation, timely intervention can halt this progression. Accurately segmenting polyp contours in medical images poses significant challenges, primarily due to the high heterogeneity in polyp appearance and morphology, blurry boundaries with background interference, and class imbalance coupled with data scarcity. To address these challenges, this paper presents BiFuse-UNet, a novel dual-encoder TransUNet architecture built upon a bidirectional fusion mechanism. Building upon the original TransUNet architecture, BiFuse-UNet incorporates several key modules. Firstly, a multi-axis attention module (MaxViT block) is integrated to form a dual-encoder structure, which extracts multi-scale local textural features across different dimensions and captures long-range dependencies through self-attention. Secondly, a Merge fusion module is designed, incorporating a Coordinate Attention module and the SaE block from SENetV2, to enhance the model's perception of local and detailed features. Finally, a Biformer module is introduced to strike an efficient balance between global semantics and local details, thereby improving the segmentation accuracy for subtle polyps and early-stage polyps with atypical morphology. Extensive evaluations on the Kvasir-SEG and CVC-ClinicDB dataset demonstrate that BiFuse-UNet achieves state-of-the-art segmentation performance, outperforming seven existing methods on key metrics such as the Dice coefficient, Recall, Precision, and IoU. The proposed model effectively mitigates the challenges of morphological heterogeneity, blurry boundaries, and background interference, leading to highly accurate pixel-level segmentation.
27 Apr 2026
Read appraisal →BMJ health & care informatics
Omission and hallucination prevalence of clinical guidelines in diagnostic large language model outputs
OBJECTIVE: Meaningful assessments of how large language models (LLMs) incorporate clinical guidelines require large-scale testing over many queries. Here, we evaluate the prevalence of clinical guideline omissions and hallucinations in a large sample of diagnostic LLM outputs. METHODS: We used simulated case vignettes and zero-shot prompting to generate diagnostic outputs and rationales from GPT-4.1 and DeepSeek-V3. English case vignettes were created for hypercholesterolaemia and type-2 diabetes mellitus. Each vignette contained identical medical information, while sociodemographic characteristics varied in terms of sex, ethnicity and location. We calculated the prevalence of existing and hallucinated clinical guidelines in LLM outputs across disease, LLM and sociodemographic characteristics. RESULTS: We analysed a total of 12 197 LLM outputs, which quantifies three hazard areas: omissions (up to 97% for DeepSeek-V3 and 46% for GPT-4.1), hallucinations (up to 9%) and inconsistencies (guideline citation rate ranging from 0% to 78.39% across sociodemographic vignettes). Omission and hallucination rates were generally similar across vignettes with different sex or ethnicity data, yet were particularly sensitive to patient location. DISCUSSION: This study highlights significant variability in clinical guideline prediction across two different diseases, three different sociodemographic variables and two LLMs, even when the LLMs were instructed by identical prompts, establishing clinical guideline prediction in LLM outputs as a stochastic event. CONCLUSION: The stochastic nature of LLMs creates a unique challenge for evidence generation and clinical deployment. Being able to measure and capture this stochasticity within high-quality research designs will be a prerequisite to advancing the responsible deployment of LLMs in healthcare.
27 Apr 2026
Read appraisal →BMJ health & care informatics
i-MoMCARE: AI-enabled mobile app for maternal and child health care in Cambodia - a pilot implementation and evaluation study
OBJECTIVES: In Cambodia, village health support groups (VHSGs) are central to maternal and child health (MCH) service delivery, yet maternal mortality remains high with regional disparities. Although digital health solutions show promise, their real-world use is underexplored. This study therefore aims to evaluate the i-MoMCARE app in supporting VHSGs and health centre staff for delivering MCH services. METHODS: A mixed-methods approach guided by the Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) framework was used. Reach was assessed using programme records (number of users trained and MCH cases managed). Effectiveness and adoption were evaluated using the mHealth App Usability Questionnaire (MAUQ) and qualitative data on user experience and intention to use. Implementation was examined through system usage monitoring and feedback on workflow integration. Maintenance was explored using qualitative insights on long-term use, barriers and recommendations. RESULTS: 44 VHSGs and 10 health centre staff were trained, and 96 MCH cases were managed with the app. Usefulness was rated highly (MAUQ 5.58, SD 1.04), with users appreciating the user-centric design that improved usability and integration into daily workflows. Effectiveness was similarly high (MAUQ 5.50, SD 0.95); the clinical decision support system and point-of-care alerts were valued as reliable decision-support tools, and the electronic medical record (EMR) was seen as a secure data repository. However, adoption was hindered by hardware and software limitations (MAUQ 5.79, SD 0.75), including device availability and low digital literacy. DISCUSSION: Our findings highlight the value of the i-MoMCARE app in supporting VHSGs, particularly those with limited training, by enhancing their decision-making. The study also lays the groundwork for a unified EMR in primary care. However, concerns around hardware and software limitations hinder adoption and sustained use. CONCLUSION: To enable scale-up, we recommend reducing access barriers and supporting sustained use through diversifying the platform's design and implementing phased incentivisation strategies.
27 Apr 2026
Read appraisal →BMJ health & care informatics
AI-generated clinical summaries: errors and susceptibility to speech and speaker variability
OBJECTIVES: To evaluate whether variability in patients' communication style (personality), international English-accents (human and synthetic) and speech impairments affects the accuracy of a Clinical AI Scribe (CAIS) and identify where performance degrades to inform pre-deployment validation and monitoring. METHODS: We conducted simulated primary-care consultations using trained actors. For personality types, four scenarios were enacted, each with five patient-personality types. For accents, transcripts of consultations were used to generate combinations of seven accents across five scenarios. The CAIS produced summaries that were compared with transcripts, and errors classified as omissions, factual inaccuracies or hallucinations. For speech impairments, public recordings representing five profiles were transcribed and word-recognition accuracy calculated. RESULTS: Personality types showed no statistically significant differences in errors (all p>0.05). Extraversion had the highest total errors (median 3.5). Across accents, comparisons were non-significant for both patient and doctor voices (patients: p=0.851; doctors: p=0.980). Omissions predominated, with low rates of hallucinations and factual inaccuracies. Omissions were slightly higher for Chinese-accented and Indian-accented doctors (both medians 3.0). Conversely, speech impairments differed: cleft palate and vowel disorders were near-perfect, whereas phonological impairment markedly reduced recognition (p<0.001). DISCUSSION: Operationally, CAIS deployment should include clinician-in-the-loop verification, subgroup performance monitoring (accents, impairments) and predefined 'switch-off' criteria for severe phonological patterns. High-quality synthetic voices are a pragmatic proxy for accent testing when balanced corpora are unavailable. CONCLUSIONS: Under controlled conditions CAIS performance was broadly stable across communication styles and most accents, but vulnerable to specific speech characteristics, particularly phonological impairment, in this single-system simulation study.
27 Apr 2026
Read appraisal →JMIR medical informatics
Deep Learning Model Using Transfer Learning for Detecting Left Ventricular Systolic Dysfunction: Retrospective Algorithm Development and Validation Study
BACKGROUND: Artificial intelligence-augmented electrocardiogram (AI-ECG) models for detecting left ventricular systolic dysfunction (LVSD) often exhibit degraded performance in patients with comorbidities. OBJECTIVE: This study aimed to introduce and validate a recalibration method using longitudinal patient data to enhance prediction accuracy and simulate its clinical utility for ongoing monitoring. METHODS: We conducted a multicenter, retrospective cohort study using data from 2 hospitals in Korea. A dataset of paired transthoracic echocardiograms (TTEs) and electrocardiograms (ECGs) matched within a 2-week interval was constructed, separating pairs into baseline (first for each patient) and follow-up assessments. In addition to conventional supervised learning, we developed a patient-wise recalibration strategy that incorporated historical left ventricular ejection fraction measurements and prior AI-ECG outputs to adjust for future predictions, thus empirically mitigating confounding effects. Pretraining was also implemented to enhance the model's performance. RESULTS: The recalibrated 12-lead DeepECG LVSD model achieved an area under the receiver operating curve of 0.956 (95% CI 0.946-0.965) for internal validation and 0.940 (95% CI 0.936-0.945) for external validation of follow-up TTE-ECG pairs. The uncalibrated 12-lead DeepECG LVSD model also showed modest performance, with an area under the receiver operating curve of 0.953 (95% CI 0.941-0.965) in the internal validation and 0.947 (95% CI 0.943-0.951) in the external validation when tested on baseline TTE-ECG pairs. Recalibration yielded statistically significant improvements in the 12-lead DeepECG LVSD models (P<.001), with enhanced and more balanced performance across all clinical subgroups. CONCLUSIONS: Patient-wise recalibration improved accuracy and consistency across various comorbidities by mitigating performance degradation and bias. This broadens the application of AI-ECG for LVSD detection from low-risk screening to high-risk longitudinal monitoring.
27 Apr 2026
Read appraisal →PloS one
Research on the development of an automated system for psychology questionnaire generation based on large language models
This study reimagined the psychology questionnaire development process using large language model ((LLM) technology, aiming to overcome the protracted preparation cycles and significant human bias inherent in traditional scale development. We developed a specialized fine-tuning scheme for a corpus of 169 professional psychological questionnaires. By integrating instruction fine-tuning with human feedback reinforcement, we significantly enhanced the adaptability of the Qwen-2.5 and GLM-4 models for demanding professional psychological assessment tasks. The optimized models demonstrated remarkable gains across key dimensions: text generation quality (BLEU-4 increased by 0.05, ROUGE-L by 0.057), scientific rigor (logical consistency improved by 28.6%), and cultural adaptability (achieving over 85% accuracy in cross-regional expression conversion). This research solidly supports the feasibility of leveraging LLM technology to drive research paradigm transformation in psychology, offering crucial methodological support for developing efficient, intelligent psychological measurement tools.
27 Apr 2026
Read appraisal →Journal of medical Internet research
Exploring Attitudes Toward AI-Based Contactless Sensors in Health Among Five Stakeholder Groups: Qualitative Study
BACKGROUND: The rapid rise of artificial intelligence-based contactless sensors (AI-CS) is expected to significantly transform how patients are measured, monitored, and understood through a versatile, noninvasive approach to data collection and health assessment. However, there is a lack of empirical research specifically focusing on AI-CS in health. Moreover, existing studies tend to focus on medical or patient perspectives, while neglecting other stakeholders such as researchers, political actors, or the general public. OBJECTIVE: The study aims to provide an in-depth empirical ethical analysis and, through a multistakeholder approach, a uniquely comprehensive overview by addressing the research question: what are the attitudes of different stakeholders (patients, health care professionals, researchers, political stakeholders, and the general public) toward AI-CS and their applications in health? METHODS: We conducted a cross-sectional study with 104 participants using a semistructured interview guide. Interviews were analyzed using qualitative content analysis with ATLAS.ti software (ATLAS.ti Scientific Software Development GmbH), following a 3-component model of feelings, thoughts, and behavioral aspects. RESULTS: The results of the study provide an in-depth analysis of attitudes toward AI-CS in health among different stakeholders. Overall, the results show a high level of openness to AI-CS in health across all stakeholder groups. In terms of feelings and their correlation with behavioral aspects, 2 key trends emerged: first, greater experience and knowledge correlated with a reduced tendency to react emotionally. Second, participants with positive experiences with technologies were generally more open and positive toward contactless sensors. The combined findings on thoughts and behavioral aspects highlighted 3 key tensions-around contact(lessness) and the importance and ambivalence of touch, between protection and surveillance (particularly regarding path- and context-dependency) and between the benefits and challenges of unobtrusiveness (especially in relation to control and governance implications). In addition, the analysis revealed the need for information and consent about AI-CS and clarified possible technical implementations and fields of application. CONCLUSIONS: This study provides a comprehensive and empirically grounded ethical analysis of stakeholder attitudes toward AI-CS in health. The findings offer valuable guidance for the responsible development, implementation, and governance of AI-CS in health care contexts.
27 Apr 2026
Read appraisal →Journal of medical Internet research
Effectiveness of Digital Serious Games on Knowledge and Attitudes in Public Health Education: Systematic Review and Bayesian Network Meta-Analysis of Randomized Controlled Trials
BACKGROUND: Inadequate health literacy and low engagement challenge public health education. Digital serious games show potential to enhance health knowledge and attitudes. However, the comparative effectiveness of different game formats is unclear. OBJECTIVE: This study aimed to evaluate and compare the effectiveness of different digital serious game formats in improving public health knowledge and attitudes. METHODS: This systematic review and Bayesian network meta-analysis followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Seven databases (PubMed, CINAHL, Embase, PsycINFO, Cochrane Library, Scopus, and Web of Science) were searched from January 2000 to October 2025. An updated search in February 2026 identified no additional studies. Eligible studies were randomized controlled trials (RCTs) involving nonprofessional participants comparing digital serious games with traditional or noninteractive education. Standardized mean differences and 95% credible intervals were pooled using Bayesian network models with random effects. Subgroup analyses examined population characteristics, intervention duration, health topic, and delivery format. Risk of bias was assessed using the Cochrane risk-of-bias tool, and evidence certainty was rated using the Grading of Recommendations Assessment, Development and Evaluation. RESULTS: Forty randomized controlled trials from 19 countries (N=8764 participants) were included. Digital serious games significantly improved knowledge (standardized mean difference 0.66, 95% CI 0.32-0.99; I²=89.1%) and attitudes (standardized mean difference 0.50, 95% CI 0.27-0.76; I²=80.7%) compared with traditional education. Multisession interventions showed larger effects than single-session interventions for knowledge (0.76 vs 0.43) and attitudes (0.53 vs 0.30), with greater improvements among adolescents, nonpatient populations, and Asian studies. Network meta-analysis showed low heterogeneity (I²=8% for knowledge; 3% for attitudes). Mobile app-based, computer-offline, and web-based games ranked highest for knowledge; computer-offline, web-based, and virtual reality games ranked highest for attitudes. Evidence certainty was moderate for knowledge and low-to-moderate for attitudes. CONCLUSIONS: Digital serious games improve public health knowledge and attitudes across diverse contexts. Using a Bayesian network meta-analysis of randomized controlled trials, this review compares the relative effectiveness of different game formats. Mobile app-based, computer-offline, and web-based games most improved knowledge; computer-offline, web-based, and virtual reality formats most improved attitudes. Multisession interventions were more effective than single-session ones, particularly for adolescents and nonpatient populations. These findings guide scalable digital health education strategies. Future research requires adequately powered trials, longer follow-up, and standardized frameworks.
27 Apr 2026
Read appraisal →ACS sensors
A Temperature-Pressure Dual-Mode Flexible Patch Sensor with Temperature Compensation and Pressure Decoupling Functions for Pulse Detection and Recognition
Accurate and continuous detection of human pulse waves is critical for long-term health monitoring and physiological assessment during physical activity. However, most conventional flexible pulse sensors are single-mode, making them susceptible to interference from both ambient and physiological temperature fluctuations. Developing integrated dual-mode sensors remains challenging due to the difficulty in effectively decoupling pressure and temperature signals. Herein, we report a dual-mode flexible patch sensor featuring a unique "compass-like" structural design. The sensor demonstrates a broad pressure detection range (50 Pa-200 kPa) and rapid response/recovery times (60/96 ms), while its integrated temperature-sensing unit achieves a high resolution of 0.1 °C. Through unique structural design, the sensor achieves robust pressure decoupling of the temperature signal, with resistance fluctuations of only 0.102% under pressures up to 30 kPa. This independent temperature signal is subsequently utilized as a reference for the real-time temperature compensation of the pressure signal. The compensated pressure signal exhibits minimal temperature drift (41 × 10-6 °C-1 across 5-45 °C), enabling stable, interference-resistant monitoring of radial artery pulse waves under dynamic thermal conditions. Furthermore, when integrated with a ResNet-based deep learning model, the sensor successfully classifies diverse pulse signals with an accuracy of 97.6%. This research provides an effective solution for interference-resistant wearable electronics, offering significant potential for intelligent disease diagnosis and sports health management.
27 Apr 2026
Read appraisal →ACS sensors
Multifunctional Wearable OLED-OPD System with Wavelength-Tunable Emission for Phototheranostics
The growing demand for noninvasive wearable healthcare technologies calls for compact platforms capable of integrating diagnostic sensing and therapeutic light delivery within a single device. Conventional systems typically rely on spatially separated light sources or rigid inorganic LEDs, leading to inefficient area usage and limited mechanical compatibility with soft biological tissues. Here, we present a multifunctional wearable optoelectronic platform based on wavelength-tunable organic light-emitting diodes (WTOLEDs) integrated with organic photodiodes (OPDs). The vertically stacked red and blue OLED structure enables polarity-dependent wavelength switching under AC driving, generating alternating emissions at 624 nm and 464 nm from a single compact device without lateral source separation. The surrounding geometry, in which the light-emitting region encircles the light-receiving element, provides an area-efficient optical interface that enhances optical coupling and signal sensitivity within a flexible form factor. Using this platform, we demonstrate dual-wavelength physiological monitoring via photoplethysmography (PPG) and therapeutic applications, including red-light photobiomodulation and blue-light-mediated bilirubin photodegradation. The device further exhibited stable thermal and mechanical performance under wearable operating conditions. These results establish a compact phototheranostic architecture enabling wavelength-selective sensing and therapy within a single wearable platform.
26 Apr 2026
Read appraisal →ACS sensors
Wearable Flexible Acoustic Alarm Device Based on MXene/Laser-Induced Graphene@Polyimide Thermoacoustic Film
Flexible acoustic devices have advantages of high flexibility and large sound output area, capable of adhering to the surface of the skin or clothing, which shows great potential in the field of wearable electronics. However, the design of acoustic material structures and their functional applications are still difficult. Here, we fabricated a porous laser-induced graphene (LIG) microarray layer on a polyimide (PI) film by laser direct writing. Subsequently, we uniformly coated the few-layer MXene nanosheets onto the LIG surface to obtain an MXene/LIG@PI film featuring low specific heat capacity per unit area, large interlayer spacing, excellent electrical conductivity, and high thermal conductivity. Meanwhile, the MXene/LIG@PI-based acoustic device composed of flexible copper electrodes exhibits excellent acoustic performance with a high sound pressure level of 77.6 dB and maintained a stable acoustic spectrum as the frequency increased from 20.0 Hz to 20.0 kHz. Finally, the portable acoustic alarm device composed of the piezoelectric module and the MXene/LIG@PI-based acoustic module was designed, which can be worn on various parts of the human body clothing, to provide audible alarm reminders in case of accidental falls or drops during outdoor activities. This study provides a novel approach for designing new flexible acoustic materials and devices.
26 Apr 2026
Read appraisal →ACS sensors
Wearable Multimodal Detection System for Real-Time In Situ Analysis of Exhaled Breath Condensate
Exhaled breath condensate (EBC) analysis, a promising noninvasive respiratory monitoring method, has emerged as a pivotal technique for assessing the health status of patients with respiratory disorders and is widely used in clinical research and daily health management. However, conventional analytical methods face challenges in real-time in situ detection of physicochemical indicators and active EBC collection in a power-free way. Herein, a wearable multimodal detection system (WMDS) with efficient collection and real-time analysis of EBC is developed. Specifically, the WMDS consists of a bio-inspired collector, an electrochemical sensor (EBC analysis), a respiratory sensor (humidity and respiratory rate), a temperature sensor, and a flexible printed circuit board. The miniature-sized collector with a cactus spine-like structure can actively harvest 4.1 μL of EBC within 1 min without power consumption. Leveraging self-developed sensors and wireless data transmission circuitry, the WMDS enables real-time in situ monitoring of multimodal EBC analytes (hydrogen peroxide, nitrite, urea) and respiratory parameters (temperature, humidity, and rate). Remarkably, the WMDS exhibits dual-range detection capability covering both physiological and pathological conditions: the low-concentration range of 0-500 μmol/L is applicable for routine health monitoring and early disease screening, with detection limits (LODs) of 0.209, 0.155, and 0.573 μmol/L and sensitivities of 2.7 × 10-2, 3.4 × 10-2, and 9.0 × 10-3 μA/(μmol/L) for EBC analytes; the high-concentration range exceeding 500 μmol/L is designed for severe pathological condition detection, where LODs and sensitivities are 302.2, 278.7, 325.2 μmol/L and 1.9 × 10-2, 1.3 × 10-2, 3.9 × 10-3nA/(μmol/L) respectively. As a proof-of-concept, the WMDS is applied to on-body respiratory monitoring, validating its potential application in real-time in situ health monitoring.
26 Apr 2026
Read appraisal →ACS sensors
Self-Correction of pH-Induced Signal Variations in Methylene Blue-Labeled Aptamer Electrochemical Biosensors: Wearable Cortisol Detection in Sweat
Common electrochemical aptamer-based (E-AB) biosensors employing methylene blue (MB) redox reporters suffer from significant pH-induced signal variations. Consequently, dynamic pH fluctuations in biofluids, such as in sweat, can greatly distort the measured signal and lead to inaccurate target quantification. Here, we introduce a self-correction strategy that enables accurate analyte quantification by compensating for pH-induced signal variations in MB-based aptasensors using the Nernstian shift of the MB peak potential. The highly reproducible reversible pH dependence of the MB peak potential enables real-time monitoring of the sample pH that provides a continuously synchronized built-in compensation of the pH interference, thereby eliminating the need for external pH sensors or additional measurement steps. This precise yet simple and effective self-correction of pH effects ensures that the Square Wave Voltammetry (SWV) signal reliably reflects real-time analyte variations, as demonstrated using a cortisol MB-labeled E-AB biosensor in both in vitro and on-body settings. In vitro measurements in artificial sweat across pH 5.5-7.5 showed excellent correlation with real-time pH and cortisol changes, confirming the reliability of the peak potential-based correction. On-body measurements using an epidermal wearable patch showed cortisol changes in sweat that would have been missed without this signal-correction method. This approach is broadly applicable to other MB-labeled E-AB biosensors and biofluids, providing a robust strategy for continuous on-body monitoring.
26 Apr 2026
Read appraisal →JMIR formative research
Exploring User Experiences of an Augmented Reality Smartphone App Prescribing Exercise for Children and Young People With Cancer: Results From a Qualitative Study
BACKGROUND: Mobile health (mHealth), and specifically smartphone apps, have grown exponentially in both functionality and accessibility and are becoming an important component of health care. Research exploring the use of mHealth for managing or treating chronic diseases, such as cancer, has shown promising effects. Yet, comparatively little work has examined how such technologies can enhance exercise interventions for young people with cancer. To optimize the effectiveness of mHealth in these contexts, it is essential to build a stronger evidence base on user experience. OBJECTIVE: This study aimed to investigate how healthy children and young people engaged with an augmented reality (AR) app developed specifically for children and young people undergoing cancer treatment, and to identify design features that may support engagement and behavior change in the intended clinical population. METHODS: School and university students, aged 8-21 years, were eligible to participate in the study. Practical workshops allowed participants to engage with the AR exercise app before taking part in focus groups to explore user experiences. Data were analyzed using qualitative content analysis, which also involved a critical friend approach using 2 researchers (HM and KS). Suggested improvements were mapped against the motivational affordances' taxonomy. RESULTS: A total of 39 participants aged 8-21 years took part in the focus group study. Participants found the demonstrations and varied exercises useful but expressed some concerns regarding data safety and functionality of the novel AR avatar. It was proposed that additional educational components, challenges, and rewards, as well as a customizable avatar, social support features, and audio instructions for a more inclusive design would be desirable and could enhance user experience. When mapped against the motivational affordances taxonomy, the suggested improvements aligned primarily with mechanisms of user education, challenges, feedback, cooperation, and comparison. CONCLUSIONS: This study provides an understanding of how apps that prescribe exercise can be optimized to enhance motivation and user experience. By assessing feedback and suggestions for improvements, the findings highlight key design features that may support engagement. While this initial work focused on healthy, age-matched participants, further evidence specifically in children and young people with a childhood cancer diagnosis is needed.
26 Apr 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
ACT on Vaping: Pilot Randomized Controlled Trial of a Novel Digital Health App With Text Messaging for Young Adult Vaping Cessation
BACKGROUND: There is no published evidence to support the efficacy of any digital vaping cessation program for young adults (YAs) at differing levels of readiness to quit. In this pilot randomized controlled trial, we evaluated the preliminary acceptability and efficacy of a program for vaping cessation based on acceptance and commitment therapy (ACT on Vaping), delivered via a smartphone app and text messaging. METHODS: YAs aged 18-30 (n = 61) were randomized 1:1 to ACT on Vaping (n = 31) or incentivized text message control (n = 30). Outcome data were collected at 3 months post-randomization. Results were compared against a priori benchmarks for acceptability (satisfaction of ≥3.5 on 5-point scale) and efficacy relative to control (meeting at least one of three): ≥1-point difference in Contemplation Ladder change scores; ≥5 percentage difference in 24-hour quit attempts, ≥5 percentage difference in cotinine-confirmed 30-day point prevalence abstinence (PPA) from all non-therapeutic nicotine/tobacco. RESULTS: Satisfaction with ACT on Vaping averaged 3.8, exceeding the acceptability benchmark. A higher proportion of participants in the ACT on Vaping arm reported a 24-hour quit attempt (87.5% vs. 75.9%), exceeding the efficacy benchmark. Both changes in quit readiness (+0.96 in ACT on Vaping vs. +0.72 in control) and cotinine-confirmed 30-day PPA (4.2% in ACT on Vaping vs. 0% in control) were descriptively higher for ACT on Vaping but did not reach the benchmark level for efficacy. CONCLUSIONS: ACT on Vaping had promising acceptability and preliminary efficacy. A fully powered trial of ACT on Vaping is warranted to evaluate its efficacy. IMPLICATIONS: Digital interventions are a promising yet under-researched approach for reaching and supporting YAs to quit vaping. This proof-of-concept pilot randomized controlled trial evaluated a novel mobile health application and associated text messaging program (ACT on Vaping) for young adult vaping cessation and found preliminary evidence for acceptability and efficacy relative to an incentivized text message control arm, warranting evaluation in a fully powered trial as a next step. TRIAL REGISTRATION: NCT05897242.
26 Apr 2026
Read appraisal →JMIR mental health
Effectiveness of a Fully Automated Mobile Therapeutic Versus a General Chatbot in Reducing Depression and Anxiety and Improving Well-Being: Feasibility Randomized Controlled Trial
BACKGROUND: Given the increasing prevalence of depression and anxiety disorders and enduring barriers to care, there is a critical need for alternative treatment options. Generative artificial intelligence (AI) chatbots show promise for increasing access to mental health care, though more direct research is needed to establish their efficacy. OBJECTIVE: This pilot study aimed to test the efficacy of a generative mental health chatbot rooted in solution-focused therapy compared to the general-purpose ChatGPT and an assessment-only control (AOC) group on depression, anxiety, and well-being. METHODS: A total of 185 English-speaking adults were recruited online and randomly assigned to one of three groups: AI therapy, ChatGPT, or AOC. Of these, 147 eligible participants filled out a pretreatment assessment. Over a 3-week period, the AI therapy group (n=44) was instructed to complete 3 structured, fully automated app-based sessions per week (9 total), while the ChatGPT group (n=60) was instructed to engage in 9 unstructured conversations with ChatGPT (GPT-4o-based models). The control group (n=43) received no intervention. In the AI therapy group, 39% (n=17) completed all sessions, as did 62% (n=38) of those in the ChatGPT group. Primary outcome measures, self-assessed online at baseline and postintervention, included the Patient Health Questionnaire-9 (PHQ-9), Overall Depression Severity and Impairment Scale (ODSIS) (depression), 7-item Generalized Anxiety Disorder Scale (anxiety), and World Health Organization Well-Being Index (5-item version) (well-being). Linear mixed effects models were used for data analysis. RESULTS: Compared to AOC, both the AI therapy group (d=-0.47; P=.01) and the ChatGPT group (d=-0.44; P=.02) demonstrated significant reductions in depression scores measured by PHQ-9. The AI therapy group showed nonsignificant reductions in anxiety (d=-0.37; P=.11) and ODSIS depression scores (d=-0.25; P=.22) and an increase in well-being (d=0.12; P=.53) compared to AOC. Similarly, a nonsignificant reduction in anxiety (d=-0.27; P=.22) and ODSIS depression scores (d=-0.12; P=.53) and an increase in well-being (d=0.20; P=.29) were observed in the ChatGPT group compared to AOC. The AI therapy group did not significantly outperform the ChatGPT group on any outcomes (PHQ-9: b=-0.19; d=0.03; P=.87; 7-item Generalized Anxiety Disorder Scale: b=-0.57; d=-0.11; P=.62; ODSIS: b=-0.59; d=-0.13; P=.50; and WHO: b=-0.38; d=-0.07; P=.69). CONCLUSIONS: Both the structured generative AI chatbot and ChatGPT showed a significant reduction in depression scores compared to the control group. No significant effects were observed across other outcomes, although descriptive trends indicated improvements in anxiety. While the AI therapy group showed descriptively better outcomes for depression and anxiety, differences between groups were not significant. A larger sample and longer intervention may be needed for the emerging trends to yield clinically meaningful effect sizes.
26 Apr 2026
Read appraisal →Biomedizinische Technik. Biomedical engineering
AI in healthcare: mapping Romania's transition from research to intellectual property & industry solutions
OBJECTIVES: Romania is aligning its healthcare AI ecosystem with the European Union's AI Act (2024/1689), through its own National AI Strategy. Despite a growing volume of research in medical AI, significant gaps remain in translating this output into patents and domestically developed high-risk medical devices. This study assesses Romania's research productivity, patenting activity, and commercial solutions in medical AI. METHODS: We conducted an analysis combining bibliometric data, patent information, and medical device databases, and analyzed 619 Romanian-authored articles on medical AI, 272 patent records, and identified domestic AI-powered software providers. RESULTS: Romania's publication output in medical AI has surged post-2018, primarily driven by academic institutions in Bucharest and Cluj. Patents are predominantly filed by multinationals, indicating a potential disconnect between research and industrial output. Six AI medical software solutions were identified, most under the low-risk classifications. CONCLUSIONS: While Romania is expanding its medical AI research, it faces barriers in converting academic output into innovation. The dominance of foreign corporations in patent filings, reliance on international funding for high-impact research, and the scarcity of domestically developed high-risk medical AI solutions highlight important gaps. Addressing these disparities is essential for national alignment in the AI medical innovation space.
26 Apr 2026
Read appraisal →JMIR mental health
Expected Competencies and Personal Attributes of Digital Health Navigators to Support Digital Mental Health Care: Focus Group and Interview Study With Patients and Health Care Professionals
BACKGROUND: Digital mental health apps (DMHAs), and in particular digital therapeutics (DTx), offer promising opportunities to support mental health care. However, their effective use in outpatient settings in Germany remains limited. To overcome this gap, the role of digital health navigators (DHNs) has been introduced. DHNs are trained individuals who support patients and health care professionals in selecting, using, and integrating DMHAs into care. Despite increasing interest in this role, there is limited evidence on the competencies, knowledge, and personal attributes required for DHNs to work effectively in mental health settings. OBJECTIVE: The study aims to explore the expected competencies, knowledge areas, and personal attributes that DHNs need to effectively support the implementation and use of DTx in outpatient mental health care. METHODS: As part of the prestudy of the Digital Navigators for Acceptance and Competence Development with Mental Health Apps (DigiNavi) study, a qualitative study was conducted involving 35 participants (7 general practitioners, 8 patients in general practice, 11 outpatient psychiatrists/psychologists, and 9 patients in psychiatric outpatient clinics) from different general practices and psychiatric outpatient clinics in Germany. A total of 17 semistructured interviews and 4 focus groups were conducted to explore expectations of DHNs. Data were analyzed using qualitative content analysis. RESULTS: Participants emphasized that DHNs should combine strong interpersonal skills (empathy, patience, and sensitive communication) with technical and basic clinical competencies. Most favored DHNs as integrated clinical team members (eg, medical assistants), citing their existing patient relationships, but noted time and training constraints. Key expectations included the ability to support patients with DTx use, adapt communication to individual needs, and convey data privacy information clearly. Foundational knowledge of mental health conditions and sensitivity to crises were considered important for identifying warning signs and escalating concerns. While DHNs were seen as essential intermediaries between patients, health care professionals, and DTx, participants highlighted the necessity for clearly defined roles, structured training, and realistic expectations to prevent role overload and enable sustainable implementation in outpatient mental health care. CONCLUSIONS: DHNs require a specialized skill set that bridges clinical understanding, digital expertise, and interpersonal competence. Our results lay the groundwork for developing training curricula and implementation strategies that align with real-world expectations for the DHN role. Defining these core competencies is essential for supporting the sustainable and effective integration of DMHAs into mental health care.
26 Apr 2026
Read appraisal →JMIR mHealth and uHealth
Effectiveness of Mobile Health Interventions in Pediatric Cancer: Systematic Review and Meta-Analysis of Randomized Controlled Trials
BACKGROUND: Cancer poses a significant threat to children's health, and mobile health (mHealth) is emerging as a key tool for remote disease management, health education, and follow-up. However, evidence of its effectiveness remains limited. OBJECTIVE: This study aimed to summarize the effects of mHealth interventions for pediatric cancer compared with usual care, providing evidence-based support for optimizing intervention models and improving patient outcomes. METHODS: A systematic search of 14 databases identified randomized controlled trials (RCTs) on mHealth apps for pediatric patients with cancer from inception to August 1, 2025. Two reviewers independently screened studies, extracted data, assessed bias risk, and graded evidence quality. The meta-analysis was conducted using RevMan 5.4 and Stata 15. RESULTS: A total of 24 RCTs involving 2645 patients were included. This review found that mHealth interventions significantly reduced infection rates (odds ratio [OR] 0.25, 95% CI 0.10-0.60; P=.002) and the overall incidence of peripherally inserted central catheter (PICC) complications (OR 0.16, 95% CI 0.10-0.24; P<.001), while improving quality of life (standardized mean difference [SMD] 1.34, 95% CI 0.13-2.55; P=.03), self-management ability (SMD 6.39, 95% CI 1.26-11.53; P=.01), and treatment adherence (OR 2.83, 95% CI 1.41-5.66; P=.003). However, mHealth interventions had no significant effect on PICC catheter displacement (OR 0.44, 95% CI 0.15-1.29; P=.13) or health knowledge (SMD 4.44, 95% CI -2.40 to 11.29; P=.20). Further high-quality studies are needed to verify their impact in these areas. The intervention components covered 9 behavior change techniques: goals and planning, feedback and monitoring, social support, shaping knowledge, repetition and substitution, reward and threat, comparison of outcomes, natural consequences, and regulation. CONCLUSIONS: This systematic review and meta-analysis synthesized evidence from RCTs. The findings support the use of mHealth to reduce infections and PICC-related complications among pediatric patients with cancer while improving quality of life, self-management capabilities, and treatment adherence. These results underscore the importance of incorporating mHealth strategies into pediatric cancer care and guide the development and enhancement of future mHealth interventions.
26 Apr 2026
Read appraisal →JMIR aging
The Associations Between Digital Exclusion and Physical or Cognitive Function in Middle-Aged and Older Adults: Systematic Review and Meta-Analysis
BACKGROUND: Digital exclusion posed a significant challenge, especially in middle-aged and older adults, which affected their health outcomes. However, the evidence regarding the associations of digital exclusion on physical or cognitive function outcomes was controversial, and no systematic review had been performed to synthesize the pooled associations. OBJECTIVE: This study aimed to explore the relationship between digital exclusion and physical or cognitive function in middle-aged and older adults. METHODS: We conducted a systematic review and meta-analysis of cohort and cross-sectional studies, including Chinese or English publications retrieved from PubMed, Embase, Web of Science, PsycINFO, Scopus, CNKI, and Wanfang databases up to August 31, 2024. The risk of bias was evaluated using the Newcastle-Ottawa Scale (NOS). The pooled effect size was calculated based on odds ratios (ORs), hazard ratios, risk ratios, and 95% CIs. This study was registered on PROSPERO (CRD42024585459). RESULTS: Nineteen studies met the inclusion criteria, including 13 cohort studies and 6 cross-sectional studies, which had moderate-to-low risk of bias. The pooled analysis indicated that digital exclusion had prospective associations with decreased basic activities of daily living (incidence rate ratio 1.35, 95% CI 1.12-1.64, I2=94.7%) and instrumental activities of daily living (incidence rate ratio 1.46, 95% CI 1.13-1.89, I2=96.2%), or cross-sectional association with activities of daily living (OR 1.23, 95% CI 0.41-3.73, I2=91%), with no statistical significance in the prospective association with frailty (OR 1.21, 95% CI 0.92-1.59, I2=95.2%). There were prospective associations between digital exclusion and dementia (hazard ratio 1.78, 95% CI 1.43-2.22, I2=0%), decreased Mini-Mental State Examination scores (OR 1.96, 95% CI 1.39-2.75, I2=0%), as well as cross-sectional associations with Mini-Mental State Examination scores (OR 2.90, 95% CI 2.07-4.07, I2=0%), and no statistical significance in the prospective association with cognitive impairment (risk ratio 2.08, 95% CI 0.98-4.44, I2=78.2%). CONCLUSIONS: Our findings indicated the negative associations of digital exclusion with physical and cognitive functions. Future research and clinical practice should consider designing digital interventions and services that match the physical and cognitive capacities and preferences of middle-aged and older adults, thereby improving digital engagement and reducing the harms associated with digital exclusion. Policies should focus on expanding access, reducing financial barriers, and improving digital literacy. However, due to the presence of heterogeneity and publication bias, the results should be interpreted cautiously.
26 Apr 2026
Read appraisal →Biomedizinische Technik. Biomedical engineering
Advanced deep learning framework for breast cancer detection using digital breast tomosynthesis images
OBJECTIVES: Early and accurate detection of breast cancer is critical for improving patient survival outcomes. This study proposes a robust deep learning-based framework for breast cancer detection using digital breast tomosynthesis (DBT) images, leveraging both single-slice and multi-slice inputs. METHODS: The proposed work includes image normalization, resizing and Laplacian Pyramid Enhancement (LPE). Various features were extracted and fused in different combinations. To retain the most discriminative features Exhaustive Feature Selection (EFS) is used. A hybrid model integrated using ResNet V2, MobileNet V3 and Inception V3+ for classification. Finally ensemble learning with XGBoost was applied and Hyperparameters were optimized using a grid search strategy. RESULTS: The hybrid model with multi-slice DBT inputs achieved better improvements with accuracy, sensitivity, specificity and area under the curve (AUC). While applying LPE, feature fusion and EFS substantially enhanced the hybrid model's diagnostic performance. CONCLUSIONS: The findings demonstrate the strong potential of advanced methods in enhancing performance. Future research work will focus on integrating this pipeline with clinical decision support systems, multi-center datasets and extending to other breast imaging modalities.
26 Apr 2026
Read appraisal →JMIR mHealth and uHealth
A Deep Neural Network for Interpreting Wearable Electrocardiogram Data in Atrial Fibrillation: Prospective Observational Diagnostic Accuracy Study
BACKGROUND: Atrial fibrillation (AF) and atrial flutter (AFL) are common arrhythmias associated with the risk of ischemic stroke, which can be reduced with anticoagulation therapy. Thus, early diagnosis of AF and AFL is essential. However, diagnosis may be challenging due to the paroxysmal and asymptomatic nature of these arrhythmias. OBJECTIVE: Current diagnostic workflows involve time-consuming and resource-intensive manual review of noisy signals and prolonged recordings. We evaluated a mobile system that combines a wireless wearable single-lead chest strap electrocardiogram (ECG) and a novel deep neural network (DNN)-based artificial intelligence (AI) method for detecting AF/AFL episodes, AF/AFL burden, and rhythm change and estimated the delay in the detection of rhythm change from AF/AFL to sinus rhythm. We also assessed the rhythm classification performance. METHODS: A total of 116 patients with recent-onset AF or AFL undergoing cardioversion were monitored using a mobile single-lead chest strap ECG system. Simultaneously, a 3-lead Holter ECG served as the reference. The DNN-based AI analyzed the single-lead chest strap ECG data to detect AF/AFL, non-AF/AFLrhythm, and noninterpretable segments, as well as to estimate AF/AFL burden and detect rhythm change. Performance metrics included sensitivity, specificity, positive predictive value, negative predictive value, and intraclass correlation coefficient for AF and AFL burden estimation. RESULTS: The sensitivity and specificity for detecting AF/AFL were 91.9% (204.9/223.0 h) and 99.6% (242.4/243.5 h), respectively. The sensitivity for detecting AF was 96.2% (191.5/199.0 h), whereas it was 55.8% (13.4/24.0 h) for detecting AFL. The positive predictive value and negative predictive value for AF/AFL detection were 99.5% (204.9/206.0 h) and 93.1% (242.4/260.5 h), respectively. The intraclass correlation coefficient between the AF/AFL burden estimated by the DNN-based AI method and that derived from the physician-interpreted reference ECG was 0.96 (95% CI: 0.94-0.97; P<.001). Rhythm change detection occurred within 1 minute in most cases. CONCLUSIONS: The mobile single-lead chest strap ECG system powered by a DNN-based AI algorithm demonstrated strong performance in detecting AF, estimating AF burden, and recognizing rhythm change to sinus rhythm. This AI-driven approach enables automated and accurate rhythm analysis, supporting clinical decision-making. Further validation in real-world ambulatory settings is warranted.
25 Apr 2026
Read appraisal →Journal of medical Internet research
Personalized Smartphone Messaging for Secondary Prevention After Percutaneous Coronary Intervention: Randomized Controlled Trial
BACKGROUND: Cardiac rehabilitation improves outcomes after percutaneous coronary intervention (PCI), but participation remains suboptimal due to barriers such as distance, cost, and lack of referral. Mobile health interventions may enhance accessibility and engagement. OBJECTIVE: This study evaluated the effectiveness and user acceptability of a smartphone messaging app (AnSim) in improving cardiovascular risk factors among patients who recently underwent PCI. METHODS: A 2-arm randomized controlled trial was conducted at 2 Korean hospitals; 120 patients who had undergone PCI within the prior month were randomized (1:1) to receive AnSim plus usual care (intervention) or usual care alone (control). The intervention comprised personalized messages tailored by behavioral stage and risk profile, delivered 6 times/week for 6 months. Outcomes were assessed at baseline, 6 months, and 9 months. The primary outcome was change in blood pressure (BP) at 6 and 9 months. Secondary outcomes were lipid profiles, hemoglobin A1c, BMI, smoking status, physical activity, and achievement of guideline-recommended risk-factor targets. User acceptability was assessed by questionnaire. RESULTS: Overall, 115 (95.8%; intervention: n=58, control: n=57) participants completed follow-up at 6 months and 110 (91.7%; intervention: n=54, control: n=56) at 9 months. At 6 months, systolic BP was 124.9 (SD 16.7) mmHg in the intervention group and 124.8 (SD 13.1) mmHg in the control group (between-group difference 0.1 mmHg, 95% CI -5.4 to 5.6), and diastolic BP was 80.2 (SD 11.4) mmHg vs 78.4 (SD 10.6) mmHg (difference 1.8 mmHg, 95% CI -2.3 to 5.9). At 9 months, systolic BP was 126 (SD 14.2) mmHg vs 128 (SD 15.4) mmHg (difference -2 mmHg, 95% CI -7.6 to 3.6), and diastolic BP was 80.8 (SD 13.2) mmHg vs 81.3 (SD 12.0) mmHg (difference -0.5 mmHg, 95% CI -5.3 to 4.3). No significant between-group differences were observed in secondary outcomes. In post hoc analyses, participants with above-median message reading were more likely to achieve ≥4 of 5 guideline-recommended risk-factor targets at 9 months (69% vs 19.2%; relative risk 3.59, 95% CI 1.57-8.18; P<.001) and logged more diary entries (mean difference 203.4 entries, 95% CI 79.3-327.5; P=.001). BP responders at 9 months read messages on more days than nonresponders (110.3 vs 83.3 d; mean difference 27 d, 95% CI 4.6-49.3; P=.02). Among survey respondents (54/60, 90%), 87% (47/54) found the messages helpful and 81.4% (44/54) wished to continue. Two events occurred in the control group (P=.50). CONCLUSIONS: This trial found no between-group improvement in BP with low-intensity personalized messaging compared to an attention-matched diary app, but exploratory analyses suggest engagement may be a key determinant of potential benefit. Unlike comprehensive app-based cardiac rehabilitation programs, AnSim provides scalable behavioral reinforcement that can be integrated into post-PCI follow-up with minimal burden. These findings support future pragmatic trials optimizing engagement strategies and evaluating longer-term clinical and implementation outcomes.
25 Apr 2026
Read appraisal →Journal of medical Internet research
Perspectives Regarding the Privacy, Security, and Confidentiality of Data Collected via mHealth Apps in Saudi Arabia: Qualitative Analysis
BACKGROUND: As Saudi Arabia aims to enhance the adoption and utilization of mobile health (mHealth) apps in the health care sector, it is vital to identify the potential challenges that may be faced in the process and how to address them. OBJECTIVE: This study aimed to explore patients' and stakeholders' perspectives regarding the privacy, security, and confidentiality of data collected via mHealth apps in Saudi Arabia. METHODS: A qualitative research design was used by conducting an in-depth interview with 25 participants, comprising health care workers (HCWs), patients, and mHealth app developers. The interview questions focused on awareness of mHealth apps and issues relating to data security, privacy and confidentiality, benefits of mHealth apps, challenges faced, and features that may improve the utilization of such digital health technologies. Thematic analysis was performed using NVivo software (version 12; QSR International). RESULTS: Patients were relatively less informed regarding events associated with data privacy and security than HCWs. Specific factors were identified to influence patient awareness with HCWs, suggesting the need for patient education and collaboration with cybersecurity organizations. Participants posited that advanced security features, user-friendly features, online consultation for emergencies, remote monitoring features, and taking patient needs into account are important facilitators of mHealth apps. Shared experiences mainly revolved around the accessibility to information and reliability of mHealth apps, while the challenges were related to poor usability, technical difficulties, data security, and data breaches. Patient autonomy, remote monitoring, self-care management, medical adherence, and time and cost-saving summed up the perceived benefits of the mHealth app. CONCLUSIONS: These findings may assist policymakers in developing strategies to improve Saudi users' and patients' adoption of mHealth apps and address the concerns raised to benefit significantly from these advanced health care modalities.
25 Apr 2026
Read appraisal →Journal of the American College of Cardiology
Cardiovascular Science, Medicine, and Society: The Brave New World: The ACC Braunwald Lecture 2025
Recent scientific and technological advances have dramatically expanded the possibilities in cardiovascular medicine. Landmark clinical trials have introduced transformative therapies for lipid disorders, heart failure, and obesity, offering clinicians powerful new tools to manage increasingly complex patient populations. Discoveries in molecular medicine, including gene therapies and RNA therapeutics, have ushered in new biopharmaceutical platforms and promise the potential for curative or infrequent dosing regimens. Minimally invasive, percutaneous, and robotic surgical techniques, along with next-generation devices and interventional procedures, are enhancing patient outcomes and broadening access to advanced care. The rapid rise of artificial intelligence and digital health technologies is reshaping how we gather, interpret, and act on data across the care continuum. Together, these developments mark the arrival of a brave new world in cardiovascular medicine. Yet the promise of this new era is tempered by unequal access, ethical dilemmas, and potential unintended consequences at both the patient and societal levels. This review is written in honor of Dr Eugene Braunwald, who has been a pioneer in cardiovascular science and technological innovation. The paper summarizes recent milestones in cardiovascular medicine and offers a forward-looking perspective on how to realize the full potential of innovation by translating scientific progress into equitable, responsible care and improved health outcomes.
25 Apr 2026
Read appraisal →JMIR human factors
Personalizing Mobile Apps for Health Behavioral Change According to Personality: Cross-Sectional Validation of a Preference Matrix
BACKGROUND: Mobile health (mHealth) apps are increasingly used to support healthy lifestyle behaviors through features such as health tracking and personalized reminders. Personalized messaging, tailored to users' profiles, has been shown to improve engagement and retention in health-related contexts. Prior research has linked personality traits, based on the Big Five model, to preferences for specific app mechanisms, leading to the development of a preference matrix for personalizing mHealth apps. This matrix comprises 15 mechanisms derived from behavior change techniques and gamification elements, intended to guide developers in optimizing engagement according to user profiles. OBJECTIVE: This study aimed to validate this preference matrix by examining whether the associations between mechanisms and Big Five personality traits reported in the literature align with user preferences observed in an experimental setting. METHODS: A cross-sectional study was conducted using an online survey that collected demographic data, mHealth app usage, and personality traits. Participants were presented with mockups illustrating 15 mechanisms and were asked to select their preferred options. Logistic regression and ordinal logistic regression analyses were performed to examine associations between personality traits, mechanism selection, and motivation scores. All analyses were adjusted using the Bonferroni correction to account for multiple comparisons. RESULTS: A total of 214 participants completed the survey (mean age 29.42, SD 10.41 y; n=118, 55.1% women; n=89, 41.6% men; n=5, 2% identifying as other; and n=2, 1% nonrespondents). Higher conscientiousness significantly increased the likelihood of selecting the collection mechanism (eg, collecting badges or points; odds ratio [OR] 1.87, 95% CI 1.27-2.75). For competition (eg, competing with other users), conscientiousness (OR 3.22, 95% CI 1.73-6.00) and agreeableness (OR 1.93, 95% CI 1.08-3.45) were significant predictors. Preferences for rewards (eg, virtual incentives such as points or virtual currency) were associated with conscientiousness (OR 2.36, 95% CI 1.53-3.63) and neuroticism (OR 1.97, 95% CI 1.36-2.86). Additionally, 4 mechanisms-self-monitoring, progression, challenge, and quest-were selected by more than half of the participants, independent of personality traits. CONCLUSIONS: The findings partially validate the proposed preference matrix. Conscientiousness consistently emerged as a key predictor of preference across multiple mechanisms, highlighting its central role in engagement with gamified mHealth features. While some mechanisms appear to have universal appeal, others show personality-specific preferences, underscoring the value of combining baseline mechanisms with targeted personalization strategies in mHealth app design.
25 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Online self-compassion-based interventions on patient outcomes in patients with cancer: a systematic review
PURPOSE: This systematic review investigates the effectiveness of online self-compassion-based interventions and their impact on the outcomes of cancer patients. METHODS: A comprehensive search was carried out in the Web of Science Core Collection, Cochrane Library, CINAHL, PubMed, ScienceDirect, Scopus, Ovid, and SpringerLink databases without any year limitation until January 01, 2026. Cochrane and PRISMA guidelines were used for systematic review and reporting. RoB2 and Joanna Briggs Institute guidelines were utilized to assess the risk of bias. RESULTS: Eight studies included in the systematic review were conducted between 2017 and 2025 in Australia, the USA, the Netherlands, China, and Türkiye. The self-compassion interventions applied in the studies included mindful self-compassion, self-compassion-focused writing, compassion mind training, and kindness interventions. Online self-compassion-based interventions have been found to increase patients' levels of self-compassion, mindfulness, body image appreciation, well-being, self-acceptance, posttraumatic growth, and positive affect while reduce their levels of self-criticism, social isolation, anxiety, stress, and depression, body image distress. CONCLUSIONS: Studies have shown the positive effects of self-compassion-based interventions on patient outcomes. A sensitivity analysis that included only randomized controlled trials demonstrated robustness in all outcomes except anxiety; however, some research results were obtained from a single randomized controlled trial. Since some study results were derived solely from quasi-experimental studies, they were not included in the analysis. Therefore, more randomized controlled research is required to integrate these interventions into clinical patient care. Online self-compassion interventions can contribute to the planning of holistic nursing interventions for patient outcomes.
25 Apr 2026
Read appraisal →The British journal of ophthalmology
Multimodal artificial intelligence for retinal detachment diagnosis using fundus imaging and patient questionnaires
BACKGROUND/AIMS: This study aimed to develop a multimodal artificial intelligence (AI) system that integrates fundus imaging and patient questionnaire data to achieve clinician-level diagnostic accuracy for diagnosing retinal detachment (RD). METHODS: Ultra-widefield fundus images and comprehensive patient questionnaires were collected from patients with RD and healthy controls at Tsukazaki Hospital. A multimodal model was developed using the Contrastive Language-Image Pretraining framework to classify RD cases, alongside separate image-only and questionnaire-only models for comparison. Per-image and per-subject analyses were conducted to assess model performance. RESULTS: The multimodal model outperformed single-modal models in per-image and per-subject assessments. It achieved accuracy, recall and F1 scores of 0.899±0.054, 0.902±0.043 and 0.902±0.048 in the per-image analysis and 0.893±0.071, 0.949±0.044 and 0.873±0.074 in the per-subject analysis, respectively. The AI model's overall diagnostic accuracy was slightly lower than that of human clinicians; however, it exhibited a higher recall rate, indicating improved detection of true RD cases. CONCLUSION: Integrating fundus imaging with patient questionnaire data significantly improves AI-based RD diagnosis. Future research needs to focus on expanding the dataset and refining the questionnaire design to further improve model performance.
25 Apr 2026
Read appraisal →Journal of medical economics
Value frameworks for digital health technologies: a comparative analysis
AIMS: This study aimed to review and compare existing value frameworks for digital health technologies (DHTs) and identify common domains to guide their evaluation. MATERIALS AND METHODS: A comprehensive literature search was conducted across PubMed, Embase, and Google Scholar, along with citation and website searches, was conducted in August 2025. English-language publications that provided guidance on evaluating value of DHTs were included. A narrative review approach was employed to synthesize findings and identify key value domains and indicators. RESULTS: From an initial pool of 1,132 articles, four met the inclusion criteria and formed the basis of the review, supplemented by additional frameworks from relevant organizations. Six core value domains were identified: technical and security aspects, data rights and governance, clinical characteristics, economic characteristics, health inequalities, and user preferences. Five value frameworks from the United Kingdom (UK) National Health Service, Digital Therapeutic Alliance, World Bank, Institute for Clinical and Economic Review-Peterson Health Technology Institute, and UK National Institute for Health and Care Excellence were compared across these domains. A heat map analysis demonstrated variation in the inclusion of indicators, reflecting differing stakeholder priorities and perspectives. LIMITATIONS: Limitations include potential bias, subjectivity, and limited scope inherent in a narrative review, although elements of PRISMA were applied to reduce risks. Non-English frameworks and those specific to certain technologies were excluded. Additionally, frameworks for both DHTs and digital therapeutics were considered together. CONCLUSION: Six core value domains are consistently emphasized across value frameworks. However, variations across value frameworks highlights the influence of stakeholder needs and priorities. Further research is needed to refine these frameworks and support consistent and informed decision-making in DHTs adoption and implementation. Digital health technologies (DHTs) are increasingly used in healthcare, but their value is assessed in variable ways. We reviewed published studies to identify common elements. Six key areas emerged: technical and security aspects, data rights and governance, clinical and economic characteristics, health inequalities, and user preferences. While some areas are consistently included, others vary across frameworks. Our findings suggest that more consistent approaches could support clear decisions about adopting digital health tools.
25 Apr 2026
Read appraisal →BMJ health & care informatics
Barriers associated with the implementation, adoption, scale-up and sustainability of mHealth in Sub-Saharan Africa: a systematic review guided by the NASSS Framework
OBJECTIVES: The objectives of this study were to highlight and evaluate the factors affecting the non-adoption, abandonment of and challenges to scale-up, spread and sustainability of mHealth in Sub-Saharan Africa (SSA) and to assess the appropriateness of the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) Framework in the SSA context. METHODS: A systematic search of published research was conducted using PubMed and Embase research databases from 2012 to 2025. Peer-reviewed studies from SSA countries, evaluating mHealth interventions, were included in this study. Papers were analysed to identify barriers according to the seven domains of the NASSS Framework, which are (1) condition, (2) technology, (3) value proposition, (4) adopters, (5) organisation(s), (6) wider system, (7) embedding and adaptation over time RESULTS: This study identified 409 barriers across 91 papers. 309 barriers were covered by the seven NASSS Framework domains, while 100 were unaddressed by the framework. The main barriers were associated with domain 2, the technical aspects of the technology (27%). Other prominent barriers related to the wider system (18%), the adopter (11%), the organisation (9%) or condition (8%), with the value proposition (1%) and embedding and adaptation over time (1%), least hindering implementation. Challenges which were unaddressed by the framework (24%) included socioeconomic and infrastructural issues, distinct to lower and middle-income countries (LMIC). DISCUSSION: This paper highlights the importance of evaluating rudimentary aspects of mHealth and preparing infrastructure before addressing more complex issues during mHealth implementation in SSA. CONCLUSION: Although the NASSS Framework is applicable to the SSA region, it would benefit from some further development to improve its applicability in LMICs.
25 Apr 2026
Read appraisal →JMIR human factors
Psychometric Properties of Digital Divide Measurement Instruments in Health Contexts: Systematic Review
BACKGROUND: The digital divide constitutes a growing form of social inequality, influencing access to, use of, and benefit from digital health technologies. Accurate assessment of this phenomenon requires psychometrically sound measurement instruments, especially in health care contexts. OBJECTIVE: This systematic review aimed to identify and evaluate the psychometric properties of instruments developed to measure the digital divide, with a particular focus on their application in health-related settings. METHODS: A systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 and COSMIN (Consensus-Based Standards for the Selection of Health Measurement Instruments) guidelines. Literature searches were performed in PubMed, Scopus, Dialnet, and Web of Science for studies published between 2015 and 2025. Inclusion criteria required studies to report a psychometric evaluation of instruments measuring the digital divide. RESULTS: Four studies met the inclusion criteria (ranging from 875 to 1337 participants). The identified scales primarily measured digital skills and capital. All studies reported adequate internal consistency, with Cronbach α coefficients ranging from 0.70 to 0.94. Structural validity was confirmed through factor analysis in all studies. However, data on temporal stability and responsiveness were absent. CONCLUSIONS: Existing instruments show potential for assessing digital inequality in health, but further psychometric validation and cultural adaptation are required. Nursing professionals are encouraged to lead efforts in developing and validating context-sensitive instruments to promote digital health equity.
25 Apr 2026
Read appraisal →JMIR formative research
Global Mapping of Telemedicine Regulation and Ethical Safeguards: Mixed Methods Exploratory Document Analysis
BACKGROUND: Telemedicine has become central to digital health strategies, yet the regulatory environment that shapes ethical safeguards and equitable access remains uneven and incompletely assessed across countries. Legal and normative instruments matter because they define requirements for privacy, consent, accountability, professional readiness, and barrier reduction. OBJECTIVE: This study aimed to map the current global landscape of normative instruments related to telemedicine and identify which ethical and social safeguards are explicitly addressed, with particular attention to equity. METHODS: We conducted a document analysis guided by the READ (ready the materials, extract data, analyze data, and distill findings) framework. From February 2024 to February 2026, we conducted a structured web-based search across all World Health Organization (WHO) member states with no language restrictions, using official government sources, statutory professional regulators, and institutional publication channels. Retrieval combined internal site searches, direct navigation, external search engine queries, and targeted snowball sampling to identify currently in-force instruments. Two researchers independently extracted and coded data using a predefined codebook. We operationalized 10 binary items covering regulatory presence and scope (questions 1 and 2), safeguards for data protection (question 3), consent and disclosure (questions 4 and 5), prior in-person prerequisites (question 6), monitoring (question 7), training requirements (question 8), and equity (questions 9 and 10). We summarized frequencies overall and stratified by WHO region and World Bank income group and conducted a qualitative thematic analysis of included normative instruments. RESULTS: Of the 194 WHO member states, 81 (41.8%) had at least one current normative instrument related to telemedicine in force. Among these, 72.8% (59/81) defined telemedicine or telehealth. Data protection provisions were most common (73/81, 90.1%), followed by mandatory informed consent (n=71, 87.7%) and monitoring mechanisms (n=65, 80.2%). Fewer countries required disclosure of telemedicine limitations (n=36, 44.4%) or mandated telemedicine-specific training (n=26, 32.1%). Prior in-person consultation requirements were uncommon (n=8, 9.9%). Equity-related safeguards were uneven: 51.9% (n=42) referenced justice, equity, or nondiscrimination, whereas 30.9% (n=25) included concrete barrier reduction provisions (eg, digital inclusion or accommodations for people with disabilities and minors). CONCLUSIONS: Telemedicine regulation is becoming more common, but both coverage and safeguarding content remain uneven. While privacy, consent, and monitoring are frequently addressed where regulation exists, disclosure, physician competency, and operational equity measures are less consistently specified. Strengthening telemedicine governance will require translating ethical commitments into enforceable standards that address digital determinants of access and protect groups at risk of exclusion.
25 Apr 2026
Read appraisal →PloS one
Evaluation of large language models in medical examinations: A scoping review protocol
INTRODUCTION: Standardized medical examinations, used to assess trainee clinical competencies, provide a rigorous means to verify LLM accuracy and reliability in medical contexts. Although current evaluations use these exams to test LLMs' clinical reasoning, significant performance variations occur across different clinical scenarios. Existing methods struggle to adapt to evolving research needs. This study synthesizes prior research on LLMs in medical exams, highlighting current limitations and proposing future research directions. METHODS AND ANALYSIS: The formulation of the protocol was guided by the standards set forth in the JBI Manual for Evidence Synthesis. Following the establishment of precise inclusion/exclusion criteria and search strategies, we will execute systematic searches in the PubMed and Web of Science Core Collection databases. The method encompasses literature review, data extraction, analytical frameworks, and process mapping. By employing this method, researchers maintain methodological rigor during the entire research process. ETHICS AND DISSEMINATION: This protocol describes a method for performing a scoping review. The investigation focuses on the organized synthesis and examination of previously published research. It does not include human/animal experimentation or sensitive data collection. Ethical approval is not required for this literature-based study.
24 Apr 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
Computer Vision Models for Detecting Large Cigars on Social Media
INTRODUCTION: Large cigars, which include premium cigars and large manufactured cigars, are widely promoted on social media and digital media, highlighting the critical need for scalable methods to monitor such content. To fill this need, we aimed to train and test an automated computer vision model to identify large cigars. METHODS: In 2022, we obtained 876 large cigar images and 1526 non-cigar images (ie, cigarettes, e-cigarettes, and pens) from Reddit, a global social media platform organized into subcommunities (subreddits) focused on specific topic areas, discussions, and communities. We used the You Only Look Once Version 7 (YOLOv7) computer vision model to train large cigar detection and evaluated its performance using recall, precision, and F1 scores (ie, harmonic mean of recall and precision scores). The data were split into 75% (594 cigar images and 250 non-cigar images) for training and 25% (282 cigar images) for validating and testing. Non-cigar images (n = 1276) were used to evaluate for false positives. RESULTS: Our model achieved a recall of 0.98, a precision of 0.99, and an F1 score of 0.98, demonstrating high accuracy in correctly identifying large cigars while minimizing false positives. It successfully distinguished large cigars from similarly shaped objects with an accuracy of 97%. CONCLUSIONS: Our computer vision model accurately distinguished large cigars from non-cigar images, demonstrating its potential as an automated, scalable tool for monitoring this understudied tobacco product on social media and other digital media. IMPLICATIONS: This computer vision model offers a novel and scalable approach to tobacco monitoring by automatically detecting large cigars in social media content. Its application may enhance the efficiency of product identification and facilitate research on use patterns, user perceptions, and emerging marketing strategies, thereby generating evidence to inform regulatory decision-making and policies aimed at preventing initiation.
24 Apr 2026
Read appraisal →Annals of medicine
Artificial intelligence and wearable sensors in sports injury risk prediction: current status and future perspectives.
BACKGROUND: Artificial intelligence (AI) and wearable sensors are increasingly reshaping sports injury risk prediction by enabling continuous, individualized, and data-driven assessment. DISCUSSION: This review summarizes recent advances in wearable technologies - including inertial measurement units (IMUs), electromyography (EMG), physiological monitors, and flexible electronics - and their use in capturing biomechanical, physiological, and psycho-physiological indicators relevant to injury risk. Deep learning (DL) models, particularly those capable of temporal and multimodal fusion, have shown promise in predicting injuries such as anterior cruciate ligament (ACL) tears, muscle fatigue, and stress fractures. Through representative applications and mechanism-informed analysis, we highlight the growing role of interpretable AI, real-time feedback, and behavior-integrated predictive systems. CONCLUSIONS: Key challenges remain, including data heterogeneity, limited generalizability, compliance issues, and privacy concerns. Future directions point toward personalized modeling, explainable systems, federated learning, and digital twin frameworks. Together, these advancements mark a shift toward proactive, intelligent injury prevention across athletic and clinical settings.
24 Apr 2026
Read appraisal →Journal of medical Internet research
Exploring Patient Perspectives on the Use of Artificial Intelligence to Inform Joint Decision-Making for Patients With Multiple Conditions in Primary Care in the United Kingdom: Qualitative Study
BACKGROUND: Multimorbidity, living with 2 or more long-term health conditions, is increasing globally and now affects over one-quarter of adults in England. People with multiple long-term conditions (MLTC) face complex health and treatment challenges, often experiencing fragmented care within systems oriented toward single-disease management. Artificial intelligence (AI) has the potential to support clinicians and patients by analyzing complex health data, optimizing treatment strategies, and predicting disease trajectories. OBJECTIVE: The OPTIMAL (Optimizing Therapies, Disease Trajectories, and AI-Assisted Clinical Management for Patients Living with Complex Multimorbidity) project aims to develop AI-enabled tools to support shared decision-making in primary care. This study explored how patients with MLTC perceive the use of AI to inform joint decision-making in primary care. METHODS: Semistructured interviews were conducted via telephone or video call with 29 adults living with MLTC between July and November 2023. Participants were recruited through general practitioner practices via the Clinical Practice Research Datalink and community-based organizations across the West Midlands. Interviews were transcribed verbatim and analyzed thematically using an inductive approach. Members of a patient advisory group were involved in developing study materials, refining the interview guide, and reviewing emerging findings to ensure relevance and authenticity. RESULTS: Participants identified potential benefits of AI in enhancing consultation efficiency and accuracy, improving access to information for patients and clinicians, promoting early detection of health changes, and reducing health care inequalities. However, concerns were raised about the loss of human interaction, data privacy and security, transparency of algorithms, and the potential for bias and inequity in AI systems. Trust and acceptance varied by age and familiarity with technology. Some participants expressed uncertainty about what AI entails and how it could be used in primary care. CONCLUSIONS: Patients with MLTC viewed AI-assisted decision-making in primary care with cautious optimism. While many recognized potential benefits for coordination and personalization of care, others expressed reservations about privacy, fairness, and the risk of diminished human connection.
24 Apr 2026
Read appraisal →JMIR formative research
Access to Technology-Mediated Community Mental Health Care Among Low-Socioeconomic Status Consumers With Serious Mental Illness: Qualitative Study
BACKGROUND: Access to mental health care is critical for the effective management of serious mental illness (SMI), but consumers with low socioeconomic status (SES) have lower rates of service usage and worse retention in care. Digital technologies are often lauded as a way to bridge access gaps; however, little is known about how technology-mediated care may influence care access among low-SES consumers and how consumers use technology in care access. OBJECTIVE: This study aimed to examine the applicability of Levesque et al's access framework to technology-mediated care for SMI and analyze how low-SES consumers use technology to facilitate care access. Furthermore, the study assesses whether and how technologies are involved in care access at multiple points within the process of accessing care. METHODS: This study used 2 qualitative methods: ethnographic observations at a mental health treatment court and interviews with low-SES consumers with SMI using community mental health care (n=14) and key informant interviews with health and service providers working with this population (n=14). Observations occurred from July 2022 through September 2023, and interviews occurred between January 2022 and May 2024. Data analysis involved both inductive and deductive coding approaches. Data from both the interviews and observations were analyzed in NVivo and further triangulated through analytic memos. RESULTS: Levesque et al's framework required several extensions to accommodate technology-mediated care related to SMI for low-SES consumers: (1) a cyclical rather than linear trajectory; (2) simultaneous care acquisition from multiple health and service providers; (3) staying in care long-term; (4) identification of both one-time and ongoing health needs; and (5) an emergency pathway for entering care. Consumers often faced challenges related to the varied digital requirements of each provider and a dearth of integrative, patient-facing tools like portals. Within this context, some consumers use mobile apps, communication, and telehealth technologies across various care access stages. Consumers used technology by figuring out how to navigate technology-mediated care, especially by leaning on others, such as case managers, for support. These others provided consumers with temporary technologies, showed them how to use technologies, and accompanied them through the process of using technology for accessing care. CONCLUSIONS: This study highlights that accessing care is iterative and ongoing, involving multiple forms of co-occurring service provision. A theoretical contribution of this work is its extension of Levesque et al's care access framework to better reflect technology-mediated care for SMI among low-SES consumers. This work also underscores ongoing challenges for accessing technology-mediated care and the importance of human support in addressing access difficulties. Clinical implications include incorporating digital readiness assessments and providing comprehensive guidance on how consumers can effectively use technologies for care. Future work should investigate how technology-mediated care can make care access easier rather than harder.
24 Apr 2026
Read appraisal →JMIR formative research
Multimodal Sentiment and Emotion Analysis Framework for Personalized Health Coaching Messages: Proof-of-Concept Study
BACKGROUND: Text generation approaches in health care communication have evolved along 2 major paths. The first path involves generative adversarial networks, progressing from basic architectures to specialized variants like Text-to-Text Generative Adversarial Network (TT-GAN) and Time and Frequency Domain-Based Generative Adversarial Network (TF-GAN), which address challenges in discrete text generation through techniques such as Gumbel-Softmax and reinforcement learning. The second path emerges from transformer-based architectures, particularly Generative Pretrained Transformer-2 (GPT-2), which uses extensive pretraining and self-attention mechanisms to generate contextually appropriate text. GPT-2's transformer architecture enhances persuasive health communication by generating personalized messages using various strategies like task support, dialogue support, and social support for effective health interventions. OBJECTIVE: This study aimed to use GPT-2 as a generative method to construct persuasive text in a dataset and compare the performance of sentiment analysis and emotion detection analysis. METHODS: We combined sentiment analysis tools (VADER [Valence Aware Dictionary and Sentiment Reasoner] and TextBlob) with emotion detection methods (Text2Emotion and NRCLex [National Research Council Lexicon]) to analyze health coaching messages across different persuasive types: reminder, reward, suggestion, and praise. RESULTS: TextBlob and VADER achieved accuracies of 57% and 69%, respectively, while RoBERTa (robustly optimized BERT approach)-sentiment outperformed them with an accuracy of 88%. Emotion detection showed a high prevalence of "joy" and "happy" labels (93.69% positive skew). While transformers excel in accuracy, lexicon-based models like VADER offer a better performance-efficiency balance for real-time health communication systems. For emotion detection, all categories showed perfect accuracy (1.0), while trust showed mixed results, with precision, recall, and F1-score values ranging from 0.81 to 0.96. The emotion detection analysis revealed varying success rates across different emotions, with some categories, such as anger and neutral, showing reasonable performance and others, such as trust, showing mixed performance. CONCLUSIONS: This research contributes to understanding the emotional dynamics of persuasive health communication and highlights both the capabilities and limitations of current natural language processing tools in analyzing health-related persuasive messaging. This proof-of-concept study using synthetically generated data establishes a methodological framework for multimodal sentiment and emotion analysis. The findings require validation with real-world health coaching messages before clinical deployment.
24 Apr 2026
Read appraisal →Renal failure
Decoding the renal-cochlear axis: explainable machine learning and phenotype clustering reveal high-risk hearing loss subtypes in CKD
This study develops a dual-level machine learning framework for risk stratification and phenotyping of hearing loss (HL) in patients with chronic kidney disease (CKD) using data from the National Health and Nutrition Examination Survey (NHANES). From a cohort of 3,402 CKD patients, feature selection via univariate and multivariate logistic regression identified key predictors, which were used to construct predictive models with nine machine learning algorithms. The eXtreme Gradient Boosting (XGBoost) model demonstrated superior performance, with mean area under the curve (AUC) values of 0.984 (training), 0.984 (validation), and 0.939 (testing). SHapley Additive exPlanations (SHAP) interpretation identified age as the predominant risk determinant. Subsequent Gaussian mixture modeling (GMM) clustered patients into two distinct subtypes: a low-risk subgroup (n = 1,075) with a 1.58% HL prevalence and a high-risk subgroup (n = 2,316) characterized by older age, elevated blood urea nitrogen and bicarbonate levels, and a 48.2% HL prevalence. A classifier trained on these subtypes achieved discrimination (AUC = 0.99974). A clinically a web-based tool was also developed based on the six most influential features. The findings establish a dual-level predictive framework integrating explainable ML and unsupervised clustering for HL risk assessment in CKD. This approach provides a robust strategy for the precision screening of high-risk subpopulations and supports the integration of hearing assessments into routine CKD.
24 Apr 2026
Read appraisal →European journal of preventive cardiology
i-CARE: a smartphone-based intervention to enhance cardiac rehabilitation in coronary artery disease-a randomized controlled trial
AIMS: The global shortage of healthcare professionals creates challenges in managing chronic diseases. This study evaluated a remotely delivered, theory-based self-care support intervention, 'Internet-based CArdiac Rehabilitation Enhancement (i-CARE)', delivered through a smartphone application to enhance self-management in patients with coronary artery disease (CAD). METHODS AND RESULTS: In this assessor-blinded, two-arm randomized controlled trial conducted at two regional hospitals in Hong Kong, community-dwelling adults with angiographically confirmed CAD were randomly assigned (1:1) to standard care or the i-CARE intervention. i-CARE included personalized risk assessment, skill-building, and app-based support to improve self-care behaviours. The primary outcome was change in CAD self-care behaviours; secondary outcomes were self-reported health status, health-related quality of life (HRQoL), physiological, and biomarkers. Of 294 participants randomized (n = 147 per group), the i-CARE group showed significant greater improvements in self-care maintenance [β = 4.055, 95% confidence interval (CI) 1.420-6.691, P = 0.003] and self-care confidence (β = 5.609, 95% CI 1.089-10.129, P = 0.015) at 3 months, sustained at 6 months. For secondary outcomes, the intervention also resulted in significant reductions in systolic (β = -3.725, 95% CI -7.322 to -0.128, P = 0.042) and diastolic (β = -2.153, 95% CI -4.228 to -0.079, P = 0.042) blood pressure and improvements in HRQoL at 3 months (β = 0.208, 95% CI 0.073-0.343, P = 0.003), with persistent benefits for diastolic blood pressure and HRQoL sustained at 6 months. No significant differences were observed for cardiac-specific health status, blood glucose, lipid profile, or waist-to-height ratio. CONCLUSION: Remotely delivered i-CARE intervention effectively promote self-care among CAD patients and may help address critical healthcare workforce shortages, while also conferring secondary benefits in blood pressure and HRQoL. Cardiac rehabilitation is a comprehensive management approach with proven clinical benefits to delay disease progression. However, the participation rate in cardiac rehabilitation remains low due to common barriers such as time constraints and need for transportation to centres. Mobile health applications offer innovative solutions to overcome these barriers providing multidimensional support to meet the complex needs of coronary artery disease (CAD) patients and enhance their self-management capabilities. This study aimed to evaluate the effects of an internet-based Cardiac Rehabilitation Enhancement (i-CARE) intervention among community-dwelling adults with angiographically diagnosed CAD to enhance self-care behaviours. The findings showed promising potential of digital health interventions to improve self-care behaviours, blood pressure control, and health-related quality of life in CAD patients.
24 Apr 2026
Read appraisal →American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists
Feasibility and acceptability of incorporating telemedicine into pharmacy services in the rheumatology clinics of the School of Medicine of the University of Puerto Rico
PURPOSE: Incorporating telemedicine into pharmacist services can promote medication adherence, reduce polypharmacy, and prevent adverse drug events as part of identification of drug-related problems. Our primary aim was to measure the acceptability to patients of telemedicine services using a standardized patient satisfaction survey and compare it to that for face-to-face services. Our secondary aim was to assess the feasibility of the telemedicine modality for pharmaceutical care interventions by measuring, quantifying, and categorizing drug-related problems identified during the service. METHODS: The study followed a prospective crossover design from October 2021 to September 2022. Adult patients (21 years or older) with rheumatic diseases evaluated at a university-based outpatient clinic were eligible to be enrolled. After the intervention, patients completed a survey regarding satisfaction and perception of their appointment. Descriptive statistics were used for patient demographic characteristics. Patient satisfaction with the modalities was compared using a t test. RESULTS: In this study, 296 patients were enrolled with a mean (SD) age of 50.4 (12.5) years. Participating patients were utilizing a mean (SD) of 10.2 (4.7) medications. No significant differences (P < 0.05) were observed for any of the survey questions when comparing responses for the face-to-face and telemedicine encounters. In general, patients strongly agreed that they were satisfied with the services they received and perceived the services as unique and valuable in both types of encounters. CONCLUSION: Overall satisfaction was very high, and no differences were found in the perception of or satisfaction with services between the telemedicine and face-to-face modalities.
24 Apr 2026
Read appraisal →PloS one
LHW-Net: An ensemble-based machine learning framework for brain tumor classification
The classification of brain tumors is an unsolved problem associated with heterogeneity of tumors and fluctuations in imaging conditions. In this work, the investigation introduces a powerful novel framework, named LHW-Net that combines handcrafted features called local binary patterns (LBP), histogram of oriented gradients (HOG), and wavelet transform (WT). Within the LHW-Net framework, the extracted features are utilized in different machine learning classifiers, such as K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Classifier (SVC). The results of the individual classifiers are further combined using probabilistic score fusion approach to improve classification performance. The effectiveness and robustness of the proposed work are validated by the achieved experimental results on commonly accepted benchmark datasets.
24 Apr 2026
Read appraisal →PloS one
Artificial intelligence for monitoring hand hygiene compliance in healthcare settings: A scoping review
BACKGROUND: Hand hygiene is a fundamental measure for preventing healthcare-associated infections, yet traditional monitoring methods are significantly limited by the Hawthorne effect, high resource demands, and an inability to assess procedural quality. Artificial intelligence (AI) technology has emerged as a transformative, automated, and objective approach to address these long-standing challenges. OBJECTIVE: This scoping review sought to systematically map the existing evidence, technical pathways, performance metrics, and implementation challenges of AI for monitoring hand hygiene compliance in healthcare settings. METHODS: Following the Joanna Briggs Institute (JBI) methodological framework and PRISMA-ScR guidelines, we searched five major databases (PubMed, Scopus, Embase, Web of Science, and IEEE Xplore) for articles published between January 2000 and September 2025, supplemented by grey literature searching and backward citation tracking. Two reviewers independently screened records, assessed full-text reports for eligibility, and extracted data, which were synthesized using descriptive statistics and thematic analysis. RESULTS: Of 800 records identified through database and supplementary searches, 45 studies (2007-2025) were included. The primary technical pathways identified were computer vision (53.3%), wearable sensors (24.4%), Internet of Things-integrated systems (13.3%), and radar/radio frequency-based systems (8.9%). While computer vision achieved high accuracy (95%) in setting-specific ICU models, performance dropped to 56% in generalizable models. Wearable systems demonstrated portability but showed 5%-10% lower specificity than vision-based approaches. Most evidence is derived from small-scale technical validations, with a significant lack of formal fairness analysis and evaluation of clinical workflows or cost-effectiveness. CONCLUSION: AI-based hand hygiene monitoring shows promise for supporting more objective and scalable hand hygiene surveillance in healthcare settings. However, the field remains at a largely pre-translational stage. Future research should shift from technical feasibility toward implementation science, focusing on establishing standardized motion databases, evaluating ethical governance (e.g., privacy and automation bias), and conducting pragmatic trials to demonstrate sustained clinical benefit and organizational sustainability.
24 Apr 2026
Read appraisal →European journal of preventive cardiology
Comparison of patient characteristics and health outcomes between self-selected centre-based cardiac rehabilitation and hybrid cardiac telerehabilitation: a prospective cohort study
AIMS: Data on self-selected modes of delivery of cardiac rehabilitation (CR) are limited. This study compared centre-based CR (cbCR) with hybrid cardiac telerehabilitation (hCTR) in terms of patient characteristics, change in physical and mental functioning, and achievement of guideline-directed treatment targets. METHODS AND RESULTS: From May 2022 to December 2023, consecutive patients with cardiovascular diseases were enrolled at a tertiary centre into a 3 month cbCR or hCTR programme based on shared decision-making. Changes in exercise capacity, anxiety, depression, and health-related quality of life (hrQoL) scores from admission to the discharge visit were compared between CR modalities. The achievement of two-step blood pressure and LDL-cholesterol (LDL-C) goals, as well as the HbA1c goal in patients with diabetes at the discharge visit, was compared between CR modalities. Out of 1292 patients screened, 406 (21% females, age 60.4 ± 12.7 years) were eligible and completed the study. Of those, 72% chose cbCR and 28% chose hCTR. Patients in hCTR were 3 years younger and exhibited higher baseline peak VO2 (91 vs. 80% of predicted), better hrQoL, and lower depression and anxiety scores. No significant differences were found in improvements in physical or mental functioning or in meeting blood pressure and HbA1c targets between the two groups. A smaller proportion of hCTR participants achieved the LDL-C Step I target (56 vs. 69% in cbCR). CONCLUSION: Overall, hCTR attracted slightly younger patients with better baseline health, but both modalities showed similar effects on most health outcomes. Centre-based cardiac rehabilitation was associated with tighter lipid control, which could be related to more intense counselling or patient preference. Outpatient cardiovascular rehabilitation was equally effective when performed with thrice weekly supervised exercise training sessions or partly performed at home while monitored with fitness trackers and a biosensor vest. Patients who self-select digitally monitored home training were slightly younger, fitter, and had better quality of life with lower levels of anxiety and depression. Blood lipid control, weight loss, and smoking cessation therapies need to be enhanced by tighter control and more intensive medical therapy particularly in patients choosing to perform part of the exercise sessions at home.
23 Apr 2026
Read appraisal →PloS one
A hybrid CNN-XLSTM-GRU deep learning model with autoencoder-based feature selection for hypothyroidism diagnosis.
Hypothyroidism, caused by reduced thyroid-hormone production, is often difficult to diagnose because its symptoms are non-specific and overlap with other disorders. We developed FusionNet-CXG, a hybrid deep-learning model that couples CNN, eXtended LSTM, and GRU components for hypothyroidism prediction. Experiments were performed on a public dataset (3,772 records; 30 features). Class imbalance was mitigated using SMOTE-NC, and performance was estimated using 10 repetitions of 5-fold stratified cross-validation, yielding 50 fold-level evaluations to obtain stable results. Across folds, FusionNet-CXG achieved 0.9394 mean accuracy, F1 = 0.906, and AUC-ROC = 0.94, and it exceeded CNN+LSTM and CNN + BiLSTM baselines under the same protocol. The results indicate that combining local feature extraction with recurrent modelling is effective for this task. To support interpretability, SHAP was used to quantify feature influence on the predictions. Future work will focus on external validation using more diverse real-world cohorts and incorporating temporal clinical data to improve clinical relevance.
23 Apr 2026
Read appraisal →PloS one
Controlled comparative study of YOLOv8-Pose, YOLOv11-Pose, and Detectron2 for vertebrae detection and keypoint estimation
Accurate vertebrae detection with precise keypoint localization is essential for medical image analysis and clinical applications, such as spinal alignment assessment. This study presents a controlled, task-driven comparison of four pose-based deep learning models for vertebrae detection and keypoint estimation: YOLOv8n-Pose, YOLOv11n-Pose, and Detectron2 (Keypoint R-CNN) with ResNet-50 and ResNet-101 backbones. A single-class vertebrae dataset with bounding box annotations and four anatomically meaningful keypoints per vertebra was evaluated under consistent training settings. Performance was assessed in terms of keypoint localization accuracy, detection precision, inference speed, and vertebrae-specific prediction behavior, including detection completeness and duplicate detections, which are critical for clinical usability. Results show that YOLOv11n-Pose provides the best balance between keypoint accuracy and inference efficiency, while YOLOv8n-Pose achieves the fastest inference with competitive performance. Detectron2-based models exhibit lower keypoint accuracy, slower inference, and frequent duplicate predictions that obscure correct anatomical landmarks. Additional experiments indicate that larger YOLO variants (particularly YOLOv8l-Pose) improve accuracy, albeit at increased computational cost. Overall, the results demonstrate that model capacity or architectural recency alone does not guarantee superior performance in vertebrae keypoint detection, highlighting the importance of anatomy-aware model selection for spine imaging applications.
23 Apr 2026
Read appraisal →PloS one
Understanding older adults' perception, acceptance, and adoption of smart home technologies
Smart home technologies have increased in popularity and affordability in recent years, however, there is limited research on their adoption specifically among older adults. This study aims to uncover incentives and barriers to the adoption of smart home components, given the impact of COVID-19 on older adults' quality of life (QoL). For this purpose, online audio-recorded interviews were conducted with 21 participants aged between 65 and 90 years from the UK, Malta, and Pakistan. Participants were shown various smart home technology images and videos and asked for their perception, existing knowledge, and current use. The corresponding audio recordings were transcribed and thematically analysed. Findings revealed that older adults experienced a decline in QoL during COVID-19, accompanied by an increased reliance on digital technologies. While participants recognised the potential of smart home devices to enhance independence and well-being, their knowledge of available options was limited. Key barriers included affordability, privacy and trust concerns, compatibility and integration issues, and environmental and social considerations. Cross-cultural differences emerged, with European participants reporting higher familiarity than those in Pakistan, particularly rural areas where availability was limited. These results highlight the need for designers and policymakers to improve affordability, usability, and cultural adaptability in order to support older adults' acceptance and meaningful adoption of smart home technologies.
23 Apr 2026
Read appraisal →PloS one
DSCANet: Integrating dual encoder and spatial cross-attention for polyp segmentation
Colonoscopy is a crucial clinical procedure for detecting colorectal polyps, which are strongly associated with the development of colon cancer. This endoscopic technique plays a vital role in both cancer prevention and early diagnosis. Accurate and efficient polyp segmentation is critical for enhancing the diagnostic reliability and clinical utility of colonoscopy. However, achieving precise segmentation presents significant challenges, primarily due to the diversity of polyps in their size and shape, coupled with poorly defined boundaries between polyps and surrounding tissues. To address these challenges, we propose a novel segmentation network, named DSCANet, which is a dual-branch encoder-structured network designed to efficiently fuse body and edge features for high-precision medical image segmentation. DSCANet integrates four key modules: a dual-branch encoder, a spatial cross-attention (SCA) module, a bipolar fusion (BF) module, and a flexible axis-attention (FAA) module. The dual-branch encoder consists of separate body and edge encoders, which extract respective features independently. The SCA module bridges the semantic gap between the two encoders' features. The BF module fuses the shallowest and deepest features, while the FAA module assists the decoder in extracting semantic information from high-level features. DSCANet achieved superior performance on multiple colorectal polyp segmentation datasets. The code is available at https://github.com/Shantdst/DSCANet.
23 Apr 2026
Read appraisal →PloS one
Successful student learning outcomes in Moroccan higher education: Causal configurations of pedagogical, motivational, and ICT conditions using fuzzy-set Qualitative Comparative Analysis (fsQCA)
As the demand for better educational quality and improved student performance grows, institutions face increasing challenges in making their teaching methods more effective to ensure successful learning outcomes. Most practical recommendations tend to focus on isolated effects, but learning success usually results from interconnected factors that work together. Drawing on constructivist Learning Theory, this study examines pedagogical and motivational factors that can positively impact learning results in higher education within Moroccan universities. The main goal is to identify and evaluate the nonlinear individual effects and the interactive causal configurations involving multiple conditions, such as teacher motivation, pedagogical leadership, self-efficacy, instructional innovation, ICT use, and student motivation, on learning outcomes. Empirical data were gathered through a questionnaire completed by 349 Moroccan university students, using measurement scales for key variables. The analysis employed the fuzzy-set Qualitative Comparative Analysis (fsQCA) method, which helps identify different combinations and causal pathways that lead to high academic achievement. Findings suggest that excellent learning outcomes are not caused by a single factor but by the interaction of several interdependent conditions. Different "recipes" can produce similar strong results. Certain combinations, especially those with strong teacher motivation, effective leadership, and strategic ICT use, proved to be reliable configurations, showing that technology is most impactful when integrated into supportive pedagogical and motivational environments. These results can help redefine and evaluate integrated educational policies, considering the complex interactions among individual, pedagogical, and technological factors. This approach promotes more effective and equitable learning environments through coherent bundles of mutually reinforcing interventions, instead of isolated efforts. Ultimately, this research enables educators and policymakers to develop more targeted strategies, utilize resources more efficiently, and improve overall educational effectiveness.
23 Apr 2026
Read appraisal →PloS one
An AI-based chatbot to support health-related social needs among pediatric primary care population: Protocol for a pilot randomized controlled trial
Unmet health-related social needs (HRSNs) are major drivers of poor health outcomes in early childhood. Children with unmet HRSNs are at greater risk for developmental delays, caregiver stress, and increased healthcare utilization, yet current screening approaches in pediatric primary care are resource-intensive and inconsistently implemented. AI-powered chatbots (conversational agents or virtual assistants) may offer a private, secure, scalable, and cost-effective alternative for identifying unmet needs and connecting families to services. This protocol describes a pilot randomized controlled trial designed to evaluate the feasibility, acceptability, and usability of DAPHNE, an AI-driven chatbot developed to facilitate the identification of unmet HRSNs and provide personalized community resource referrals. One hundred caregivers of children under two years of age will be recruited from Nationwide Children's Hospital pediatric primary care clinics and randomized to either the standard care (control) group or DAPHNE+ Standard care (intervention) group (n = 50 each arm). Caregivers will complete surveys at baseline, 1 month, 3 months, and 6 months post-intervention (depending on the measure). For the intervention group, participants will receive weekly chatbot prompts and on-demand access throughout the 6-month study period. Primary outcomes include study feasibility (recruitment, retention, and survey completion across both arms), acceptability (caregiver-reported ratings in both arms and intervention-specific ratings), and usability of the DAPHNE chatbot (System Usability Scale among intervention participants). Secondary outcomes includes caregiver-reported outcome measures (caregiver stress, self-efficacy, satisfaction with resource access, quality of life), and electronic health record-derived measures (including documentation of HRSN screening and referrals, adherence to well-child visits, missed appointments, emergency department utilization, and estimated healthcare costs). In addition, ten primary care providers will also participate to assess workflow integration and report on current HRSN practices. Mixed-methods analyses will integrate survey data, chatbot engagement metrics, and qualitative interviews to refine both the intervention and the study protocol. The results of this study will inform the design of a future multi-site trial to evaluate the efficacy and implementation of DAPHNE for addressing HRSNs in pediatric primary care. Trial registration: NCT07168382.
23 Apr 2026
Read appraisal →JMIR research protocols
WhatsApp-Led mHealth Intervention for Hypertension Management Among Adults Aged 40 to 59 Years in Kerala, India: Protocol for a Mixed Methods Study
BACKGROUND: Medication adherence and lifestyle modification remain key in the management of hypertension. Leveraging mobile technologies to improve patient adherence has been found to be effective in chronic disease management. Given the high mobile penetration and the opportunities it provides for encouraging lifestyle modification, an app such as WhatsApp, which is a free-to-use and real-time messaging platform, could facilitate patient-clinician interactions. We present a protocol for a WhatsApp-led mobile health (mHealth) intervention to improve adherence to medication and healthy lifestyles among adults aged 40 to 59 years in Kerala, India. OBJECTIVE: The study aims to assess the impact of the WhatsApp-led mHealth strategy on blood pressure measures and adherence to medication and lifestyle modification. The study also focuses on exploring participants' views on feasibility, facilitators, and challenges, as well as their intention to use this WhatsApp-led mHealth strategy. METHODS: The study will follow a multiphase mixed methods (sequential explanatory) design with an initial quantitative phase (quasi-experimental study design) followed by a qualitative phase (phenomenological approach). Multistage cluster sampling will be adopted. Using a survey checklist and blood pressure measurements, community-dwelling adults aged 40 to 59 years will be screened for hypertension. Approximately 120 participants with hypertension will be assigned each to the intervention and control arms. All the participants will be advised to follow routine hypertension care from health care providers. Additionally, participants in the intervention arm will receive regular health messages and reminders over WhatsApp. A baseline survey will be conducted at enrollment, and a follow-up survey will be conducted after 3 months at the end of the intervention. The qualitative phase will involve follow-up interviews. Participants will be selected through quota sampling from those with the lowest and highest adherence to medication and lifestyle modification after receiving the intervention. Statistical analysis for survey data and thematic analysis for qualitative data will be conducted. RESULTS: The study received ethics approval (IHEC/CUK/2024/04) from the Institutional Human Ethics Committee of the Central University of Kerala. All 240 participants were recruited by July 2025. The study completed data collection in August 2025 and is expected to finish data analysis by April 2026. The study results are expected to be submitted for publication in July 2026. CONCLUSIONS: The study will be a potential strategy to address nonadherence to treatment and lifestyle regimens, which is a critical bottleneck to controlling hypertension and preventing complications. The study could be proof for a WhatsApp-led mHealth strategy that can be integrated into hypertension management at the community level, especially for improving hypertension awareness and reminders for behavior change.
22 Apr 2026
Read appraisal →JMIR infodemiology
Cross-Sectional Evaluation of Medical Disinformation Safeguards in Consumer-Facing Large Language Model Platforms
This cross-sectional evaluation of six consumer-facing large language model platforms found significant heterogeneity in safeguard performance against the generation of health disinformation, with Claude and ChatGPT demonstrating complete resistance across all prompt types, while Copilot, Meta AI, Grok, and Gemini exhibited substantial vulnerabilities.
22 Apr 2026
Read appraisal →JMIR human factors
Perceptions of Health Care Professionals on the Integration and Use of AI in Clinical Cancer Care: Interview Study
BACKGROUND: Artificial intelligence (AI) is increasingly recognized for its potential to transform cancer care. However, much of the existing evidence of its efficacy comes from controlled settings. There remains a need to complement this knowledge with insights into how AI tools are perceived and used in real-world clinical settings, as well as how their use impacts clinical practice. OBJECTIVE: This study aimed to explore key factors influencing clinicians' acceptance of AI tools and examine how AI adoption and use impact clinical workflows in cancer care. METHODS: We used purposive sampling for recruiting oncology-related health care professionals and collected data using web-based semistructured interviews to gather their perceptions. Data were thematically analyzed and interpreted through the lenses of sociotechnical systems theory and the Unified Theory of Acceptance and Use of Technology. RESULTS: Participants largely accept and perceive AI tools as beneficial to clinical practice. Unified Theory of Acceptance and Use of Technology constructs were reflected in our data as determinants of intention to adopt AI tools. Trust appears as an influential factor in shaping attitudes toward AI tools. Acceptance is found to both precede AI tool use and to grow following successful integration. The use of AI tools is perceived to yield operational benefits, such as reduced workload and time savings, and clinical benefits, such as increased diagnostic reliability and reduced patient recall. Minimal disruption to clinical workflows following integration of AI tools was reported for some cancer screening applications and organ-at-risk segmentation, whereas greater disruption was anticipated for 3D cancer screening. Although accountability and lack of explainability are highlighted in literature as barriers to AI adoption, participants do not view these as significant obstacles in image-based diagnostic contexts. Additionally, negative impacts, such as overreliance on AI and reduced critical review of AI results, arise in association with the use of AI tools. CONCLUSIONS: Participants perceive AI tools to deliver benefits to clinical cancer care. However, their adoption relies on their alignment with clinical needs and seamless integration into clinical workflows. To encourage clinician acceptance, the identified concerns must be addressed. Future work should focus on training programs, co-design with clinicians, and exploration of mitigation strategies for emerging adverse effects, such as automation bias and potential skill erosion.
22 Apr 2026
Read appraisal →Journal of neurology
Predicting disease progression in multiple sclerosis with clinically accessible information and technology
BACKGROUND: Predicting disease progression at the individual level is essential for personalized medicine. We previously developed machine-learning tools to estimate 5-year progression risk in people with multiple sclerosis (PwMS). Such models should account for disease-modifying therapy (DMT) and objective outcome definitions. METHODS: In a retrospective multicenter case-control study, we evaluated adults with relapsing-remitting multiple sclerosis (RRMS) at baseline. Using machine-learning, we developed two complementary tools for individualized 5-year risk estimation: DAAE-M, optimized for transparency, software-neutral use, and mitigation of indication bias, and ELIE, optimized for dynamic landmark-based modeling, complex treatment histories, and mitigation of immortal-time bias. Disease progression was defined using both a clinical outcome (RRMS-to-progressive MS) and an objective outcome (late-stage confirmed progression independent of relapse activity). RESULTS: Among 34,510 people with RRMS (72.6% female, mean age = 37.1, mean disease duration = 5.8), 9.8% and 21% met clinical and objective progression criteria, respectively, over five years. Both models demonstrated good calibration across risk-groups (Brier scores 0.06-0.16). DAAE-M provided patient-level risk estimates with monotonic risk escalation across risk-groups for clinical (3.1%/11.2%/22.6%/33.0%) and objective (8.4%/14.5%/23.3%/38.8%) progression. For DAAE-M, high-efficacy DMT was associated with approximately half the progression risk compared with low-efficacy DMT (risk-ratios: 0.42-0.59; p < 0.01). ELIE also showed good calibration across risk deciles with increasing incidence for both clinical (0.3%/1.2%/1.7%/2.5%/3.7%/5.5%/7.2%/10.2%/14.3%/21.5%) and objective (0.9%/1.6%/2.5%/4.0%/5.8%/7.8%/10.2%/15.3%/20.9%/32.5%) outcomes. CONCLUSION: We developed two well-calibrated machine-learning-based tools for individualized 5-year prediction of clinically- and objectively-defined MS progression, each with distinct strengths in usability, bias handling, and treatment modeling. These findings support future tool use in personalized risk stratification and secondary prevention.
21 Apr 2026
Read appraisal →Scientific reports
End-to-end pipeline for automated heart failure diagnosis with clinical notes using SNOMED-CT
Diagnosis of heart failure is complex but crucial for patient outcomes and often hindered by the untapped potential of unstructured clinical notes. We introduce a novel end-to-end pipeline for heart failure diagnosis, leveraging electronic health records (EHR) and German clinical notes from 846 patients. Our pipeline synthesizes abbreviation disambiguation, translation of German clinical notes to English, medical entity linking to SNOMED-CT, and subsequent classification. The classification was performed using a Support Vector Machine (SVM) and compared against a fine-tuned medBERT.de neural baseline. We reduced the reliance on training data with zero-shot learning to address limitations with abbreviation disambiguation and entity linking approaches. Validation against benchmark datasets and cardiologists demonstrates high accuracy for real clinical use. Abbreviation disambiguation achieved an accuracy of up to 96.1%. Entity linking achieved competitive performance compared to state-of-the-art approaches on selected evaluation datasets. The SVM classification approach utilizing SNOMED-CT concepts and EHR data achieved an F1-score of 65.3%, on par with the medBERT.de neural baseline using clinical notes and EHR data. Despite challenges regarding limited language-specific resources and reference dataset availability for SNOMED-CT annotations in German, our pipeline demonstrates high potential for real-world clinical use and clinical decision support grounded in the standardized SNOMED-CT ontology.
21 Apr 2026
Read appraisal →PloS one
SCGAN-generated skin-tone-balanced extension of the HAM10000 dataset for fairer dermatological AI
This dataset presents a synthetic collection of skin lesion images generated using a tone-conditioned Generative Adversarial Network (GAN) to model skin tone variation in dermatological datasets, motivated by the limited representation of darker skin tones in commonly used benchmarks. The dataset includes 10,000 images, at a resolution of 128×128 pixels, equally divided between medium and dark skin tones, with lesion class labels assigned to follow the class distribution of the original HAM10000 dataset. To quantitatively characterize the generated data, we report Fréchet Inception Distance (FID) scores and a baseline classification experiment comparing models trained on HAM10000 alone versus HAM10000 augmented with the proposed synthetic images. This work provides a controlled synthetic extension intended to support fairness-aware experimentation, data augmentation, and downstream evaluation in dermatological machine learning.
20 Apr 2026
Read appraisal →Journal of health organization and management
Implementing telemedicine for cancer care in European healthcare organizations: lessons from the eCAN Joint Action
PURPOSE: Telemedicine represents a promising innovation to complement conventional cancer care in Europe to improve treatment quality and patient outcomes. Funded between September 2022 and December 2024, the Joint Action "Strengthening eHealth including telemedicine and remote monitoring for health care systems for CANcer prevention and care" (eCAN JA) aimed to bring the benefits of telemedicine to cancer patients across the European Union (EU) Member States, with 35 partner organizations from 16 countries and explored its feasibility through multicenter clinical trials. This study sheds light on key factors to implement telemedicine services for cancer care in European healthcare organizations (HCOs) and offers a forward-looking perspective by conducting a foresight exercise as part of the Sustainability Work Package (WP4) of the eCAN JA. DESIGN/METHODOLOGY/APPROACH: Foresight is an umbrella term for innovative strategic planning, policy formulation and solution design methods that empower decision-makers. For our multi-country foresight study, we followed three sequential steps to gain qualitative and quantitative findings on the facilitators of telemedicine services for cancer patients in clinical settings, comprised of (1) literature review; (2) surveys to HCOs in different EU Member States and (3) a foresight workshop with survey respondents. FINDINGS: Telemedicine implementation in HCOs requires equipping healthcare professionals with the necessary skills, ensuring system compatibility and addressing resource constraints for hybrid care models for success. Future policies in the EU should focus on establishing telemedicine training for healthcare professionals and support interoperability as well as user-friendliness of telemedicine services in HCOs. Policies should also address clinical workload challenges, enable harmonized telemedicine protocols across Europe, provide incentives for implementation and invest in digital infrastructure in HCOs for a sustainable telemedicine adoption. ORIGINALITY/VALUE: To the best of our knowledge, this is the first study to conduct a foresight analysis on the feasibility of telemedicine for cancer care across different HCOs in EU Member States.
20 Apr 2026
Read appraisal →PloS one
An explainable multi-head attention network for healthcare IoT threat detection based on the MedDefender-MHAN framework
The rapid proliferation of Internet of Medical Things (IoMT) devices in healthcare environments has created critical cybersecurity vulnerabilities that demand both accurate and interpretable intrusion detection solutions. Existing deep learning-based intrusion detection systems (IDS) achieve high detection accuracy but lack inherent explainability, limiting their clinical adoption under regulatory frameworks such as GDPR and FDA guidelines. This paper presents MedDefender-MHAN, an explainable multi-head attention network specifically designed for healthcare IoT threat detection. The proposed framework introduces a novel dual-stream architecture that combines convolutional neural networks for local spatial feature extraction with transformer-based encoders for long-range temporal dependency modeling. Unlike existing approaches that apply explainability as a post-hoc process, MedDefender-MHAN embeds interpretability directly into the multi-head attention mechanism, enabling real-time gradient-weighted explanation generation without external XAI pipelines. Evaluated on CICIDS2017 and TON_IoT benchmark datasets, MedDefender-MHAN achieves detection accuracies of 99.47% and 98.92% respectively, with sub-3ms inference latency and a throughput of 435 samples per second. Explainability evaluation demonstrates 94.6% alignment with expert-annotated attack signatures and 91.9% temporal accuracy, outperforming post-hoc methods such as SHAP and Integrated Gradients. These results confirm that MedDefender-MHAN provides a clinically viable, regulatory-compliant security solution for real-world healthcare IoT infrastructure. The proposed framework addresses the dual imperatives of methodological transparency and clinical impact, directly responding to the growing need for trustworthy AI-driven security solutions in regulated healthcare IoMT environments.
20 Apr 2026
Read appraisal →Journal of medical Internet research
Long-Term Outcomes, Moderators, and Predictors in Online Mindfulness-Based Cognitive Therapy for People With Cancer: Secondary Analysis of a Randomized Controlled Trial
BACKGROUND: A 3-armed randomized controlled trial (RCT) demonstrated that 2 formats of online mindfulness-based cognitive therapy (eMBCT)-group-blended and individual-unguided-effectively reduced psychological distress and improved positive health outcomes in people with cancer and survivors, when compared to care as usual, up to 3 months posttreatment. However, the long-term effectiveness and possible predictors and moderators of treatment outcomes remain unknown. OBJECTIVE: This study examined the long-term effects (up to 9 months posttreatment) of group-blended and individual-unguided eMBCTs on psychological distress and other mental health outcomes in people with cancer. Additionally, it explored possible predictors and moderators of treatment effects across the 2 delivery formats. METHODS: The study population consisted of people with cancer who were initially randomized to group-blended or individual-unguided eMBCT within a 3-arm RCT, augmented by those who completed the waitlist condition and were subsequently randomly allocated to one of the eMBCT formats. Both groups were assessed over a 9-month follow-up period. Outcomes completed at baseline, posttreatment, and 3-, 6-, and 9-month follow-up assessments included psychological distress (primary), fear of cancer recurrence, rumination, fatigue, mindfulness skills, decentering, self-compassion, and well-being. Linear mixed-effects models examined changes over time, while linear mixed-effects models and binary logistic regression analyzed potential predictors and moderators of psychological distress and dropout. RESULTS: Of the 186 participants enrolled in the 3-arm RCT, 161 participants were randomly assigned to either group-blended or individual-unguided eMBCTs after adding those initially assigned to the waiting condition (group-blended: n=71; individual-unguided: n=90). The majority of participants were female (n=129, 80%), diagnosed with breast cancer (n=78, 48%), and were undergoing or had completed treatment with curative intent (n=124, 77%). The mean age was 52.8 (SD 11.4) years. Both eMBCT formats resulted in significant reductions in psychological distress, fear of cancer recurrence, rumination, and fatigue, alongside improvements in mindfulness skills, decentering, and self-compassion, up to 9 months posttreatment. Higher baseline rumination, as well as lower mindfulness skills and self-compassion at baseline, predicted larger reductions in psychological distress from baseline to the 9-month follow-up period. Additionally, highly distressed participants in the group-blended eMBCT arm were more prone to dropout than those with lower distress scores, whereas psychological distress was not associated with dropout in the individual-unguided format. No other significant moderators were identified. CONCLUSIONS: Group-blended and individual-unguided eMBCTs effectively reduced psychological distress and improved well-being among people with cancer and survivors, with greater benefits for those with fewer psychological resources. However, individuals experiencing higher levels of distress were more likely to discontinue group-blended eMBCT. These findings highlight the importance of considering individual preferences and pragmatic factors in treatment decisions. Larger, fully powered RCTs are needed to confirm these results and provide more definitive guidance on treatment format selection.
20 Apr 2026
Read appraisal →Journal of medical Internet research
Facility-Level Associations Between Use of a Digital Health Platform for Voluntary Counseling and Testing and HIV Testing Outcomes in the Urban Primary Health Care Centers of Guangzhou, China: Cross-Sectional Study
BACKGROUND: WellTest, a digital health platform designed to facilitate HIV voluntary counseling and testing (VCT) services, has been widely implemented in Guangzhou, China. However, the extent of its use by primary health care centers (PHCs) and the facility-level associations between WellTest use and HIV testing outcomes remain unclear. OBJECTIVE: This study aimed to assess the use of WellTest at the facility level across PHCs in urban Guangzhou, China, and to explore the associations between WellTest use and HIV testing outcomes. METHODS: We obtained data on VCT services visits from the National HIV/AIDS Prevention and Control Information System and the WellTest platform between January 1 and December 31, 2022. Two facility-level HIV testing outcomes, the HIV testing volume and HIV positivity rate, were calculated for each PHC. A structured questionnaire collected data on the PHCs' characteristics. Multilevel negative binomial regression and zero-inflated gamma models were used to examine associations between WellTest use and 2 HIV testing outcomes at the facility level. RESULTS: A total of 81 PHCs across 5 urban districts in Guangzhou were included. WellTest was used for 7997 active consultations and 7969 HIV tests that resulted in 157 newly diagnosed HIV cases, for an overall positivity rate of 2.0%, during 2022. The median share of clients booking via WellTest was 71% (IQR 34%-98%), and 81% (66/81) of PHCs offered online slots on all service days without mandates. PHCs that actively confirmed appointments had significantly lower HIV testing volumes compared with those that took no actions after clients scheduled appointments (incidence rate ratio 0.75, 95% CI 0.58-0.97; P=.03) but exhibited higher HIV positivity rates (β=.39, 95% CI 0.02-0.76; P=.04). Additionally, PHCs where ≥50% of clients used WellTest to schedule appointments showed higher HIV positivity rates than those with lower uptake (β=1.11; 95% CI 0.82-1.40; P<.001). CONCLUSIONS: WellTest showed substantial use among VCT clients and providers in urban Guangzhou PHCs, with greater use of digital appointments and counselor follow-up linked to increased HIV positivity rates. Strategies to optimize institutional adoption may help address stigma-related barriers, strengthen engagement in HIV testing, and support HIV case finding in primary health care settings.
20 Apr 2026
Read appraisal →Journal of medical Internet research
The Real-Time Support Role of Augmented Reality Technology in Shared Decision-Making in Neurosurgery Under the SEGUE Framework: Randomized Controlled Trial
BACKGROUND: Preoperative risk communication is essential for shared decision-making (SDM) in neurosurgery; however, conveying complex neuroanatomy and surgical risks using traditional verbal explanations can limit understanding and contribute to dissatisfaction and medicolegal disputes. Augmented reality (AR) may provide patient-specific, interactive 3D visualization to support these conversations. OBJECTIVE: This study evaluates whether AR-assisted preoperative risk communication improves objective understanding and other SDM-related outcomes, compared with communication supported by a conventional physical anatomical model within a standardized SEGUE-informed protocol. METHODS: A prospective, single-center, randomized controlled trial was conducted with 62 neurosurgery communication recipients (patients when capable; otherwise, a legally authorized representative [LAR]). Patients were stratified by planned surgical approach (frontal, parietal, and occipital) and, within each stratum, were randomized into an experimental AR group and a control physical-model group. The primary outcome was postsession objective understanding, assessed by a multiple-choice knowledge questionnaire. Secondary outcomes were subjective understanding, communication satisfaction, pre-to-post anxiety changes, communication duration, and neurosurgeons' communication skills from video recordings. RESULTS: Of the 67 individuals screened, 62 communication recipients were enrolled and completed all assessments (patients, n=30; LARs, n=32; AR, n=32; and control, n=30). Objective understanding was higher with AR than with the physical model in the prespecified pooled comparison (P=.01). Communication satisfaction was also higher with AR (P<.001). There were no clear between-group differences in subjective understanding (P=.41), anxiety changes (ΔState-Trait Anxiety Inventory [ΔSTAI] Y-1, P=.37; ΔSTAI Y-2, P=.84), in-session face-to-face communication duration (excluding any presession AR technical preparation time; P=.73), or SEGUE scores (P=.60). Exploratory stratified analyses suggested larger comprehension gains with AR in the parietal and occipital approach strata. CONCLUSIONS: In a standardized preoperative SDM conversation, an integrated visualization-support package combining AR, patient-specific modeling, and interactivity improved neurosurgical decision makers' objective understanding and satisfaction without prolonging in-session, face-to-face communication duration. Larger multicenter trials with longer-term outcomes are warranted to confirm effectiveness and to evaluate implementation and cost considerations. TRIAL REGISTRATION: ISRCTN Registry ISRCTN11483487; http://www.isrctn.com/ISRCTN11483487.
20 Apr 2026
Read appraisal →BMJ open
Implementation of a digital tool for monitoring and managing the emotional and cognitive sequelae of post-intensive care syndrome: ICURA study protocol for a randomised clinical trial
INTRODUCTION: Intensive care units (ICUs) can be a particularly challenging environment for patients who are mentally vulnerable. In addition to the physical stress associated with critical illness and its management, there are physiological and psychosocial factors that can negatively impact a patient's mental health. Approximately half of ICU survivors will experience post-intensive care syndrome, a set of emotional, neuropsychological and physical sequelae that can significantly affect patients' functionality and quality of life, both in the short and long term. The main objective of this study is to investigate whether the ICU Recovery Answers (ICURA) digital follow-up platform can effectively detect emotional and cognitive problems in critically ill patients and its impact on functionality and health-related quality of life during the first year after ICU discharge. METHODS AND ANALYSIS: Multicentre longitudinal prospective study involving ICU adult patients, with randomised follow-up comparing a telemedicine monitoring programme versus usual medical care during 1 year after discharge. A total of 360 participants will be recruited during their ICU admission in two hospitals in Spain. Efficacy outcomes will focus on participants' level of functioning, assessed with the WHO Short Disability Assessment Schedule, and quality of life, measured with the 12-Item Short Form Survey at 1, 6 and 12 months after ICU discharge. Emotional state and cognitive impairment will be evaluated using the Patient Health Questionnaire-9, Generalised Anxiety Disorder-7 and Treatment-Outcome Post-Traumatic Stress Disorder Scale and the Montreal Cognitive Assessment by telephone at 1, 3, 6, 9 and 12 months after ICU discharge. ETHICS AND DISSEMINATION: The implementation of this project is expected to have a direct impact on the satisfaction of ICU survivors, improving their well-being, personalised follow-up and quality of life. Results from this study will be disseminated at various scientific conferences, national and international meetings, and will be shared with the general public and other relevant parties. The dissemination of these results will occur through scientific publications, allowing the medical and scientific community to benefit from the study's findings. Ethics approval from the Ethics Board of Parc Taulí Foundation and Balearic Islands with reference numbers 2022/3031 and IB 5072/22 PI: Protocol version 1 of 18 November 2022. TRIAL REGISTRATION NUMBER: NCT06504979.
20 Apr 2026
Read appraisal →PloS one
AI literacy among basic school teachers in Ghana: A structural equation modelling analysis
Understanding the synergy of Artificial Intelligence (AI) literacy among teachers is imperative for successful integration in educational settings. This study examined basic school teachers' (BSTs) AI literacy based on four key aspects: AI knowledge and understanding (KUAI), AI application (AAI), AI application evaluation (EAIA), and AI ethics (AIE). The study employed a cross-sectional survey design and purposive sampling to collect data from 319 BSTs in Ghana. A variance-based structural equation modelling (VB-SEM) approach was used to explore the relationships between these dimensions and provide insights into their interrelationships. The study found that AAI positively influenced KUAI, EAIA, and AIE. However, AIE did not significantly influence KUAI and EAAI. In addition, KUAI did not influence EAIA. The findings contribute to understanding the current state of AI literacy among BSTs in Ghana and highlight areas for improvement in AI education and professional development programmes.
19 Apr 2026
Read appraisal →JMIR cancer
Digital and Paper-Based Hospital Workflows and 60-Day Mortality in Acute Leukemia: Retrospective Natural Experiment
BACKGROUND: Electronic medical record (EMR) systems have been associated with better clinical workflows and fewer documentation errors. However, evidence regarding their effect on time-sensitive leukemia care in public hospitals in Latin America remains limited. OBJECTIVE: This study aimed to compare 60-day mortality and urgent supportive-care processes between 2 tertiary public hospitals in Mexico with different documentation models (integrated EMR vs traditional physical records [TPR]), under the hypothesis that digital workflows may facilitate more timely treatment. METHODS: We conducted a retrospective natural experiment including 274 patients with newly diagnosed acute leukemia treated between February 2023 and April 2025. Clinical characteristics, treatment intensity, complications during induction, antibiotic administration times, and survival outcomes were abstracted from finalized institutional records that are routinely reviewed at discharge by the institutional medical record committee as part of standard quality-assurance procedures. The primary outcome was 60-day mortality. Secondary outcomes included time to first antibiotic dose after recognition of febrile neutropenia, treatment-related complications, and the number of operational steps required for urgent care processes. A subgroup analysis of 70 patients with complete timing documentation was performed for antibiotic administration. Comparisons between hospitals were performed using univariate tests, Kaplan-Meier 60-day survival curves, and multivariable logistic regression. RESULTS: Of the 274 included patients, 104 (38%) were treated at the hospital using an integrated EMR, and 170 (62%) at the hospital using TPR. Sixty-day mortality was lower in the EMR hospital (6/104, 5.8%) than in the TPR hospital (61/170, 35.9%; P<.001). In the subgroup with complete timing data, the mean time from febrile episode recognition to first antibiotic administration was shorter in the hospital using EMR than in the hospital using TPR (54, SD 18.4 minutes; P<.001). Although the EMR hospital used a higher proportion of high-intensity regimens, patients in that hospital had better 60-day outcomes. In multivariable analysis, hospital type remained independently associated with 60-day mortality (odds ratio 0.11, 95% CI 0.05-0.26; P<.001), whereas kidney injury and hepatotoxicity were associated with worse outcomes. CONCLUSIONS: In this natural experiment, the hospital using an integrated EMR had a lower 60-day mortality and shorter time to antibiotic administration than the hospital using TPR. These findings are hypothesis-generating and suggest that digital workflows may contribute to more timely urgent supportive care, but they should be interpreted with caution, given the retrospective 2-center design and the potential for residual confounding.
19 Apr 2026
Read appraisal →Scientific reports
A hybrid dual-stream CNN framework with dynamic data augmentation and improved Manta Ray Foraging Optimization for robust glaucoma detection.
A progressive neurological condition, glaucoma is one of the main causes of irreversible blindness in the globe. Early detection is crucial to preventing irreversible vision loss however conventional diagnostic methods are often time-consuming, and heavily reliant on clinical expertise. This study presents an innovative deep learning framework designed to automate glaucoma detection while addressing key challenges such as data imbalance, image quality inconsistencies, and the need for accurate feature extraction. The proposed framework begins with a dedicated preprocessing pipeline that enhances image clarity, and isolates clinically relevant regions of interest. To handle class imbalance, a novel hybrid data augmentation strategy is introduced, combining geometric transformations with adaptive Gaussian noise injection that adjusts intensity according to image characteristics, thereby simulating realistic clinical variability. At its core, the framework leverages a dual-stream CNN architecture that integrates DenseNet121 for structural feature extraction and ResNet50 for texture representation. A lightweight channel-wise attention mechanism is then introduced to selectively emphasize clinically significant channels while suppressing redundant features, thereby balancing efficiency and interpretability. To optimize model performance while reducing computational overhead, an Improved Manta Ray Foraging Optimization (IMRFO) algorithm is employed. IMRFO enhances the standard MRFO with Partial Centroid Opposition-Based Learning (PCOBL) to dynamically fine-tune hyperparameters, including augmentation settings and transfer learning configurations. Experimental validation was conducted on four public benchmark datasets, ACRIMA, Drishti-Gs, ORIGA, and RIM-ONE-DL, demonstrating the framework's superior performance across all evaluation metrics. The model achieved 100.00% accuracy, precision, recall, and AUC on both ACRIMA and Drishti-Gs, with losses of 0.003 and 0.001, respectively. On ORIGA, it reached 99.70% accuracy, 99.80% precision, 99.30% recall, and 99.50% AUC (loss = 0.01). On RIM-ONE-DL, the model scored 99.90% accuracy, 99.70% precision, 99.50% recall, and 99.70% AUC (loss = 0.006). These findings confirm the framework's robustness and clinical applicability for effective glaucoma screening.
19 Apr 2026
Read appraisal →Medical science monitor : international medical journal of experimental and clinical research
Use of Deep Learning Models in the Diagnosis of Proptosis Through Orbital Magnetic Resonance Imaging
BACKGROUND Proptosis is a common manifestation of orbital disease; however, current diagnostic tools, such as the Hertel exophthalmometer and manual radiological measurements, have limited reproducibility and are observer-dependent. More objective, automated approaches are needed. In this single-center retrospective study, orbital magnetic resonance imaging (MRI) examinations from 521 participants (261 with proptosis, 260 controls) were analyzed. Proptosis was defined on MRI using interzygomatic line-based distance criteria. Three-dimensional convolutional neural network models based on DenseNet121, DenseNet169, DenseNet264, and ResNet50 architectures were trained on volumetric orbital MRI data. MATERIAL AND METHODS Data were divided into training, validation, and test sets, and 5-fold cross-validation with early stopping was used to optimize and validate model performance. Diagnostic performance was assessed using accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS DenseNet121 achieved the best overall performance, with mean accuracy of 95.0%, AUC of 0.986, sensitivity of 92.7%, and specificity of 96.9% across 5-fold cross-validation. CONCLUSIONS To the best of our knowledge, prior artificial intelligence studies in orbital imaging have primarily focused on CT-based measurements, radiomics approaches, or thyroid-associated orbitopathy assessment rather than end-to-end 3-dimensional deep learning analysis of orbital MRI volumes. In this context, the present study explores a volumetric MRI-based deep learning framework for automated proptosis detection, emphasizing patient-level classification and explainable model interpretation.
19 Apr 2026
Read appraisal →Journal of medical Internet research
Effectiveness of mHealth Interventions for Improving eHealth Literacy Among Patients With Chronic Diseases: Meta-Analysis and Systematic Review
BACKGROUND: With the widespread use of the internet and mobile devices, eHealth literacy promotion is critical for medical equity. Mobile health (mHealth) serves as a pivotal tool for enhancing eHealth literacy by providing accessible, interactive platforms for health information engagement. However, the evidence regarding the effectiveness of mHealth interventions on eHealth literacy among patients with chronic diseases remains inconclusive. OBJECTIVE: This study aimed to evaluate the effectiveness of mHealth interventions on eHealth literacy among patients with chronic diseases based on randomized controlled trials (RCTs) and summarize supportive evidence from quasi-experimental and qualitative studies. METHODS: A comprehensive search strategy was developed, and 8 electronic databases were systematically searched for studies published up to February 12, 2026. Patients with chronic diseases were included based on predefined inclusion criteria. The Cochrane risk of bias 2 tool for RCTs and the ROBINS-I tool for quasi-experimental studies were used to assess the risk of bias. Given the anticipated substantial heterogeneity among the studies included, we used a random-effects model based on the Hartung-Knapp-Sidik-Jonkman method to pool effect sizes. A narrative and quantitative synthesis of the findings was provided where appropriate. RESULTS: A total of 15 studies were included in this review, including 6 RCTs, 5 quasi-experimental studies, and 4 qualitative studies, involving a total of 2884 patients with chronic diseases. Meta-analyses of RCTs suggested that mHealth interventions could improve eHealth literacy, with a pooled mean effect size of standardized mean difference (SMD)=1. 19 (95% CI 0.14-2.23; P=.03; I²=97.75%; PI [prediction interval]=-2.68 to 5.05). Subgroup analyses by intervention targets showed that interventions on targets with specific disease produced larger mean effects (SMD=1.61; 95% CI 0.16-3.06; PI=-5.40 to 8.63), while interventions targeting the population with general chronic diseases produced smaller effects (SMD=0.36; 95% CI 0-0. 73; PI=-0. 21 to 0. 94). Analysis by intervention duration subgroup showed that the combined effect of studies with intervention duration <3 months was statistically significant (SMD=0.61; 95% CI 0.09-1.13; I²=88.04%; PI=-5.72 to 6.95); while the combined effect of studies with intervention duration ≥3 months was not statistically significant. Taking into account bias and the risk of GRADE (Grading of Recommendations, Assessment, Development, and Evaluation), the certainty of RCT evidence was moderate, and the certainty of quasi-experimental evidence was low. CONCLUSIONS: mHealth interventions could improve eHealth literacy among patients with chronic diseases on average. By using prediction intervals, this study reveals that the effectiveness of mHealth interventions is highly context-dependent and closely linked to implementation factors. Advancing beyond prior work, this study centers on eHealth literacy as a core outcome and integrates multiple types of evidence. Meanwhile, this finding emphasizes the need for evidence-based intervention programs and more rigorous implementation of intervention designs in future research.
19 Apr 2026
Read appraisal →Sleep
Optimizing automated sleep stage scoring of 5-s mini-epochs: a transfer learning study
STUDY OBJECTIVES: Conventional sleep staging relies on 30-s epochs, potentially concealing transient sleep stage intrusion and reducing precision. Building on our previous study of mini-epochs, we investigated whether U-Sleep, an existing automatic deep learning-based sleep staging model with high performance in epochs, could be optimized to similar performance level in 5-s mini-epoch scoring, thereby enabling more detailed sleep characterization. METHODS: We created a dataset of 48 000 human-scored 5-s mini-epochs from 100 polysomnographies. We compared mini-epochs to human-scored epochs before U-Sleep was optimized using transfer learning and evaluated on a test set. Model performance was assessed using F1-scores, confusion matrices, stage distributions and transition rates comparing scorings of the original U-Sleep before, and the optimized U-Sleep after transfer learning to human-scored mini-epochs. RESULTS: Compared to human-scored epochs, human-scored mini-epochs captured significantly more transitions (1.70/min vs. 0.21/min, p < .001), and significantly more wake (8.4 per cent vs. 5.4 per cent), N1 (7.2 per cent vs. 5.4 per cent), and N2 (51.8 per cent vs. 40.9 per cent), less N3 (15.4 per cent vs. 25.2 per cent), and REM sleep (16.7 per cent vs. 23.0 per cent) (all p < .001). Optimizing U-Sleep improved its performance significantly from F1 = 0.74 to F1 = 0.81 (p < .05) and gave increased transition rates in the test set (original U-Sleep: 1.06/min, optimized U-Sleep: 1.34/min, human-scored mini-epochs: 1.70/min). Stage distributions did not differ between optimized U-Sleep's scorings and human-scored mini-epochs. CONCLUSION: After optimization, U-Sleep performance in mini-epochs matched the high performance levels previously reported in both human and automated 30-s epoch scoring. This demonstrates the feasibility of precise, automated high-resolution sleep staging. Future work should include external validation and application to full-night recordings. Statement of Significance Conventional 30-s epochs limit temporal resolution in sleep staging and may conceal transient intrusions of wake or sleep stages. However, no validated methods are available for high-resolution scoring. In this study, we trained and validated the state-of-the-art deep learning model U-Sleep for accurate automatic 5-s mini-epoch scoring using a large dataset of human-scored mini-epochs. The optimized model achieved a high performance, matching levels from previously reported automatic and human epoch scoring. Compared to epoch scoring, mini-epochs captured significantly more stage transitions, supporting their ability to uncover sleep dynamics that are otherwise lost. Our findings show the potential of high-resolution sleep staging for more detailed characterization of sleep architecture and demonstrate the feasibility of precise, automatic mini-epoch scoring.
19 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Artificial intelligence in cancer survivorship: evaluating ChatGPT's performance in rehabilitation-related queries
PURPOSE: This study aimed to evaluate the performance of ChatGPT in responding to patient-centered queries related to cancer rehabilitation. It examined the accuracy, completeness, and overall quality of ChatGPT's responses across major domains of cancer rehabilitation from a physical therapy perspective. METHODS: Fifteen representative patient queries covering eight domains of cancer rehabilitation-including general exercise, fatigue, bone metastasis, neuropathy, balance and falls, breast cancer, women's health, and pediatric cancer-were developed by two licensed physical therapists. Each query was input into ChatGPT-4, and responses were independently evaluated for accuracy, completeness, and global quality based on established clinical practice guidelines and expert consensus. RESULTS: ChatGPT demonstrated a mean accuracy score of 3.93 (SD = 1.10) on a 5-point scale, a mean completeness score of 2.13 (SD = 0.83) on a 3-point scale, and a mean global quality score of 3.60 (SD = 1.12) on a 5-point scale. Responses were generally accurate and clinically relevant but often lacked details regarding exercise dosage, safety precautions, and guideline-concordant recommendations. CONCLUSION: ChatGPT shows potential as an accessible tool for general cancer rehabilitation education but requires refinement to ensure accuracy, contextual relevance, and safety. IMPLICATIONS FOR CANCER SURVIVORS: AI-driven conversational models may improve patient access to rehabilitation information and self-management support when used under professional oversight and guided by evidence-based frameworks.
19 Apr 2026
Read appraisal →Journal of medical Internet research
Comparing Pulmonary Telerehabilitation and Center-Based Pulmonary Rehabilitation for Effectiveness and Adherence in Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-Analysis of Randomized Controlled Trials
BACKGROUND: Pulmonary rehabilitation (PR) is a cornerstone of chronic obstructive pulmonary disease (COPD) management; however, access to traditional center-based PR (CBPR) remains limited. Digital and remote models, collectively termed pulmonary telerehabilitation (Tele-PR), have increasingly been used, but their heterogeneity in technology use, supervision, and interaction mode may influence effectiveness and sustainability. OBJECTIVE: This systematic review and meta-analysis aimed to compare the effectiveness and adherence of Tele-PR with those of CBPR in adults with COPD while systematically evaluating the impacts of supervision intensity and delivery models on key clinical outcomes. METHODS: This review followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 and PRISMA-S (Preferred Reporting Items for Systematic reviews and Meta-Analyses literature search extension) guidelines. PubMed, Embase, the Cochrane Library, and the Web of Science were searched from inception to December 10, 2025, to identify randomized controlled trials comparing Tele-PR or home-based PR (HBPR) with CBPR in adults with COPD. Random effects meta-analyses were conducted using the Hartung-Knapp-Sidik-Jonkman method. Between-study heterogeneity was assessed using τ², I², and 95% prediction intervals. Risk of bias was evaluated with the Cochrane Risk of Bias 2 tool, and certainty of evidence was graded using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach. RESULTS: Seventeen randomized controlled trials involving 1658 participants were included. After intervention, Tele-PR and CBPR showed comparable average effects on exercise capacity by 6-minute walk distance (k=9; n=950, 57.3%; mean difference -5.37 m, 95% CI -15.68 to 4.95; P=.26; τ²=103.97; I²=28.2%; 95% prediction intervals=-32.73 to 22.27). Although pooled effects were not statistically significant, substantial heterogeneity was observed across remote delivery models. Subgroup analyses linked digitally supported, synchronously supervised Tele-PR to less between-study variance across several outcomes, indicating greater consistency in treatment effects across different settings while revealing that low-technology HBPR yielded more variable outcomes, particularly in symptom burden. At long-term follow-up (≥6 mo), between-group differences in functional and symptom outcomes diminished, and short-term gains in exercise capacity did not consistently translate into increased daily physical activity. Certainty of evidence ranged from moderate to very low, mainly downgraded for performance bias, inconsistency across intervention models, and imprecision. CONCLUSIONS: Tele-PR may achieve short-term clinical outcomes comparable to CBPR. Distinct from prior reviews, we stratified remote programs by delivery models and supervision, identifying digitally supported Tele-PR and low-technology HBPR as 2 clinically distinct paradigms with differing consistency of effects. We further propose a structured "supervision gradient" to interpret model-dependent variability in effects across Tele-PR approaches, providing a context-sensitive framework for evidence-informed, model-specific implementation. Future remote rehabilitation should integrate real-time professional supervision and long-term behavioral maintenance to sustain benefits. Tele-PR may be particularly valuable for expanding PR access, while CBPR remains essential for patients requiring close in-person supervision or complex multidisciplinary care.
18 Apr 2026
Read appraisal →JMIR formative research
Development of iGET Living, a Digital Graded Exposure Intervention for Youth With Chronic Pain: Multiphase User-Centered Design and Pilot Study
BACKGROUND: Pediatric chronic pain affects up to one-third of youth and is associated with significant disruptions in social, emotional, and behavioral functioning. Although behavioral treatments are effective, access remains limited due to geographic, financial, and systemic barriers. Digital behavioral health interventions offer a promising solution, but many lack user-centered design, iterative refinement, and implementation-informed development strategies that support usability and scalability. OBJECTIVE: This study aimed to develop and iteratively refine iGET Living, a digital graded exposure intervention for youth with chronic pain, using a combined user-centered and implementation-informed framework, and to evaluate its preliminary acceptability, feasibility, and user-perceived success. METHODS: Guided by the Consolidated Framework for Implementation Research (CFIR) and the mHealth (mobile health) Agile Development and Lifecycle model, intervention development proceeded through 3 phases. Phase 0 translated an evidence-based in-person graded exposure treatment (GET Living) into an initial digital prototype. Phase 1 involved iterative user-centered refinement across 3 cycles of qualitative development sessions with youth with chronic pain (n=15), incorporating think-aloud usability testing, Likert-rated feedback, and rapid qualitative analysis mapped to CFIR constructs to guide real-time modifications to content, design, and functionality. Phase 2 piloted the refined intervention with a new sample of youth (n=38, n=30 completers) recruited from a tertiary pediatric pain clinic to evaluate feasibility, acceptability, treatment credibility and expectancy, and user-perceived functional improvements. Quantitative outcomes were summarized descriptively, and qualitative exit interview data were analyzed using rapid qualitative analysis. RESULTS: Across development cycles, youth feedback informed substantive refinements to the intervention, including reducing text density, incorporating animated educational videos, enhancing interactive features, and improving navigation and layout. These changes resulted in progressive improvements in clarity, satisfaction, and acceptability across prototypes. In the Phase 2 pilot study, participants reported moderate-to-high treatment credibility (mean of 19.71 out of 30) and expectancy (mean of 17.96 out of 30), as well as high satisfaction (mean of 46.12 out of 60). Acceptability ratings across domains of the Theoretical Framework of Acceptability were favorable. Qualitative exit interviews highlighted the interventions' perceived role in helping youth re-engage in valued activities. CONCLUSIONS: Using a combined CFIR and agile development approach, iGET Living emerged as a feasible, acceptable, engaging digital graded exposure intervention for youth with chronic pain. These findings highlight the value of integrating implementation frameworks and participatory design early in digital intervention development and support further evaluation in a preliminary efficacy trial.
18 Apr 2026
Read appraisal →Oncology reports
Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review)
Acute leukemias (ALs) are a diverse group of hematological malignancies characterized by the abnormal proliferation of immature cells. Microscopic observation of cell morphology based on the French‑American‑British classification remains a fundamental diagnostic method for ALs. However, manual screening from bone marrow smear images is often inefficient, laborious and prone to subjective bias, leading to potential misdiagnosis or missed diagnosis. Artificial intelligence (AI), particularly machine learning (ML), has expanded human capabilities in analyzing complex datasets, leading to breakthroughs in multiple fields, including medical research and clinical practice. Increasingly, ML applications are being developed to diagnose hematological diseases by extracting and aggregating morphological characteristics from peripheral blood and bone marrow smears. However, applying ML methods to recognize cell morphology in hematological diseases presents unique challenges compared with other pathology subspecialties. The present review provided an overview of AI and ML applications in ALs diagnosis, focusing on cell segmentation and data mining methods from microscopy images, and highlights their advantages over manual microscopy.
18 Apr 2026
Read appraisal →PloS one
Evidence-based action plan for integrating artificial intelligence in an academic medical centre-a multidisciplinary approach
INTRODUCTION: As the effectiveness of artificial intelligence (AI) in enhancing various facets of healthcare delivery becomes more apparent, it is anticipated that AI will soon find its way into standard clinical practices, even in low and middle-income countries. The objective of this study was to create an action plan for integrating AI into medical education, research, and clinical practices utilizing the SWITCH model of change integrating both rational and emotional aspects. METHODS: This exploratory qualitative study employed reflexive thematic analysis of semi-structured interviews and a co-design workshop, followed by the collaborative development of an action plan. The study was conducted from May 2023 to May 2024, at the Aga Khan University, Karachi, Pakistan. The development of an action plan was informed by interviews, co-design workshop, and discussions with diverse group of academic leaders, healthcare professionals and medical students. All interviews, workshop sessions, and planning meetings were audio recorded, transcribed verbatim, and anonymized. Data management was conducted manually using Microsoft Word and Excel. Findings after thematic analysis of the qualitative interviews and findings from the co-design workshop were gradually, and inductively transformed into the content for the action plan for integrating AI into healthcare. RESULTS: The content analysis of interviews identified following four themes: AI opportunities, apprehensions toward AI-induced changes, pushing change through leadership styles, and importance of AI related capacity building. During the workshop, participants discussed aligning current AI knowledge with future requirements by identifying clear instructions, emotional motivators, and environmental changes required on the path of AI integration. The proposed action plan conceptualized AI integration as multidimensional change process comprising three domains: strategic actions, change pathway enablement and environmental readiness and human motivation operationalized through twenty actionable components. CONCLUSION: The study findings provide a context specific conceptual action plan for healthcare professionals to integrate AI into medical education, clinical service and research, Future work should focus on pilot implementation and empirical validations of the action plan across diverse healthcare settings to assess feasibility, effectiveness and scalability.
18 Apr 2026
Read appraisal →PloS one
Pretraining effective T5 generative models for clinical and biomedical applications
This paper presents a study of the impact of corpus selection and vocabulary design on the performance of T5-based language models in clinical and biomedical domains. We introduce five different T5-EHR models, each pretrained from scratch using different combinations of clinical and biomedical corpora alongside domain-specific vocabularies. We evaluated these models across a variety of clinical and biomedical tasks to quantify the impact of pretraining data and vocabulary tokenization choices on downstream performance. Our findings reveal the importance of aligning both pretraining corpus and vocabulary with the target domain. Models pretrained exclusively on clinical data achieve superior performance on clinical tasks, while adding biomedical data contributes only marginal gains in most cases, with a few exceptions. Similarly, the choice of vocabulary significantly influences model performance, with clinical-specific vocabularies outperforming general biomedical vocabularies in tasks requiring a deeper understanding of clinical language. Also, the T5 generative models perform competitively with state-of-the-art discriminative models on several biomedical benchmarks, demonstrating strong generalization to biomedical domain. Overall, these results emphasize that task-specific selection of corpus and vocabulary is essential for optimizing model performance in clinical and biomedical natural language processing (NLP).
18 Apr 2026
Read appraisal →JMIR aging
Leveraging Digital Health Technologies to Assess Older Adults' Frailty and Nutritional Status: Two Cross-Sectional Studies
BACKGROUND: With the rising prevalence of aging worldwide, there is a necessity for reliable and frequent assessments of older individuals' health status to manage and potentially prevent age-related complications. Digital health technologies (DHTs), such as wearable devices, provide an opportunity to gather objective, continuous, and unobtrusive measurements, enabling effective health management in everyday life. OBJECTIVE: We aimed to evaluate the relationship between digital endpoints and frailty, nutritional status, and patient-reported outcomes (PROs) in older individuals, as well as quantified their compliance and comfort with DHTs. METHODS: In two cross-sectional studies, namely the Geriatric Anorexia Studies 1 and 2, including three in-clinic visits and 2-3 weeks of at-home monitoring, 94 participants (mean age 72.98 years, SD 6.28 years) were stratified based on their frailty status (n=39, 41%, nonfrail; n=45, 48%, prefrail; and n=10, 11%, frail) and nutritional status (n=70, 74%, with normal nutrition and n=24, 26%, at risk for malnutrition), as assessed in a clinical setting using the Fried Frailty Score and the Simplified Nutritional Appetite Questionnaire, respectively. We remotely monitored older adults using different DHTs. At home, participants were monitored with a wrist accelerometer for physical activity, continuous glucose monitoring (CGM) for glucose concentration, a digital body scale for weight and body composition, and a digital nutritional scale for meal tracking. Compliance with devices was assessed via wear time and correct at-home usage, while comfort was evaluated using questionnaires. The association between digital endpoints and frailty/nutritional status was investigated via linear regression, followed by ANOVA, and the relationship between digital endpoints collected at home and PROs was evaluated via Spearman's ρ. Weight and body composition were also assessed during in-clinic visits with a research-grade scale, used to validate the at-home digital body scale measurements, via the intraclass correlation coefficient, Pearson's R, and Bland-Altman plots with mean bias. RESULTS: Physical activity digital endpoints collected at home were significantly different across frailty and nutritional groups and significantly correlated with self-reported appetite, fatigue, and physical function (eg, for mean daily activity in the maximum 60 minutes of activity, ρ=0.28, -0.23, and 0.47, respectively). More than 80% of participants reported that all devices were mostly to very acceptable to wear/use: 95% (88/93) for the body scale, 86% (43/50) for CGM, 80% (40/50) for the nutrition scale, and 91% (84/93) for the wrist accelerometer. Compliance ranged from 60% of monitoring days for the digital nutrition scale to 90% for the wrist accelerometer. CONCLUSIONS: Physical activity digital endpoints show clear prognostic significance for frailty and malnutrition in older individuals and reflect self-reported measures. DHTs can be reliably deployed at home for older individuals to measure their physical activity, weight and body composition, glucose concentration, and meal intakes, thus enabling patient-centric, data-centric clinical trials. TRIAL REGISTRATION: ClinicalTrials.gov NCT04858932; https://clinicaltrials.gov/study/NCT04858932; ClinicalTrials.gov NCT05211973; https://clinicaltrials.gov/study/NCT05211973.
18 Apr 2026
Read appraisal →PloS one
EAC-Agent: A deep learning framework for multimodal emotion-aware conversational agent with contextual response generation
Conversational agents, commonly referred to as chatbots, have become an integral part of various important applications, such as customer support and virtual assistants in enterprise-level solutions. Despite their widespread use, current solutions predominantly rely on a single modality input, primarily text, which limits their ability to fully understand the correct emotions of users and respond accordingly. The increasing demand for multimodal inputs in conversation such as text, audio, and videos, highlights the need for a more comprehensive approach. Existing conversational agents face significant challenges in generating emotionally aware responses, as they lack the ability to effectively handle emotion embeddings, leading to limitations in emotional accuracy and contextual appropriateness. Focusing on these challenges, this research work leads to a novel multimodal approach which incorporates features from text, audio, and visual. The EAC-Agent proposed a sequence-to-sequence model with transformer, along with pre-trained embeddings such as GloVe. The self and cross-modal attention on text, audio and visual is used to generate a more emotionally intelligent response. EAC-Agent is validated through comparison with existing techniques on two benchmark datasets. The obtained results demonstrate superior performance in emotion classification and response generation. The proposed model achieves an accuracy of 76.27% on IEMOCAP and 67.57% on MELD for emotion recognition from multimodal user inputs. In addition, the emotion-aware response generation module shows clear improvements, with perplexity values of 39.01 and 42.30, BLEU scores of 0.31 and 0.30, and ROUGE-L scores of 0.45 and 0.44 on IEMOCAP and MELD, respectively. EAC-Agent demonstrates clear superiority over existing models and holds great promise for applications in customer service, healthcare, and other areas requiring empathetic and contextually appropriate interactions.
18 Apr 2026
Read appraisal →PloS one
A deep reinforcement based echo state network for network intrusion classification
Network intrusion classification referred to the process of monitoring and analyzing network traffic to identify suspicious activities or attacks. In this work, author proposed a novel approach to classify network intrusion by utilizing deep reinforcement learning (DRL), integrating a reservoir computing approach Echo State Network (ESN). A DRL-based approach improved upon traditional deep learning by adapting dynamically to novel/unknown and evolving attack patterns. Unlike static models, DRL continuously learned optimal strategies through interaction with the environment, allowing for better detection of previously unseen threats in real-time. To address the class imbalance often encountered in network intrusion datasets, we evaluated the performance of several advanced data balancing techniques, including Borderline-SMOTE, SMOTE-ENN, ADYSN, and K-means SMOTE. The findings demonstrated that the K-means-based data balancing method outperformed other techniques, resulting in the most robust performance across various metrics. Author conducted multi-dataset validation on benchmark datasets like NF-BoT-IoT, NF-UNSW-NB15, NF-ToN-IoT, NF-ToN-IoT-v2, NF-CSE-CIC-IDS2018 and NF-UNSW-NB15-v3 to ensure robustness across different network flow data. For adaptive modeling testing, author excluded some attack types from training data and included them in testing data (e.g., DoS, Backdoor attacks were excluded from the training data but included in the testing data (see Table 3)). The proposed approach enhanced the accuracy and reliability of intrusion detection, making it a viable solution for securing modern network infrastructures. The source code of this work is available in this Github repository (https://github.com/codewithkhurshed/DRLZDNIDS).
18 Apr 2026
Read appraisal →JMIR human factors
Interactive, Personalized Patient Decision Aid for COVID-19 Vaccination in Canada: User-Centered Design Approach
BACKGROUND: The COVID-19 pandemic highlighted the need for practical digital health tools to support informed decision-making amid rapidly evolving evidence and widespread misinformation. OBJECTIVE: We iteratively developed and refined VaxDA-C19, a bilingual (English and French) web-based patient decision aid designed to support informed decision-making in Canada about COVID-19 vaccination. VaxDA-C19 integrates interactive and personalized features aimed to enhance vaccine confidence, reduce cognitive overload, and respond to diverse informational needs. METHODS: VaxDA-C19 was developed using an iterative, user-centered design approach. Throughout the development process, we involved a citizen panel, health care professionals, user experience designers, and scientific experts to guide refinements. We also conducted usability testing sessions with adults in Canada, using semistructured interviews, comparative testing, and think-aloud protocols with thematic analysis. We ultimately conducted 4 design cycles in total with adults in Canada (users) and expert reviewers (experts). Cycle 1 involved 9 people (9 users), cycle 2 involved 22 people (22 users), cycle 3 involved 9 people (3 users and 6 experts), and cycle 4 involved 9 people (9 experts). RESULTS: In cycle 1, user feedback guided design decisions about how to present quantitative information and technical vaccine descriptions more simply. In cycle 2, while most users (9/11, 82%) favored in-depth explanations of vaccine development, a few raised concerns about content that could be perceived as politically charged. Cycle 3 identified usability improvements, including more explicit navigation controls, simplified medical terminology, and optimized interactive components (avatars and sliders). Expert reviews in cycle 4 refined linguistic consistency, mobile responsiveness, content transparency, and scientific accuracy, emphasizing explicit instructional guidance and bilingual accessibility. CONCLUSIONS: Our iterative process produced a personalized, bilingual digital decision aid to support evidence-informed, values-congruent decisions about COVID-19 vaccination. A randomized controlled trial will further evaluate VaxDA-C19's impact on vaccination intentions, knowledge retention, emotional responses, decisional conflict, and decisional regret. If it proves effective, the patient decision aid may also be used as a platform to support other vaccine decisions, namely, influenza, measles, shingles, pertussis, and potentially other emerging infectious diseases.
18 Apr 2026
Read appraisal →Biomedical physics & engineering express
A multimodal attention fusion-based model for pathological voice detection
Pathological voice detection provides a non-invasive and cost-effective approach for the early screening of laryngeal disorders. However, most existing methods rely on single-modal acoustic features and mainly focus on binary classification, which limits their ability to achieve fine-grained discrimination among multiple pathological voice categories. To address these limitations, this paper proposes a Multimodal Fusion Network (MFNet) with attention-based fusion for pathological voice classification. The proposed framework jointly models time-domain and frequency-domain information within a unified architecture. Specifically, SincNet is employed to extract task-relevant narrowband acoustic features directly from raw speech waveforms, while the Tunable Q-factor Wavelet Transform (TQWT) and SKNet are combined to learn multi-scale spectral representations from Mel-spectrograms. In addition, an Attentional Feature Fusion (AFF) module is introduced to adaptively integrate features from the two branches, thereby enhancing the discriminative capability of pathological voice representations. Comparative experiments on three public pathological voice datasets show that the proposed method outperforms representative baseline models and demonstrates strong robustness and generalization across different datasets. Furthermore, ablation studies further validate the effectiveness of the proposed architectural design. These results indicate that MFNet provides an effective solution for pathological voice classification and has promising potential for computer-aided voice disorder screening.
18 Apr 2026
Read appraisal →BMJ open
Comparison of the effects of eHealth-delivered sedentary breaks versus physical activity promotion interventions on sedentary time: protocol for a systematic review and meta-analysis
INTRODUCTION: Prolonged sedentary behaviour (SB) is an independent risk factor for adverse health outcomes, with current WHO guidelines emphasising both increased physical activity (PA) and reduced sitting time. While electronic health (eHealth) interventions offer scalable solutions, the comparative effectiveness of two dominant strategies: sedentary break interventions (frequent interruptions of sitting) versus PA promotion (structured activity sessions) remains unclear. This systematic review and meta-analysis protocol aims to compare the effectiveness of interventions targeting breaks in SB versus increased PA in reducing sedentary time through meta-analysis of randomised controlled trials (RCTs) comparing either intervention to a control condition, while exploring key moderating factors including participant characteristics, intervention type, duration, eHealth delivery mode, theoretical basis, use of behaviour change techniques (BCTs), PA intensity and SB frequency. METHODS AND ANALYSIS: This protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines. A comprehensive search will be conducted in PubMed, Embase, Web of Science and the Cochrane Library from inception to 31 July 2025, with language restrictions limited to English and Chinese publications. RCTs comparing eHealth-delivered interventions promoting sedentary breaks with those increasing PA in adults will be included. The primary outcome is sedentary time (objectively measured or self-reported), and secondary outcomes include health outcomes such as cardiometabolic markers, fatigue and well-being. Two reviewers (SC and XN) will independently screen studies, extract data and assess risk of bias using validated tools. Meta-analyses will be performed if sufficient homogeneous data are available, comparing changes in sedentary time. Subgroup analyses will explore effects by participant characteristics including gender (male vs female), age groups (<60 vs ≥60 years), intervention type (sedentary breaks vs PA), duration (short-term ≤3 months vs medium-term >3 to <6 months vs long-term ≥6 months), eHealth platform/delivery mode (eg, smartphone apps vs websites vs wearable device prompts vs text messages), theoretical basis (eg, theory-driven interventions vs non-theoretical interventions), use of BCTs (eg, interventions incorporating BCTs vs interventions without BCTs), SB break frequency (eg, every 30 min vs every 60 min) and PA promotion intensity (eg, light vs moderate vs vigorous). A narrative synthesis will be conducted for studies unsuitable for quantitative analysis. ETHICS AND DISSEMINATION: Ethical approval is not required as this study involves secondary analysis of published data. Findings will be disseminated through peer-reviewed publication and conference presentations. PROSPERO REGISTRATION NUMBER: CRD420251042994.
18 Apr 2026
Read appraisal →JMIR formative research
Public Perceptions of AI in Medicine and Implications for Future Medical Education: Cross-Sectional Survey
BACKGROUND: The integration of artificial intelligence (AI) into clinical practice is contingent on public trust. This trust often depends on physician oversight, yet a significant gap exists between the need for AI-competent physicians and the current state of medical education. While the perspectives of students and experts on this gap are known, the views of the US general public remain largely unquantified. OBJECTIVE: This study aimed to assess US public perceptions regarding AI in medicine and the corresponding emergent needs for medical education. We specifically sought to quantify public trust in different diagnostic scenarios, concerns about physician overreliance on AI, support for mandatory AI education, and priorities for the future focus of medical training. METHODS: We conducted a cross-sectional, web-based survey of adults in the United States in November 2025. Participants (N=524) were recruited via SurveyMonkey Audience. We calculated descriptive statistics, frequencies, proportions (percentages), and 95% CIs for all main survey items. RESULTS: A total of 524 participants completed the survey. Most (n=329, 62.8%; 95% CI 58.6%-66.9%) placed the most trust in a physician's diagnosis based on their expertise alone; only 7.8% (n=41; 95% CI 5.5%-10.1%) trusted an AI-first diagnostic model. Trust was highly contingent on training: 93.9% (n=492) of participants rated formal physician training on AI limitations as "essential" or "very important." Widespread concern about physician overreliance on AI was reported, with 81.1% (n=425) being "very concerned" or "extremely concerned." Consequently, 85.1% (n=446) agreed or strongly agreed that training on AI use, ethics, and limitations should be mandatory in medical school. When asked about future educational priorities, 70.2% (n=368; 95% CI 66.3%-74.1%) believed that medical education should focus on human-centered skills (eg, empathy and communication) over clinical skills. CONCLUSIONS: The US public expressed conditional trust in medical AI, strongly preferring physician-led and critically supervised models. These findings reveal a clear public mandate for medical education reform. The public expects future physicians to be mandatorily trained to appraise AI, understand its limitations, and refocus their professional development on the human-centered skills that technology cannot replace.
18 Apr 2026
Read appraisal →PloS one
LungNet: Leveraging state-space models with SE-enhanced skip connections for precise CT-based lung lesion segmentation
Lung cancer, which accounted for 2.48 million new cases and 1.82 million deaths worldwide in 2022, continues to be the most lethal cancer across the globe, underscoring the urgent demand for more advanced diagnostic tools. Although computed tomography (CT) imaging has long been central to lung cancer detection, the heterogeneous and complex characteristics of lung lesions make accurate segmentation particularly challenging. Current deep learning methods face a critical bottleneck: convolutional neural networks (CNNs) often struggle to capture long-range dependencies due to limited receptive fields, while Transformer-based architectures incur prohibitive computational costs when processing high-resolution CT volumes. Furthermore, standard skip connections in traditional UNet models frequently introduce redundant noise, leading to the dilution of subtle lesion features. To address these specific technical gaps, we introduce a novel deep learning framework that integrates Mamba state-space models with an improved UNet architecture. In this design, to mitigate feature redundancy, Squeeze-and-Excitation networks are embedded into skip connections, while auxiliary losses are introduced to address the degradation of shallow features and capture fine-grained lesion features across varied morphologies. Such a framework not only accommodates the intricate differences in lesion size, shape, and spatial distribution but also achieves a balance between global context modeling and linear computational efficiency. By uniting the local feature extraction strengths of convolutional layers with the long-range dependency modeling power of state-space models, our approach achieves more precise delineation of lung lesion boundaries. Extensive experiments conducted on multiple datasets provide compelling evidence of the method's effectiveness: it attains state-of-the-art segmentation accuracy and demonstrates significant promise for enhancing early detection, ongoing disease monitoring, and treatment planning in lung cancer patients. This advancement delivers a robust solution for the inherently complex task of lung lesion segmentation. Moreover, because lung cancer treatment is costly and insurance coverage plays a decisive role in distributing expenses, the study's outcomes also carry considerable implications for the insurance sector.
17 Apr 2026
Read appraisal →JMIR formative research
Artificial Intelligence Design for Race-Based Prostate Cancer Stage Classification With Multilayer Perceptron: Feature Selection Optimization Approach
BACKGROUND: Prostate cancer progression exhibits significant variability influenced by biological and racial factors. DNA methylation profiling has shown potential in early cancer detection, but its integration with machine learning across racially diverse populations remains limited. OBJECTIVE: This study aimed to develop a prostate cancer stage classifier for the majority White cohort using DNA methylation data and a multilayer perceptron (MLP) model in order to classify prostate cancer stages into early (stages I-II) and late (stages III-IV) stages and assess its performance when applied to other racial groups to highlight the need for race-specific models. METHODS: Methylation and phenotype data from the TCGA-PRAD (The Cancer Genome Atlas Prostate Adenocarcinoma) dataset were processed using differentially methylated position (DMP) analysis to identify CpG sites correlated with cancer stages. These features were further refined through recursive feature elimination (RFE) and used to train MLP models. Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were used to interpret the model and identify key DNA methylation features contributing to model predictions. RESULTS: The best-performing model achieved 95% accuracy and up to 99% area under the curve on the majority race (White) training data using 90 selected features. However, performance declined sharply in racial minority groups, revealing the effects of sample imbalance and race-specific methylation patterns. Feature importance examination indicated strong patterns within certain CpG sites driving model predictions. CONCLUSIONS: We propose a race-aware MLP model for prostate cancer stage classification using DNA methylation data, which has been optimized through DMP and RFE-based feature selection. SHAP and LIME confirmed the predictive relevance of selected CpG sites, supporting model transparency. The results highlight high performance within the White cohort but reveal poor generalization to racial minority groups, emphasizing the importance of race-specific modeling strategies.
17 Apr 2026
Read appraisal →Journal of medical Internet research
Artificial Intelligence for Identifying Patient-Reported Outcome and Experience Measures in Oncology: Retrospective Cross-Sectional Study Using ClinicalTrials.gov
BACKGROUND: Traditional health care systems have evolved to increasingly recognize patients' perspectives as key to improving the quality of care, especially in oncology. Hence, patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) can enhance patient-health care provider communication while facilitating individualized care. This tailored approach improves patient outcomes and highlights the need for research methods to account for variability in patient experiences across diverse sociodemographic and clinical backgrounds while proposing artificial intelligence (AI) to automate and accelerate the identification of PROMs and PREMs. OBJECTIVE: This study aimed to estimate the proportion of clinical studies including PROMs or PREMs, using either a traditional expert-based identification method or an AI-enriched approach. METHODS: In a retrospective cross-sectional study using the ClinicalTrial.gov database, we focused on oncology studies between 2012 and 2022. Two methods were used to identify PROMs and PREMs: (1) a traditional expert-based method, where an algorithm identified PROMs and PREMs from a list of 346 oncology-specific PROMs and PREMs (extracted from the PROQOLID database, Mapi Research Trust) and/or 11 PROM- and PREM-specific terms, and (2) an AI-enriched method using a bidirectional encoder representations from transformers model, trained on 2399 outcomes labeled by experts. To evaluate algorithm performance, results were compared with expert decisions. Studies were classified by PROM/PREM use, analyzed using logistic regression to identify drivers, and a named entity recognition model identified frequently used measures. RESULTS: A total of 24,491 studies were included. According to the traditional expert-based algorithm, 7549 (31%) studies used at least one PROM or PREM, as compared to 8029 (33%) studies identified by the AI-enriched algorithm, increasing from 2012 to 2022 (P<.001). With 90% (95% CI 88%-92%) accuracy, the AI-enriched algorithm outperformed (P<.001) the expert-based algorithm (84%, 95% CI 82%-86%) in identifying PROMs and PREMs. Breast and digestive cancers accounted for nearly 50% of all oncology studies using PROMs and PREMs, with the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 being the most frequently used. As expected, trials in phases 2 to 4 more frequently included PROMs and PREMs than preclinical or early phase 1 studies (odds ratio [OR] 1.8, 95% CI 1.1-2.8 for phase 2; OR 3.6, 95% CI 2.3-5.8 for phase 3; and OR 2.6, 95% CI 1.6-4.4 for phase 4). In observational studies, cross-sectional and prospective studies incorporated more PROMs and PREMs than retrospective studies (OR 4.6, 95% CI 3.3-6.4 and OR 3.2, 95% CI 2.5-4.1, respectively). CONCLUSIONS: Our study shows that an AI-enriched algorithm outperforms traditional expert methods in identifying PROMs and PREMs in oncology. Combining expert-labeled data with AI enables scalable, automated trial monitoring, supports efficient research, informs stakeholders, enhances patient-centered decisions, and can be extended to other diseases and databases.
17 Apr 2026
Read appraisal →JMIR cardio
Large Language Models in Cardiology: Systematic Review
BACKGROUND: Large language models (LLMs) are increasingly used in health care, but their role in cardiology has not yet been systematically evaluated. OBJECTIVE: This review aimed to assess the applications, performance, and limitations of LLMs across diverse cardiology tasks, including chronic and progressive conditions, acute events, education, and diagnostic testing. METHODS: A systematic search was conducted in PubMed and Scopus for studies published up to April 14, 2024, using keywords related to LLMs and cardiology. Studies evaluating LLM outputs in cardiology-related tasks were included. Data were extracted across 5 predefined domains and the risk of bias was assessed using an adapted QUADAS-2 tool (developed by Whiting et al at the University of Bristol). The review protocol was registered in PROSPERO (CRD42024556397). RESULTS: A total of 33 studies contributed quantitative outcome data to a descriptive synthesis. Across chronic conditions, ChatGPT-3.5 (OpenAI) answered 91% (43/47) heart failure questions accurately, although readability often required college-level comprehension. In acute scenarios, Bing Chat omitted key myocardial infarction first aid steps in 25% (5/20) to 45% (9/20) of cases, while cardiac arrest information was rated highly (mean 4.3/5, SD 0.7) but written above recommended reading levels. In physician education tasks, ChatGPT-4 (OpenAI) demonstrated higher accuracy than ChatGPT-3.5, improving from 38% (33/88) to 66% (58/88). In patient education studies, ChatGPT-4 provided scientifically adequate explanations (5.0-6.0/7) comparable to hospital materials but at higher reading levels (11th vs 7th grade). In diagnostic testing, ChatGPT-4 interpreted 91% (36/40) electrocardiogram vignettes correctly, significantly better than emergency physicians (31/40, 77%; P< .001), but showed lower performance in echocardiography. CONCLUSIONS: LLMs show meaningful potential in cardiology, especially for education and electrocardiogram interpretation, but performance varies across clinical tasks. Limitations in emergency guidance and readability, as well as small in silico study designs, highlight the need for multimodal models and prospective validation.
17 Apr 2026
Read appraisal →Sleep
Mamba-based deep learning approach for sleep staging on a wireless multimodal wearable system without electroencephalography
STUDY OBJECTIVES: We investigate a Mamba-based deep learning approach for sleep staging on signals from ANNE One (Sibel Health, Chicago, IL), a non-intrusive dual-module wireless wearable system measuring chest electrocardiography, triaxial accelerometry, chest temperature, and finger photoplethysmography and finger temperature. METHODS: We obtained wearable sensor recordings from 357 adults undergoing concurrent polysomnography at a tertiary care sleep lab. Each polysomnography recording was manually scored, and these annotations served as ground truth labels for training and evaluation of our models. Polysomnography and wearable sensor data were automatically aligned using their electrocardiography channels with manual confirmation by visual inspection. We trained a Mamba-based recurrent neural network architecture on these recordings. Ensembling of model variants with similar architectures was performed. RESULTS: After ensembling, the model attains a 3-class (wake, non-rapid eye movement sleep, rapid eye movement sleep) balanced accuracy of 84.02 per cent, F1 score of 84.23 per cent, Cohen's κ of 72.89 per cent, and a Matthews correlation coefficient (MCC) score of 73.00 per cent; a 4-class (wake, light NREM [N1/N2], deep NREM [N3], REM) balanced accuracy of 75.30 per cent, F1 score of 74.10 per cent, Cohen's κ of 61.51 per cent, and MCC score of 61.95 per cent; a 5-class (wake, N1, N2, N3, REM) balanced accuracy of 65.11 per cent, F1 score of 66.15 per cent, Cohen's κ of 53.23 per cent, MCC score of 54.38 per cent. CONCLUSIONS: Our Mamba-based deep learning model can successfully infer major sleep stages from the ANNE One, a wearable system without electroencephalography, and can be applied to data from adults attending a tertiary care sleep clinic.
17 Apr 2026
Read appraisal →Sleep
Augmenting management of obstructive sleep apnea with consumer wearable devices to increase use of positive airway pressure therapy: a pilot randomized trial
STUDY OBJECTIVES: This pilot randomized trial measured the effects of a consumer-grade wearable-augmented program, which provides nightly pulse oximetry data to patients in a weekly report, on positive airway pressure (PAP) use in adults with obstructive sleep apnea (OSA) recruited from an outpatient sleep center. METHODS: This single-blind, parallel, two-group waitlist-controlled, single-site randomized trial recruited individuals with OSA with insufficient PAP use from a Department of Veterans Affairs medical center and randomized them to immediate participation in the consumer wearable-augmented self-monitoring program versus waitlist. The primary outcome was the change in modem-measured PAP use (mean hours or minutes per night over a 7-day period) from baseline (T1) to T3 assessment, which was 35 days following randomization (7 days post-intervention for the immediate group and prior to intervention for the waitlist group). RESULTS: We randomized 50 participants (mean age 47.9 years [SD 11.4]; 88% male). The change in PAP use was +1.33 hours (+79.8 minutes) for the immediate group and -0.17 hours (-10.2 minutes) for the waitlist control group, with a between-group difference of 1.50 hours (90 minutes) (p = .0097). The change in the number of days PAP was used for 4 hours or more over a 7-day period was +1.32 days for the immediate and 0.12 days for the waitlist control group, with a between-group difference of 1.20 (p = .0401). CONCLUSIONS: This pilot trial demonstrated that the consumer-grade wearable-augmented program is a promising intervention for increasing PAP use. A future full-scale trial that follows participants for a longer period is needed. Statement of Significance This pilot trial tested a novel use of the pulse oximetry feature available on some consumer-grade wearable devices to increase use of positive airway pressure (PAP) devices among adults with obstructive sleep apnea (OSA) and insufficient PAP use. The widespread and growing use of consumer-grade wearable devices offers an opportunity to leverage the features on these devices to tackle a common, problematic issue among individuals with OSA-insufficient use of PAP therapy. The randomized controlled design provides support for future research involving these devices.
17 Apr 2026
Read appraisal →Sleep
Multimodal cardiovascular risk profiling using self-supervised learning of polysomnography
STUDY OBJECTIVES: Polysomnography (PSG) provides a comprehensive assessment of brain, cardiac, and respiratory activity during sleep. While it is widely used for diagnosing sleep disorders, its potential to assess future health risks has not been fully explored. This study aimed to develop and evaluate an interpretable framework to identify physiological patterns in PSG data linked to cardiovascular disease (CVD) outcomes, without relying on manual annotations (e.g. sleep stages). METHODS: We developed a self-supervised deep learning model that extracts meaningful patterns from multi-modal signals (electroencephalography [EEG], electrocardiography [ECG], and respiratory signals [Resp]). The model was trained on data from 4398 participants. Projection scores were derived by contrasting embeddings from individuals with and without CVD outcomes. External validation was conducted in an independent cohort with 1093 participants. The source code is available on https://github.com/miraclehetech/sleep-ssl. RESULTS: The projection scores revealed distinct and clinically meaningful patterns across modalities. ECG-derived features were predictive of both prevalent and incident cardiac conditions, particularly CVD mortality. EEG-derived features were predictive of incident hypertension and CVD mortality. Resp added complementary predictive value. Combining projection scores with the Framingham Risk Score consistently improved prediction, with area under the curve values ranging from 0.607 to 0.965 in the Sleep Heart Health Study test cohort and 0.710 to 0.807 across four of five outcomes in the external validation cohort, demonstrating robust cross-cohort generalizability. CONCLUSIONS: Our findings demonstrate that the proposed framework can generate individualized CVD risk scores directly from PSG data. The resulting projection scores have the potential to be integrated into clinical practice, enhancing risk assessment and supporting personalized care. Statement of Significance This study is among the first to apply a self-supervised framework for cardiovascular risk profiling using polysomnography (PSG) data. By transforming electroencephalography, electrocardiography, and Resp into interpretable projection scores, we identified physiological markers predictive of multiple cardiovascular outcomes. These risk profiles, combined with traditional risk scores, significantly improved prediction across both internal and external cohorts. Our findings highlight the untapped potential of PSG signals beyond sleep staging, providing an interpretable, scalable, and clinically actionable approach for personalized cardiovascular risk stratification.
17 Apr 2026
Read appraisal →International journal of medical informatics
Rule-augmented constraint learning for semantic error detection in MIMIC-III knowledge graph
High-quality, error-free data is essential for developing reliable data-driven models, particularly in clinical decision support systems where inaccurate predictions can have serious consequences. While KGs offer a structured and semantically rich representation for clinical data, ensuring their consistency and correctness remains a challenge. Existing rule mining techniques provide solutions for the automatic extraction of logical constraints from KGs, but they often produce redundant or clinically irrelevant rules, especially when dealing with numeric or categorical literals such as age or lab values. KG constraints-rules intended to capture implausible or conflicting facts in the KG-can be used to spot semantic errors: facts that might conform to the underlying schema but contradict domain knowledge. In this work, we propose a novel framework for constraint learning in clinical KGs that identifies and transforms high-confidence rules into clinically plausible constraints. We propose two approaches, based on class disjointness and literal clustering combined with rule mining. We validate the clinical relevance of these generated rules using expert-curated constraints and large language models (LLMs). The results on the MIMIC-III clinical dataset show that rule filtering based constraint learning effectively preserves clinically meaningful rules that align with established medical knowledge. For numeric data, we achieve reliable value groupings through our clustering-based method, and the rules derived from these groupings were validated by LLMs. Their outputs confirm the clinical relevance of a portion of those discovered rules. By providing interpretable and scalable solutions to semantic inconsistencies in KGs, this study contributes to increasing the KG trustworthiness and its clinical usability.
17 Apr 2026
Read appraisal →JMIR cancer
Exploring the Adoption of Mobile Health Apps Among Patients with Head and Neck Cancer After the COVID-19 Pandemic: Secondary Analysis of Cross-Sectional Survey
BACKGROUND: Patients with head and neck cancer (HNC) face significant challenges in accessing coordinated care due to the complexity and multimodality of their treatment and the impact on vital functions. The COVID-19 pandemic has disrupted cancer care while accelerating the adoption of digital health solutions. Mobile health (mHealth) apps offer potential solutions for remote symptom monitoring, communication between patients and providers, and continuity of care. Nevertheless, their acceptance among patients with HNC remains limited due to age-related digital divides and concerns about accessibility. OBJECTIVE: The aim of this study was to investigate the impact of the COVID-19 pandemic on patients with HNC and to explore their attitudes toward mHealth apps as a supplement to cancer treatment. METHODS: A secondary analysis of a cross-sectional survey was conducted between January 2023 and May 2024 at Julius Maximilian University of Würzburg in Germany. A total of 355 patients with HNC were recruited and completed the structured "Cancer and COVID-19" survey via the Corona Health app or in paper form. The 25-question survey assessed sociodemographic information, the impact of the pandemic on diagnosis/treatment/aftercare, and interest in mHealth apps. Descriptive statistics and bivariate correlations were used for the analysis. RESULTS: The cohort comprised 261 (74%) men and 94 (26%) women with an average age of 67 (SD 11.5) years. Most participants (n=264, 74%) stated that the pandemic had no impact on their cancer treatment, although 20% (n=71) experienced disruptions, particularly in follow-up appointments and treatment monitoring. Only 10% (n=36) currently used health apps, but 57% (n=203) expressed a willingness to use mHealth technologies. Younger patients, patients with higher education, and participants who were more affected by the pandemic showed greater openness to digital health solutions. The most significant barriers included age, digital literacy, and perceived usefulness, while preferred app features included interaction with physicians (n=160, 45%) and data sharing with researchers (n=153, 43%). CONCLUSIONS: Although the COVID-19 pandemic had only a limited direct impact on HNC care at the institution at hand, it revealed significant patient interest in mHealth apps. However, significant barriers remain, particularly among older adults with lower digital literacy. Future mHealth initiatives should focus on improving digital literacy, addressing privacy concerns, demonstrating clinical benefits, and developing personalized, accessible solutions to optimize cancer care for this vulnerable population.
17 Apr 2026
Read appraisal →JMIR human factors
Comparing Images of Depression in Mass Media and AI-Generated Pictures: Mixed Methods Study
BACKGROUND: Images play an important role in reducing stigma related to mental health, which often is distorted in the media. In recent years, generative artificial intelligence (AI) has been used to generate images related to mental health. However, first reports suggest that AI-generated images do not depict mental health conditions accurately. In-depth studies on the topic of mental health representations in AI-generated images are still missing. OBJECTIVE: The main objective of this study is to analyze and compare the visual representation of depression in mass media and in AI-generated images. METHODS: The methodologies used were discussion groups (15 participants) and a quasi-experimental online survey (792 interviewees), aimed at people with depression and young people. RESULTS: The results showed that both the images used in the media and those generated by AI reproduced stereotypes and stigmas about depression. However, participants considered AI-generated pictures to be more stereotypical, stigmatizing, and more likely to have a negative impact on people with depression. In contrast, media images were considered more appropriate, realistic, inclusive, and that better reflected the relationship between gender and depression. Statistically significant differences were observed between the control and test groups in both people with depression and young people (P<.001), indicating that when people were aware of what images were AI-generated, they tended to reject them to a greater extent. CONCLUSIONS: Considering the current trend toward the widespread use of AI in mental health communication, it is crucial to promote closer collaborations between science journalists, AI developers, and mental health experts, including patients' associations, as well as a shift toward user-participatory AI design.
17 Apr 2026
Read appraisal →JMIR formative research
Development of a Contextualized, Research-Based Flemish Assessment Framework for Digital Care, Assistance, and Support: Delphi Study
BACKGROUND: The rapid evolution of digital technologies has transformed health, mental health, and social care, offering new modalities of digital care, assistance, and support through web-based platforms, mobile apps, extended reality, wearables, and artificial intelligence systems. Despite this proliferation, there is little consensus on what constitutes "high-quality" digital care. Challenges persist regarding data security, interoperability, accessibility, sustainability, and professional competence, whereas existing standards and regulations provide fragmented guidance. OBJECTIVE: This study aimed to develop a contextualized, consensus-based quality assessment framework for digital care, assistance, and support in Flanders, Belgium. For this purpose, perspectives across technology, organizational processes, and professional competencies were integrated. METHODS: The study used a multiphase design comprising (1) 10 expert interviews with Flemish government officials; (2) a narrative literature review of 303 peer-reviewed and gray literature sources; (3) a 3-round Delphi study with 50 experts across 5 domains (end users, facilitators, technology developers, deontology and ethics experts, and digital inclusion and media literacy experts); and (4) 4 complementary focus groups and 3 interviews with specialists in artificial intelligence, regulation, social work, mental health, and IT. The Delphi rounds gathered iterative feedback through open-ended elicitation, structured rating, and classification of quality criteria. Quantitative data were analyzed using descriptive statistics, whereas qualitative feedback was subjected to thematic analysis. RESULTS: A total of 50 experts participated in round 1, a total of 40 (80%) participated in round 2, and 27 (54%) participated in round 3. Round 1 generated 577 unique quality criteria, consolidated into 26 clusters organized under 3 pillars: technology, organization, and professional competencies. The relative importance across pillars was balanced (mean score 37.29, SD 12.38 for technology; 33.33, SD 10.39 for professional competencies; and 29.80, SD 10.45 for organizations). Accessibility, reliability, and safety ranked highest for the technology; vision, quality monitoring, and infrastructure ranked highest for organization; and support, digital competencies, and ethics ranked highest for professional competencies. The finalized framework included 112 criteria, of which 35 (31.3%) were designated as optional and 77 (68.8%) were designated as minimum requirements. Focus groups and interviews validated the framework's comprehensiveness and usability, emphasizing proportional implementation, user centrality, and alignment with European Union regulations. Stakeholders highlighted the need for tools, training, and governance mechanisms to ensure adoption and sustainability. CONCLUSIONS: This study produced a codeveloped, context-sensitive quality assessment framework that balances technological robustness, organizational readiness, and professional competence in digital care, assistance, and support. The framework can serve both as a quality safeguard and a developmental road map. Accompanying self-assessment and governance tools enhance practical applicability. Implementation success will depend on governmental support, resource allocation, and structured feedback loops. Future research should pilot the framework in real-world settings, assess its impact, and establish mechanisms for continuous updates to maintain relevance in a rapidly evolving digital landscape.
17 Apr 2026
Read appraisal →BMJ open
mHealth-based Healthy Lifestyle Promotion (MYLIFE) for metabolic syndrome risk reduction in Chinese employed adults: protocol for a cluster-randomised controlled trial
INTRODUCTION: Metabolic syndrome (MetS) poses a significant public health challenge among employed adults. Lifestyle modifications have been shown to be effective in preventing the onset and progression of MetS in employed adults, and the widespread adoption of mobile and wearable technologies introduces an appealing approach to mHealth lifestyle interventions. When widely implementing smartphone-based interventions for employed adults, enhancing equitable access and promoting wearable device use for those with MetS is a more cost-effective way to reduce health disparities, particularly in resource-limited settings. However, relevant evidence is currently lacking. OBJECTIVE: This study aims to design an mHealth-based Healthy Lifestyle Promotion (MYLIFE) trial, a three-arm randomised controlled trial (RCT), to evaluate the effectiveness of a smartphone-based lifestyle intervention and a combined smartphone-based lifestyle intervention with intensive intervention for MetS individuals using wearable devices, in reducing MetS risk among employed adults. METHODS AND ANALYSIS: This is a three-arm, parallel, single-blind, cluster RCT with a 12-week intervention and a total follow-up of 1 year. The trial will recruit 120 workplaces from the Chinese Cohort of Working Adults in a 1:1:1 ratio to either a control group or one of two intervention groups. Within these workplaces, a total of at least 348 eligible participants will be enrolled. The regular mHealth group will receive smartphone-based intervention. The intensive mHealth group will receive smartphone-based intervention for all participants, and an additional wearable device-intensive intervention for those with MetS. The primary outcome is the Chinese MetS Z score at 12 weeks. Secondary outcomes include MetS Z score, prevalence of MetS, specific MetS components, questionnaire-based indicators, and clinical outcomes at 12 weeks and 1 years. The main analysis will follow the intention-to-treat principle, using mixed-effects models to assess between-group differences in outcomes. ETHICS AND DISSEMINATION: The protocol has been approved by the Ethics Committee of the West China School of Public Health and the West China Fourth Hospital, Sichuan University (Gwll2025024). The findings will be published in peer-reviewed journals and presented at scientific conferences. TRIAL REGISTRATION NUMBER: ChiCTR2500101904.
17 Apr 2026
Read appraisal →BMJ open
Remote assessment and practice implementation for disorders in ears, nose and throat: a mixed-methodology observational study in the United Kingdom
INTRODUCTION: Ear, nose and throat (ENT) conditions are highly prevalent in primary and secondary care, yet patients frequently face prolonged waits for specialist review. In England, over half of ENT patients wait beyond the NHS 18-week referral-to-treatment target. Many of these cases can be effectively managed with advice and non-surgical interventions, presenting an opportunity for remote service innovation.This study aims to evaluate the clinical effectiveness, cost-effectiveness, acceptability and environmental sustainability of a digitally enabled remote ENT clinic model compared with traditional face-to-face pathways. METHODS AND ANALYSIS: This single-centre, mixed-methods, prospective cohort study will be conducted at University Hospitals Birmingham NHS Foundation Trust. Remote clinics will use trained staff to collect diagnostic data (including endoscopic imaging and boothless hearing tests) for consultant review via secure cloud-based software. Quantitative analysis will assess patient outcomes, costs, waiting times, carbon footprint and satisfaction. Qualitative data from semi-structured interviews with patients, clinicians and managers will explore acceptability, scalability and barriers to implementation. The qualitative data will be analysed using the framework methodology, according to the non-adoption, abandonment, scale-up, spread and sustainability framework, while the strengthening the reporting of observational studies in epidemiology framework will be used to guide the reporting of quantitative data. Cost-effectiveness analyses will follow NICE guidelines, while environmental impact will be measurement will be informed by the sustainability in quality improvement framework. Recruitment will be aiming for 300 completed datasets and 30-35 interviews. ETHICS AND DISSEMINATION: Ethical approval has been granted (IRAS 350908; REC 25/SW/0116). Findings will be disseminated via conferences, peer-reviewed journals and institutional communication channels.
17 Apr 2026
Read appraisal →JMIR formative research
Fact-Checking Large Language Model Responses to a Health Care Prompt: Comparative Study
BACKGROUND: Large language models use machine learning to produce natural language. These models have a range of potential applications in health care, such as patient education and diagnosis. However, evaluations of large language models in health care are still scarce. OBJECTIVE: This study aimed to (1) evaluate the accuracy and efficiency of automated fact-checking by 2 large language models and (2) illustrate a process through which a large language model might support a patient in redrafting a prompt to include key information needed for patient safety. METHODS: A parallel comparison of 2 large language models and 3 human experts was conducted. A clinical scenario was devised in which a woman aged 23 years questions the safety of retinoid treatment for acne by sending prompts to 2 large language models (GPT-4o and OpenBioLLM-70B). GPT-4o and OpenBioLLM-70B were asked to suggest improvements to the patient's initial prompt to elicit key information for clinical decision-making. After the patient sent the revised prompt to the large language models, the models were then asked to fact-check the final response. To test the generalizability of automated fact-checking, a set of 20 clinical statements on disparate topics, mostly related to drug indications, contraindications, and side effects, was developed. The large language models also fact-checked these 20 medical statements. The results were compared against the evaluations of 3 clinical experts. The outcome measures were as follows: (1) percentage of accuracy of automated fact-checking, (2) time to complete fact-checking, and (3) a binary outcome for prompt redrafting (advising the patient to revise her prompt by naming her acne medication to address safety concerns). RESULTS: For the scenario of a patient with acne, GPT-4o and OpenBioLLM-70B both had 86% agreement with the clinical experts' fact-checking. The large language models did not consistently convey the urgency of discontinuing isotretinoin treatment when pregnancy is suspected. In addition, the models did not adequately convey the importance of folic acid supplementation during pregnancy. For the set of 20 medical claims, GPT-4o fact-checking had 100% agreement with that of human experts, whereas OpenBioLLM-70B had 95% agreement. OpenBioLLM-70B diverged from human experts and GPT-4o on 1 question related to pediatric use of antihistamines. The expert fact-checks took a mean time of 18 (SD 3.74) minutes, GPT-4o took 42 seconds, and OpenBioLLM-70B took 33 minutes. The GPT-4o responses for the acne scenario had some inconsistencies but zero fabrication and no obvious omissions. In contrast, OpenBioLLM-70B omitted 1 key information item needed for patient safety. CONCLUSIONS: GPT-4o can interact with patients to improve the quality and comprehensiveness of the information contained in health-related prompts. GPT-4o and OpenBioLLM-70B can conduct efficient fact-checking that is close to the level of accuracy of human experts. Human experts need to perform additional checks for accuracy and safety.
17 Apr 2026
Read appraisal →JMIR mental health
Determinants of Digital Health Literacy Among Patients With Serious Mental Illness: Cross-Sectional Survey
BACKGROUND: Individuals with serious mental illness increasingly use digital devices and the internet to access health information and services but often face challenges when navigating digital tools, which may limit the benefits they receive from online health resources and digital health care services. OBJECTIVE: The objective of our study was to assess digital health literacy among individuals with serious mental illness and identify factors influencing this literacy. METHODS: Participants were recruited, using convenience sampling, from 2 psychiatric clinics, 1 day-care center, and 4 halfway houses in Taipei, Taiwan, between May 2024 and February 2025. Self-reported data were collected using a survey that incorporated the eHealth Literacy Scale, the Attitudes Toward Computer/Internet Questionnaire, and the Mobile Device Proficiency Questionnaire. Generalized linear modeling was applied to identify factors associated with digital health literacy. RESULTS: Among 255 participants included in the analysis, 83.5% (n=213) reported owning at least 1 digital device. Digital health literacy was significantly lower among individuals who reported greater perceived difficulty in using digital tools (B=-1.533, 95% CI -2.350 to -0.717; P<.001) and higher distrust in online information (B=-0.986, 95% CI -1.916 to -0.056; P=.04). By contrast, greater mobile device proficiency (B=0.144, 95% CI 0.008-0.281; P=.04) and self-efficacy (B=1.777, 95% CI 0.376-3.177; P=.01) were positively associated with digital health literacy. CONCLUSIONS: Despite widespread device ownership, digital health literacy was varied and generally suboptimal among patients with serious mental illness. Perceived difficulty and distrust emerged as major barriers; proficiency and self-efficacy facilitated higher literacy. These findings highlight the need for mental health professionals to integrate tailored digital skills training, confidence-building strategies, and ongoing support into digital health interventions for individuals with serious mental illnesses.
16 Apr 2026
Read appraisal →Journal of the American College of Cardiology
Risk-Guided Atrial Fibrillation Screening With Artificial Intelligence-Enabled Electrocardiogram Models: A VITAL-AF Trial Analysis
BACKGROUND: Screening for atrial fibrillation (AF) may lead to earlier detection and initiation of preventive measures. Current AF screening approaches using a guideline age-based threshold of ≥65 years have shown limited yield. OBJECTIVES: In an AF screening trial, we assessed whether the screening effect was larger among individuals at elevated AF risk using validated clinical and electrocardiogram (ECG)-based artificial intelligence (AI) risk models. METHODS: VITAL-AF was a cluster-randomized trial of patients aged ≥65 years treated at 1 of 16 primary care practices affiliated with Massachusetts General Hospital. Patients randomized to a screening practice were screened using a single-lead ECG. Among VITAL-AF participants without prevalent AF with at least one 12-lead ECG within 3 years before enrollment, we estimated AF risk using 3 validated models derived outside of VITAL-AF: the Cohorts of Heart and Aging Research in Genomic Epidemiology-AF (CHARGE-AF) clinical score, an AI-based model using a 12-lead ECG alone (ECG-AI), and a model combining ECG-AI and CHARGE-AF (CH-AI). Two-year incident AF discrimination was assessed by the time-dependent area under the receiver-operating characteristic curve (AUROC) and average precision. AF screening effect was defined as the difference in 2-year incident AF diagnosis rate (per 100 person-years) in screening vs control across AF risk deciles. RESULTS: Of 30,630 VITAL-AF participants without prevalent AF, 16,937 had pretrial ECG and clinical data. Each score discriminated 2-year AF risk according to AUROC (CHARGE-AF: 0.711 [95% CI: 0.671-0.749]; ECG-AI: 0.784 [95% CI: 0.743-0.819]; CH-AI: 0.788 [95% CI: 0.754-0.824]) and average precision (0.0952 [95% CI: 0.0836-0.112]; 0.132 [95% CI: 0.113-0.157]; 0.133 [95% CI: 0.117-0.159]). An AF screening effect was observed in the top decile of CH-AI (AF diagnosis rate in screening 10.07/100 person-years [95% 8.28-11.87] vs 7.76 [95% 6.30-9.21] in control, P < 0.05), corresponding to a difference in AF diagnosis rate of 2.32/100 person-years (95% CI: 0.01-4.63) and number-needed-to-screen of 43 per year. CONCLUSIONS: Use of ECG-based AI and clinical factors identified individuals at particularly high risk for AF who may benefit from screening. Findings suggest a trade-off between increasing AF screening efficiency and decreasing population coverage (ie, restriction of the screening pool). Future studies are needed to determine whether a risk-based approach is optimal or whether consideration of additional clinical- and systems-level factors (eg, access, health care system engagement) can further refine AF screening strategies. (Screening for Atrial Fibrillation Among Older Patients in Primary Care Clinics [VITAL-AF]; NCT03515057).
16 Apr 2026
Read appraisal →JMIR formative research
Development of Virtual Mental Health Stepped Care Service for a Heart Failure Remote Management Program: Qualitative Descriptive Study
BACKGROUND: Depression is highly prevalent yet undertreated among people living with heart failure, indicating barriers to mental health services. Although various digital mental health interventions have been developed to detect, treat, and manage depression in this population, these interventions have seen limited integration into clinical care and a lack of implementation research. Stepped care is a service innovation that may promote the implementation of these technologies into clinical settings, but few studies have examined how these services are designed in clinical settings. OBJECTIVE: This study aimed to identify strategies to address health system barriers to accessing mental health care from the perspective of people living with heart failure, clinicians, and researchers, and to incorporate these strategies into the design of a virtual mental health stepped care service within a heart failure remote management program. METHODS: A qualitative description study was conducted using purposive recruitment of people living with heart failure, clinicians, and researchers from a heart failure remote patient management program. As part of a service design approach, semistructured interviews explored potential strategies to address barriers to accessing mental health services. Two researchers coded the data descriptively and constructed themes to guide the development of a virtual stepped care service. RESULTS: A total of 22 participants were interviewed, comprising 13 people living with heart failure and 9 clinicians and researchers. Six themes were identified, comprising 4 requirements and 2 foundational principles. The requirements were to (1) adopt a collective approach to identify distress across methods, people, and time points; (2) maintain a referral-based approach; (3) rely on existing mental health human resources; and (4) offer patient choice among various mental health care options. These requirements were supported by two principles: (1) building on organizational strengths and (2) reducing treatment burden. Based on these findings, a virtual stepped care service was developed, incorporating a depression screening module, referral-based workflows, and, where clinically appropriate, patient choice in treatment selection. CONCLUSIONS: The stakeholder-informed design of this virtual stepped care service contributes to the limited literature on stepped care service design and demonstrates how such models can be tailored to their intended contexts. Although each component was designed to address health system barriers to mental health care for people living with heart failure, resource limitations may constrain the balance between feasibility and quality of care. Future research should evaluate the acceptability of this model among people living with heart failure and clinicians.
16 Apr 2026
Read appraisal →Physiological measurement
Towards real-time sleep stage classification: a deep learning approach leveraging PPG and ECG
Objective.This work aims to enable adaptive consumer sleep technologies (CSTs) for sleep intervention by developing a deep learning model for sleep stage classification using wearable sensor data.Approach.We propose an end-to-end deep learning approach leveraging Photoplethysmography (PPG) signals, commonly available in CSTs. Model performance is improved by pretraining with electrocardiography (ECG) from the large-scale multi-ethnic study of atherosclerosis dataset datasets. Training and evaluation are conducted with the Dataset for Real-time sleep stage EstimAtion using Multisensor wearable Technology and an additional dataset comprising of 13 participants (aged 22-71 years) without prior known sleep disorders. The dataset contains combined synchronized polysomnography (PSG) and Empatica E4 wearable data, annotated with American Academy of Sleep Medicine (AASM) sleep stages.Main results.The proposed method demonstrates sleep stage classification from minimally processed PPG signals for real-time intervention. While ECG-trained models are not directly transferable to PPG, fine-tuning significantly improves performance, achieving up to a 29% increase in multi-stage classification accuracy.Conclusion.Pretraining with ECG and fine-tuning with PPG increases sleep stage classification for end-to-end deep learning models, exceeding previous efforts particularly in 3-stage sleep classification.Significance.This work contributes to sleep health by developing a sleep stage classification model for minimally processed PPG sensor data and takes a step further towards making adaptive CSTs feasible for use with wearable sensors.
16 Apr 2026
Read appraisal →Journal of investigational allergology & clinical immunology
Future Perspectives for Artificial Intelligence in Allergy: Advances in Diagnosis, Monitoring, and Personalized Care
Artificial intelligence (AI) is significantly transforming the specialty of allergy by offering novel tools for diagnosis, risk prediction, disease monitoring, and personalized care. This narrative review comprehensively explores key advances in the application of AI techniques-expert systems, machine learning, deep learning, and natural language processing-across various allergic diseases such as asthma, anaphylaxis, atopic dermatitis, food allergy, allergic rhinitis, eosinophilic esophagitis, and drug hypersensitivity reactions. Despite these advancements, several challenges persist, including model interpretability, external validation, and ethical and regulatory barriers. Nonetheless, AI holds strong potential to enhance diagnostic efficiency, therapeutic precision, and patient outcomes. This review presents an updated perspective on the state of the art of AI in allergy, highlighting both its achievements and ongoing challenges.
16 Apr 2026
Read appraisal →JMIR research protocols
Integrating Gait Analysis and AI Into Knee Osteoarthritis Care: Protocol for a 3-Phase Participatory Qualitative Study
BACKGROUND: Knee osteoarthritis (OA) leads to pain, disability, and reduced quality of life. For advanced stages, knee arthroplasty surgery is the standard treatment, yet dissatisfaction and persistent mobility deficits remain common. Current surgical decision-making processes seldom incorporate objective predictors of outcomes, such as biomechanical data. Advances in computer vision, wearable sensors, and artificial intelligence now enable efficient capture and interpretation of clinically prognostic gait features in real-world settings. However, the clinical adoption of such innovations remains limited, hindered by usability challenges, misalignment with stakeholder needs, and system-level barriers. OBJECTIVE: This study aims to inform the development of a knee OA clinical decision support (CDS) tool that integrates gait analysis and artificial intelligence with clinical decision-making. More broadly, it seeks to advance a framework for participatory digital health CDS tool implementation by identifying and addressing suboptimal workflows, stakeholder priorities, and organizational challenges. METHODS: This study uses a 3-phase participatory design process, with in-depth interviews conducted in each phase to generate insights that inform subsequent stages. Phase 1 investigates perceived barriers to and facilitators of the implementation of a digital CDS tool and examines current workflow processes in knee OA management. Phase 2 focuses on identifying and defining user requirements to guide solution ideation for translational digital decision support. Phase 3 assesses user acceptance of prototyped solutions. Field observations in phase 1 and usability testing in phase 3 will supplement interview findings with real-world evidence. RESULTS: The study was funded in November 2024. Ethics approval was granted by the Nova Scotia Health Research Ethics Board on July 28, 2025. Data collection began in September 2025. As of February 2026, a total of 6 clinicians have been interviewed, with all 6 interviews transcribed and 4 coded as part of phase 1 analysis. Study completion is anticipated by August 2026. Findings will be published by January 2027. CONCLUSIONS: Our stakeholder-driven approach prioritizes both technological rigor and practical usability, addressing key human factors for successful implementation. While initially focused on Nova Scotia's orthopedic setting, this research provides a scalable framework for integrating digital decision support tools into broader clinical environments, advancing innovation in knee OA care and beyond.
16 Apr 2026
Read appraisal →JMIR formative research
Designing Psychologically Grounded Artificial Intelligence for Supporting Bystander-Based Cyberaggression Intervention: Mixed Methods Exploratory Study
BACKGROUND: Cyberaggression poses a growing threat to mental health, contributing to increased distress, reduced self-esteem, and other adverse psychosocial outcomes. Although bystander intervention can mitigate the escalation and impact of cyberaggression, individuals often lack the confidence, strategies, or language to respond effectively in these high-stakes online interactions. Advances in generative artificial intelligence (AI) present a novel opportunity to facilitate digital behavior change by assisting bystanders with contextually appropriate, theory-informed intervention messages that promote safer online environments and support mental well-being. OBJECTIVE: This mixed methods design study aimed to explore the feasibility of using generative AI to support bystander intervention in cyberaggression on social media. Specifically, we examined whether AI can generate effective responses aligned with established intervention strategies and how these responses are perceived in terms of their potential to de-escalate online harm and foster behavior change. METHODS: We collected 1000 real-world cyberaggression examples from public social media datasets and generated bystander intervention responses using 3 distinct prompt strategies: a generic policy reminder, a baseline GPT prompt, and a theory-driven GPT prompt (AllyGPT). To evaluate the responses, we conducted computational linguistic analyses to assess their psycholinguistic features and carried out a mixed methods evaluation. Three trained coders rated each message on favorability, conversational impact, and potential to change behavior and later participated in semistructured interviews to reflect on their evaluation process and perceptions of intervention effectiveness. RESULTS: Linguistic analyses revealed that baseline GPT responses exhibited more emotionally positive and authentic language compared to AllyGPT responses, which showed a more analytical and assertive tone. Policy reminder messages were linguistically rigid and lacked emotional nuance. Human evaluation results showed that AllyGPT responses received the highest effectiveness ratings for low-incivil cyberaggression cases in 2 dimensions (favorability and changing behavior), and baseline GPT works better for mid and high levels for all effectiveness dimensions. For medium- and high-incivility aggressions, baseline GPT responses received the highest ratings across all 3 dimensions of effectiveness (favorability, discussion-shifting potential, and likelihood of changing bullying behavior), followed by AllyGPT, with policy reminders rated lowest. Qualitative feedback further emphasized that baseline GPT responses were perceived as natural and inclusive, while AllyGPT responses, although grounded in psychological theory, were sometimes viewed as overly direct. Policy reminders were considered clear but lacked persuasive impact. CONCLUSIONS: Our work showed that designing effective AI-generated bystander interventions requires a deep sensitivity to platform culture, social context, and user expectations. By combining psychological theory with adaptive, conversational design and ongoing feedback loops, future systems can better support bystanders, delivering interventions that are not only contextually appropriate but also socially resonant and behaviorally impactful. As such, this work serves as a foundation for scalable, human-centered AI systems that promote safer online spaces and users' mental well-being.
16 Apr 2026
Read appraisal →JMIR nursing
Application of the Technology Acceptance Model to Predict Nursing Students' Intention to Use Informatics: Cross-Sectional Study
BACKGROUND: Nursing informatics is essential for digital health transformation; however, the technology acceptance of undergraduate nursing students in Saudi Arabia remains underexplored. OBJECTIVE: This study examined factors influencing nursing students' intention to use informatics technologies using the technology acceptance model. METHODS: A cross-sectional survey was conducted with 132 undergraduate nursing students. Data were analyzed using descriptive, correlational, and hierarchical regression analyses. RESULTS: Perceived usefulness (mean 3.68, SD 1.22) and perceived ease of use (mean 3.64, SD 1.32) were the strongest predictors of acceptance, together explaining 87% of the variance (R²=0.87; β=0.323 for usefulness, P<.001; β=0.195 for ease of use, P=.032). Only 25.8% (n=34) of the students often used electronic health records, while 31.8% (n=42) had no electronic health record experience, indicating a clear gap in practical informatics exposure. CONCLUSIONS: Nursing students' acceptance of informatics is primarily driven by its perceived usefulness and perceived ease of use. These findings highlight the urgent need to integrate practical, user-centered informatics training and clinical simulation into undergraduate nursing curricula to better prepare students for technology-based practice.
16 Apr 2026
Read appraisal →Journal of affective disorders
Who engages? Machine learning insights into digital mindfulness-based intervention for generalized anxiety disorder.
BACKGROUND: Although mindfulness ecological momentary interventions (MEMI) appear effective in alleviating worry symptoms, treatment engagement remains suboptimal. Determining baseline variables of MEMI over self-monitoring placebo (SM) can inform tailored interventions for individuals with generalized anxiety disorder (GAD). METHOD: Machine meta-learning methods (ML) were applied to predict two-week engagement (log-transformed number of prompts completed) among individuals randomized to MEMI or SM (N = 110). Sixteen baseline variables comprised the predictor set: clinical, demographic, process, and executive functioning (EF) factors. Random forest using a five-fold nested cross-validation approach mitigated overfitting. X-learner meta-algorithms estimated conditional average treatment engagement (CATE). Shapley additive explanations evaluated relative importance. RESULTS: The 16-predictor model displayed strong predictive performance (R-squared [R2] = 82.7%; root mean squared error [RMSE] = 0.780; mean absolute error [MAE] = 0.512). The top-10 predictor model also yielded good predictive performance (R2 = 82.1%; RMSE = 0.547; MAE = 0.307). As predicted by the CATE analysiscate, participants had the highest treatment engagement when assigned to MEMI instead of SM (d = 1.447, p < .001). The following baseline variables predicted more engagement with MEMI over SM: lower GAD severity, inhibition response time (RT), and EF errors, higher attentional control, empathy, and verbal fluency (capitalization theory); lower mindfulness, and treatment expectancy, poorer working memory, and higher set-shifting RT (compensation model). LIMITATIONS: The small sample size, single engagement metric, and brief duration might constrain generalizability. DISCUSSION: Integrating robust ML approaches could optimally identify prescriptive predictors of engagement to brief digital mental health interventions to inform targeted treatments. TRIAL REGISTRATION: ClinicalTrials.gov ID (NCT04846777).
16 Apr 2026
Read appraisal →Journal of medical Internet research
A Health-Related Digital Ecological Momentary Assessment in Children (Aged 5-11 Years): Systematic Review
BACKGROUND: Digital ecological momentary assessment (EMA) collects data on experiences as they occur in daily life, capturing dynamic, context-sensitive experiences often missed by retrospective reporting. While EMA shows promise for pediatric health research, preadolescents have distinct socioemotional and cognitive characteristics likely to affect engagement. Existing reviews have not focused on the acceptability and feasibility of EMA protocols for this age group. OBJECTIVE: This review aimed to examine digital EMA protocols used with children aged 5-11 years across health domains, focusing on protocol characteristics, acceptability, and feasibility. We address 3 research questions (RQs)-RQ1: What are the characteristics of these protocols? RQ2: What is the feasibility and acceptability of these protocols? RQ3: What are the characteristics of high and low response rate protocols? METHODS: We searched 10 databases (CINAHL, Embase, ACM Digital Library, IEEE Xplore, Cochrane Library, PsycINFO, Web of Science, PubMed, Scopus, and MEDLINE) for peer-reviewed studies published up to October 2025. Eligible studies used EMA with children aged 5-11 years to collect health data via digital devices. Two researchers independently screened studies (SC and LT); one (SC) conducted quality assessment and data extraction. Findings were narratively synthesized. RESULTS: We identified 17 protocols across 37 studies. Most targeted nonclinical populations, used handheld devices, spanned 3-28 days, and applied interval-contingent prompting (RQ1). Response rates were available or calculable for 15 of 17 protocols, ranging from 48% to 92% (RQ2). Six protocols reported response rates of ≥80%. However, key data required for pooling (eg, raw counts for planned vs completed prompts) were missing or selectively reported. This contributed to 13 of 17 protocols being rated at critical risk of bias (ROBINS-I, v2). As a result, the strength of evidence was limited by poor reporting and high risk of bias. Facilitators included uncomplicated, engaging technology, reminders, and caregiver involvement. Barriers included device burden, restricted device access, difficulty with accurate reporting, stigma, limited device awareness, and insufficient caregiver support. High-response protocols (≥80%) often involved older children or clinical groups, ≥3-week duration, fixed schedules (≥20 items per prompt, 3 or 4 times per day), timing customization, and incentives (RQ3). CONCLUSIONS: This review provides the first systematic synthesis on preadolescents, offering insight into EMA protocol design beyond prior work treating children as a single group. By examining 17 EMA protocols, the review identifies gaps in developmental appropriateness and reporting quality, highlighting where the evidence may differ from adolescent and adult EMA research. The results suggest that digital EMA for preadolescents requires greater focus on child-centered design to increase acceptability and adherence, alongside improved reporting standards, so protocols can be meaningfully compared. With these advances, EMA could be more effectively integrated into pediatric health monitoring, tailored to the needs of different age groups.
16 Apr 2026
Read appraisal →International journal of medical informatics
A structured decision-support framework for selecting imputation methods in clinical structured datasets: A secondary analysis
OBJECTIVE: Missing values are a common challenge in healthcare data analysis, and inadequate handling can introduce bias and undermine the validity of findings. Imputation methods offer a practical solution, but selecting an appropriate approach depends on multiple dataset-specific factors. This study proposes a structured decision-support framework that defines key prerequisites for choosing suitable imputation methods during the preprocessing of clinically structured datasets. METHODS: A secondary analysis of a previous systematic review was conducted, covering 69 studies to identify factors influencing imputation method selection. Domain experts evaluated assumptions regarding missing data characteristics and dataset structure, reaching consensus on the most relevant factors. These factors were synthesized into a structured framework designed to guide systematic and transparent imputation method selection in clinical data preprocessing workflows. RESULTS: Nine key factors were identified as essential for determining an appropriate imputation method. These include missing data characteristics, mechanism, pattern, and ratio and dataset attributes such as data type, variable role, distribution, and correlation. The ratio of missingness was the most influential factor, followed by variable role and missing value mechanism. Most studies emphasized the combined importance of both missing data properties and dataset features in imputation selection. CONCLUSIONS: Understanding the characteristics of missing values and dataset structure is crucial for selecting appropriate imputation methods. The proposed structured decision-support framework provides an evidence-based checklist to enhance transparency, reproducibility, and reliability in preprocessing clinical datasets within medical informatics workflows.
16 Apr 2026
Read appraisal →Journal of medical Internet research
Digital Technologies and Biomarkers for Locomotor Capacity Assessment in Older Adults: Systematic Review
BACKGROUND: Locomotor capacity, encompassing endurance, balance, muscle strength, muscle function, muscle power, and joint function of the body, is a key determinant of functional ability in older adults. Assessment tools based on digital technologies for objectively assessing locomotor capacity are increasingly being developed, but their reliability, validity, and clinical potential remain underexplored. OBJECTIVE: This systematic review aims to evaluate the current state of digital technologies, assess their validity and reliability for assessing locomotor capacity, and facilitate their effective implementation in clinical settings. METHODS: Systematic literature searches were performed in 6 electronic databases from inception to March 7, 2025. Citation lists from the included studies and gray literature from Google Scholar were additionally searched. Studies focusing on the reliability and validity of digital technologies for assessing locomotor capacity in general older adults were included. Standardized forms were used to extract information on study characteristics, participant demographics, digital technology details, and validity and reliability results. Methodological quality assessment and rating of measurement properties were conducted in accordance with the COSMIN (Consensus-Based Standards for the Selection of Health Measurement Instruments) guidelines. RESULTS: A total of 14 studies were included, of which 13 assessed balance using inertial measurement units, smartphones, balance boards, and force plates, and 1 assessed muscle power using smartphones. Fifty-one digital biomarkers were identified, including 47 for balance and 4 for muscle power assessment. Test-retest reliability coefficients ranged from 0.016 to 0.97, and validity was context specific. Overall, 13 studies demonstrated sufficient test-retest reliability and validity, whereas 1 study was rated as insufficient for convergent validity. Methodological quality was rated as "doubtful" or "inadequate" in 11 studies. CONCLUSIONS: This review provides a comprehensive summary of digital technologies for assessing locomotor capacity in older adults and identifies 51 digital biomarkers with generally acceptable reliability and validity. Unlike previous studies that focused on specific sensor types or disease-specific populations, this review integrates evidence across technologies within general older populations, providing insights into the clinical application potential of digital biomarkers as well as the key translational barriers limiting their real-world implementation. Specifically, existing digital technologies show considerable promise for early detection of functional decline, longitudinal monitoring, and informing personalized interventions. However, their clinical applicability remains constrained by limited assessment of certain locomotor components and by methodological shortcomings across current studies. Future research should prioritize rigorous, high-quality investigations that expand evaluation to a broader range of locomotor components in real-world settings while developing age-friendly tools with enhanced clinical interpretability.
16 Apr 2026
Read appraisal →BMJ open
Generative artificial intelligence-driven chatbots and medical misinformation: an accuracy, referencing and readability audit
OBJECTIVES: Artificial intelligence (AI)-driven chatbots have been rapidly adopted across research, education, business, marketing and medicine. Most interactions, however, come from non-experts using chatbots like search engines, including for everyday health and medical queries. DESIGN: We conducted an original study to audit chatbot responses in health and medical fields prone to misinformation. METHODS: Five popular chatbots were assessed: Gemini (Google), DeepSeek (High-Flyer), Meta AI (Meta), ChatGPT (OpenAI) and Grok (xAI). In February 2025, each chatbot was prompted with 10 questions from five categories: cancer, vaccines, stem cells, nutrition and athletic performance. We deployed an adversarial-like framework, using open- and closed-ended prompts designed to strain models toward misinformation or contraindicated advice. Two experts from each category rated responses as 'non-problematic', 'somewhat problematic' or 'highly problematic' using a coding matrix based on objective, predefined criteria. Citations were scored for accuracy and completeness, and each response was given a Flesch Reading Ease score. RESULTS: Nearly half (49.6%) of responses were problematic: 30% somewhat problematic and 19.6% highly problematic. Response quality did not differ significantly among chatbots (p=0.566) but Grok generated significantly more highly problematic responses than would be expected under a random distribution (z-score +2.07, p=0.038). Performance was strongest in vaccines (mean z-score -2.57) and cancer (-2.12), and weakest in stem cells (+1.25), athletic performance (+3.74) and nutrition (+4.35). Chatbot outputs were consistently expressed with confidence and certainty; from 250 total questions, there were only two refusals to answer (0.8%), both from Meta AI. Reference quality was poor, with a median completeness score of 40% (Q1-Q3: 20-67%). Chatbot hallucinations and fabricated citations precluded any chatbot from producing a fully accurate reference list. All readability scores were graded as 'Difficult' (30-50), equivalent to college sophomore-senior level. CONCLUSIONS: The audited chatbots performed poorly when answering questions in misinformation-prone health and medical fields. Continued deployment without public education and oversight risks amplifying misinformation.
16 Apr 2026
Read appraisal →Journal of affective disorders
What retrospective and dynamic assessments tell us about youth depression: A network analysis perspective.
PURPOSE: Traditional assessments of depressive symptoms often rely on retrospective self-reports, which may be affected by cognitive and memory biases. Few studies have compared retrospective and dynamic (real-time) assessments to examine the consistency and structure of depressive symptom reporting. This study aimed to compare retrospective and dynamic assessments of depressive symptoms in youth using network analysis to explore symptom-level associations and clustering. METHODS: Ninety Brazilian adolescents and young adults (mean age = 18 years), with and without depression, completed the Short Mood and Feelings Questionnaire (SMFQ) every other day for 14 days via a smartphone-based chatbot (dynamic assessment). At the end of the 2-week period, they completed the same questionnaire retrospectively. Network analyses were conducted using Exploratory Graph Analysis (EGA) and Dynamic Exploratory Analysis (DEA) to identify symptom communities and compare network structures across both assessment methods. RESULTS: Both retrospective and dynamic assessments revealed three symptom communities; however, the composition and structure of these communities differed. Retrospective assessments showed stronger connections among cognitive symptoms, while dynamic assessments displayed a more balanced distribution, with stronger associations between somatic and affective symptoms. IMPLICATIONS: Findings highlight significant differences in depressive symptom networks between retrospective and dynamic assessments. Dynamic methods may offer less biased and more ecologically valid insights into youth depression, underscoring the importance of real-time data collection in clinical assessment and research.
16 Apr 2026
Read appraisal →Computers in biology and medicine
Forecasting-based biomedical time-series data synthesis for open data and robust AI
The limited data availability due to strict privacy regulations and significant resource demands severely constrains biomedical time-series AI development, which creates a critical gap between data requirements and accessibility. Synthetic data generation presents a promising solution by producing artificial datasets that maintain the statistical properties of real biomedical time-series data without compromising patient confidentiality. While GANs, VAEs, and diffusion models capture global data distributions, forecasting models offer inductive biases tailored for sequential dynamics. We propose a framework for synthetic biomedical time-series data generation based on recent forecasting models that accurately replicates complex electrophysiological signals such as EEG and EMG with high fidelity. These synthetic datasets can be freely shared for open AI development and consistently improve downstream model performance. Numerical results on sleep-stage classification show up to a 3.71% performance gain with augmentation and a 91.00% synthetic-only accuracy that surpasses the real-data-only baseline.
16 Apr 2026
Read appraisal →JMIR mHealth and uHealth
Effect of a Comprehensive Mobile-Based Respiratory Training Program on Respiratory Function in Survivors of Acute Stroke: Randomized Controlled Trial
BACKGROUND: Respiratory dysfunction frequently occurs during the acute phase of stroke and is associated with reduced ventilatory capacity, respiratory muscle weakness, and increased pulmonary complications. However, delivering standardized respiratory training during hospitalization is often constrained by staffing and service continuity. OBJECTIVE: This study aimed to evaluate the efficacy, safety, and feasibility of a hospital-based comprehensive mobile-based respiratory training program (CMRTP) added to conventional rehabilitation in people with acute stroke who are inpatients. METHODS: This single-center, assessor-blinded randomized controlled trial enrolled 40 patients within 2 weeks after stroke onset with respiratory dysfunction (forced vital capacity <80% predicted). Participants were randomized (1:1) to CMRTP plus conventional rehabilitation or conventional rehabilitation alone. The CMRTP was delivered via the WeChat-based AIRHUB platform and performed 20 minutes twice daily, 5 days per week for 2 weeks, either independently or with caregiver assistance as needed. The primary outcome was change in forced vital capacity from baseline to week 2. Secondary outcomes included forced expiratory volume in 1 second (FEV₁), peak expiratory flow, maximal inspiratory pressure, maximal expiratory pressure, and modified Barthel index. All outcomes were assessed face-to-face by a blinded senior physician, and all analyses followed an intention-to-treat principle. RESULTS: Of 56 screened patients, 40 were randomized, and 39 completed the study. Adherence to the CMRTP reached 96%, and no serious adverse events occurred; mild, transient events (fatigue, dizziness, and hyperventilation) were recorded. Compared with the control group, the CMRTP group demonstrated greater improvement in forced vital capacity at week 2 (mean difference 0.77 L; 95% CI 0.39-1.16; P<.001; η²=0.32), with additional between-group differences in maximal inspiratory pressure (P=.001; η²=.25), maximal expiratory pressure (P<.001; η²=.08), and modified Barthel index (P=.001; η²=.26). No significant group differences were found for forced expiratory volume in 1 second or peak expiratory flow. CONCLUSIONS: A 2-week hospital-based mobile respiratory training program is feasible and safe in people with acute stroke who are inpatients and yields clinically meaningful improvements in respiratory function and daily functional performance when added to conventional rehabilitation.
15 Apr 2026
Read appraisal →International journal of medical informatics
Prioritising digital health technologies in Australian community pharmacy: a delphi study identifying barriers, enablers, and policy implications for implementation
OBJECTIVE: To determine priority digital health technologies for Australian community pharmacies and identify the key barriers, enablers, and policy/funding factors that can inform future implementation planning through expert consensus. METHODS: A two-round Delphi study was conducted with 31 experts representing pharmacy, academia, policy, and digital health. In Round 1, participants identified priority technologies, barriers, and enablers. In Round 2, 27 participants ranked five technologies, nine policy options, and six financial models. Consensus was assessed using descriptive statistics and interquartile ranges (IQRs). RESULTS: E-prescriptions and My Health Record (MyHR) were ranked as top priorities (mean = 1.70 and 2.22; IQR ≤ 1.0). Key barriers included financial constraints, interoperability issues, and digital literacy gaps. Telehealth incentives received the strongest agreement among participants, while reimbursement-based funding and government support were rated as the most supportive financial models for implementation. Broader enablers, such as a national medicine repository and stronger cross-disciplinary collaboration, were also endorsed. CONCLUSION: Digital health adoption in community pharmacy requires prioritisation of core technologies, improved system integration, workforce training, and practical funding mechanisms. These findings offer guidance for policymakers, pharmacy leaders, and digital health stakeholders aiming to embed digital tools more consistently and effectively into pharmacy practice.
15 Apr 2026
Read appraisal →International journal of medical informatics
Clinician preferences for explainable AI in critical care: a comparative study of interpretable models and visualizations for intubation decision support
BACKGROUND: The complexity of many AI models hinders their clinical adoption because the clinicians using them do not regard them as transparent. This study addresses the lack of clinician-centered explainable AI (XAI) interfaces by designing and evaluating intuitive visual explanations for intubation prediction, testing the hypothesis that workflow-compatible designs enhance acceptance. OBJECTIVE: This study compares three, time-aware, visual explanations for XAI-based intubation prediction and evaluate their acceptance, comprehension, and perceived utility among clinicians. METHODS: We developed machine learning models to estimate the near-term risk of deterioration in the patient's condition which may lead to mechanical intubation using ICU time-series data. We generated global and local explanations using SHAP and designed three customized visual formats-a temporal force plot, a temporal bar chart, and a dual-encoded SHAP heatmap. Clinicians (n = 206) evaluated comprehension and usability using objective questions and a Likert-based survey. RESULTS: Based on 4608 critically ill patients with 10 medical variables over 7 hours of data for each patient, the Random Forest (RF) model achieved the highest area under the curve (AUC): 0.94. Furthermore, the local explanations were customized and evaluated by 206 clinicians through a survey conducted on the Prolific platform. A customized heatmap representation was selected as the visualization with the highest perceived clinical utility and alignment with clinical workflows. DISCUSSION: The reported findings support the need for explanation formats to be tailored to clinical reasoning and task context, supporting the concept of cognitive fit. The heatmap's close alignment with clinicians' mental models and its graphical integrity enhances interpretability and trust. This study demonstrates that explanation effectiveness depends on contextual relevance, rather than a universal standard, and that the presentation format itself significantly shapes clinicians' trust in XAI systems. CONCLUSION: This study advances clinical XAI by introducing a time-aware explanation framework for ICU intubation decisions. By integrating temporal trends with model reasoning, our visualizations closely align with clinicians' cognitive workflows. Rigorous clinician-centered evaluation identified the dual-encoded SHAP heatmap as the most useful and workflow-compatible visualization, highlighting the importance of explanation design alongside predictive accuracy for clinical adoption.
15 Apr 2026
Read appraisal →Analytical chemistry
Dual-Signal Amplification Strategy for Synchronous Monitoring of Stress Hormones and Metabolic Indicators
The synchronous monitoring of stress hormones and metabolic indicators is crucial for long-term health management, yet it remains a significant challenge for wearable sensors, primarily due to the ultralow abundance of biomarkers in sweat and the vulnerability of weak electrochemical signals to interference. Herein, we report a wearable electrochemical sensor based on a dual-signal amplification strategy for the synchronous and highly sensitive detection of cortisol, glucose (Glu), and uric acid (UA). The CuFe-PBA/CC substrate provides a platform featuring high surface area, electrocatalytic activity, and dual signal amplification capabilities, which are the primary sources of high sensitivity. The MIP layer enhances the effective concentration of target substances near active sites through a local enrichment effect. Combined with the substrate's high catalytic activity, this amplifies the electrochemical signal specific to the target. It synergizes with an exogenous redox probe ([Fe(CN)6]3-/4-) to establish a dual-amplification pathway, which enhances the current output to the milliampere level and substantially improves the signal-to-noise ratio. To ensure the optimal performance of the sensor, the preparation parameters were optimized via the integration of machine learning. This integrated sensing system exhibits high selectivity, superior sensitivity (with detection limits of 44.78 fM for cortisol, 0.064 μM for Glu, and 0.089 μM for UA), and excellent stability (retaining 88% of the initial signal after 100 cycles of mechanical deformation). Furthermore, we fabricated the sensor into a watch-like form factor, integrated it with high-precision wearable electronic modules, and achieved real-time wireless detection of sweat cortisol, Glu, and UA in ex vivo sweat monitoring experiments. This work provides a novel solution for objective stress assessment and metabolic status monitoring, holding promise for advancing long-term health management applications.
15 Apr 2026
Read appraisal →JAMA
A Decision-Support System to Personalize Antidepressant Treatment in Major Depressive Disorder: A Randomized Clinical Trial
IMPORTANCE: Antidepressants for moderate to severe major depressive disorder may be discontinued prematurely because the prescribed antidepressant is not always the most appropriate medication for an individual. Guidelines have recommended more precise targeting of antidepressant treatment. OBJECTIVE: To evaluate the efficacy of a web-based tool to personalize antidepressant treatment. DESIGN, SETTING, AND PARTICIPANTS: This multicenter, randomized clinical trial included persons between the ages of 18 and 74 years with major depressive disorder. The trial was conducted at 47 sites in 3 countries (Brazil, Canada, and the UK). The first participant was screened on November 29, 2022, and the last follow-up visit occurred on January 15, 2025. INTERVENTION: A total of 540 participants were randomized (1:1) to an evidence-based clinical decision-support system (PETRUSHKA tool; n = 271) or usual care (n = 269). MAIN OUTCOMES AND MEASURES: The primary outcome was treatment discontinuation due to any cause at 8 weeks. The secondary outcomes included treatment discontinuation up to 24 weeks due to adverse events and changes in depressive symptoms (measured with the 9-item Patient Health Questionnaire [PHQ-9]; range, 0-27; higher scores indicate more severe depression) and anxiety symptoms (measured with the 7-item Generalized Anxiety Disorder [GAD-7] questionnaire; range, 0-21; higher scores indicate more severe symptoms). RESULTS: Of the 520 eligible participants, 493 were included in the primary analysis (median age, 35 [IQR, 25 to 48] years; 58% female; PHQ-9 mean score, 16.6 [SD, 5.1]; GAD-7 mean score, 11.5 [SD, 4.1]). At 8 weeks, 41 of 241 participants (17%) in the PETRUSHKA group discontinued the prescribed antidepressant due to any cause vs 69 of 252 (27%) in the usual care group (adjusted relative risk, 0.62 [95% CI, 0.44 to 0.88]; P = .007). At 8 weeks, 22 of 241 participants (9%) in the PETRUSHKA group discontinued the prescribed antidepressant due to adverse events vs 39 of 252 (16%) in the usual care group (adjusted relative risk, 0.59 [95% CI, 0.36 to 0.97]; P = .04). For the assessment of depressive symptoms at 24 weeks, the mean PHQ-9 score was 7.1 (SD, 5.4) in the PETRUSHKA group vs 9.2 (SD, 6.5) in the usual care group (n = 129 in each group; adjusted between-group mean difference, -1.92 [95% CI, -3.06 to -0.78]; P < .001). For the assessment of anxiety symptoms at 24 weeks, the mean GAD-7 score was 4.6 (SD, 4.1) in the PETRUSHKA group (n = 133) vs 5.8 (SD, 4.9) in the usual care group (n = 126) (adjusted between-group mean difference, -1.39 [95% CI, -2.26 to -0.52]; P = .002). CONCLUSIONS AND RELEVANCE: Compared with usual care, use of the PETRUSHKA tool increased the number of patients still taking their antidepressant at 8 weeks and improved depressive and anxiety symptoms at 24 weeks. However, lack of a double-blind design and the large amount of missing data limit the validity of these results. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05608330.
15 Apr 2026
Read appraisal →Tobacco control
Generative AI in a new era of computer model-informed tobacco research: a short report
BACKGROUND: Social media influencers who promote e-cigarettes on Instagram or TikTok for tobacco brands use marketing tactics to increase the appeal of their promotional content, for example, depicting e-cigarettes alongside healthy lifestyle or entertainment imagery that could decrease youths' risk perceptions of e-cigarettes. Monitoring the prevalence of such content on social media using computer vision and generative AI (artificial intelligence) can provide valuable data for tobacco regulatory science (TRS). METHODS: We selected 102 Instagram and TikTok videos posted by micro-influencers in 2021-2024 who promoted e-cigarettes alongside posts featuring four themes: cannabis, entertainment, fashion or healthy lifestyle. We used OpenAI's GPT-4o multimodal large-scale visual linguistic model to detect the presence of nicotine vaping, cannabis vaping, fashion, entertainment and healthy lifestyle. The model did not require any additional training and improved its performance as we modified the text prompt. RESULTS: The model's accuracy was 87% for nicotine vaping, 96% for cannabis vaping, 99% for fashion, 96% for entertainment and 98% for healthy lifestyle. CONCLUSIONS: Generative AI can achieve accurate object detection with zero-shot learning (no additional training of the pretrained model). This model can be applied to big data-scale sample sizes of images and videos to detect e-cigarette-related and other substance-related promotional content and contexts (eg, healthy lifestyle) used for the promotion of these products on social media, providing valuable data for TRS.
15 Apr 2026
Read appraisal →JMIR cancer
Physical Activity Monitoring in Adolescents and Young Adults With Cancer: Observational Feasibility Study
BACKGROUND: There is a growing interest in characterizing the relationship between long-term physical activity (PA) habits and cancer-related outcomes such as treatment-related toxicities, recurrence, and complications. Wearable devices can provide critical information to achieve this goal; however, inferences are significantly influenced by device wear adherence. OBJECTIVE: This study aimed to assess the feasibility of using wearable devices to monitor short- and long-term PA in adolescent and young adult (AYA) patients with sarcoma during and after chemotherapy in a free-living environment and evaluate the ability to accurately capture changes in PA over 3 years. METHODS: A total of 63 AYA patients with sarcoma were provided with a Fitbit Charge 3 to track daily steps, sedentary time, and heart rate for up to 3 years. RESULTS: On average, during the first 30 days of follow-up, 57.1% (36/63) of patients wore their device at least 10 hours per day, and only 23.8% (15/63) of patients wore their devices thereafter. Patients spent a mean of 80% (SD 11%) of their day in a sedentary state. Despite low adherence, daily step count trends increased over time. CONCLUSIONS: This study highlights the adherence challenges met with longitudinal PA monitoring in AYA patients with sarcoma. Wearer discomfort, lost devices, and lack of data uploading compliance contributed to data missingness and attrition. Caution is warranted when relying on wearable activity trackers to inform program decisions, accurately assess PA outcomes, and monitor program adherence longitudinally without consideration of wearer bias. Alternative methods that would be more broadly accepted by AYA patients for effective long-term monitoring should be considered.
15 Apr 2026
Read appraisal →JMIR aging
Web-Based Personalized Machine Learning Recommendations to Enhance Shared Decision-Making in Prostate-Specific Antigen Screening: Randomized Controlled Trial
BACKGROUND: Prostate‑specific antigen (PSA) screening involves complex trade‑offs between early detection and the risks of overdiagnosis. For older adults (aged ≥50 years), shared decision‑making (SDM) is often hindered by limited health literacy, sensory or cognitive impairments, and multimorbidity, which complicate risk comprehension. Traditional decision aids provide foundational knowledge but are often nonpersonalized. Machine learning (ML) may offer individualized recommendations, yet the psychological and behavioral effects of ML‑assisted SDM in geriatric populations remain poorly characterized. OBJECTIVE: This study aimed to develop and evaluate a web‑based, ML‑driven decision aid integrated into an SDM workflow to provide personalized PSA screening recommendations and to assess its effects on decisional conflict (primary outcome), state anxiety, and decision satisfaction among middle‑aged and older men. METHODS: The study followed a 2‑stage design. First, a model establishment group (n=507) was used to train and evaluate 6 ML algorithms based on clinical and values‑clarification data. A random forest model was selected for its superior performance (mean area under the curve 0.933, SD 0.350; 95% CI 0.902-0.963). Second, a randomized controlled trial was conducted with 367 participants (mean age 64.34, SD 10.30 years) randomly assigned 1:1 to the ML suggestion group (MLSG; n=185) or the control group (CG; n=182). Both groups received video‑based education, counseling, and values clarification; only the MLSG received an ML‑generated "second opinion" recommendation. Primary and secondary outcomes were assessed using the Decisional Conflict Scale (DCS), Spielberger State‑Trait Anxiety Inventory (STAI), and Satisfaction with Decision scale. RESULTS: In the randomized controlled trial (n=367), the MLSG reported significantly lower decisional conflict than the CG (total DCS score: mean difference [MD] -3.77, 95% CI -5.55 to -1.99; Cohen d=-0.44; P<.001). The MLSG reported greater perceived support (DCS7: adjusted P=.03), more adequate advice (DCS9: adjusted P<.001), and higher decision confidence (DCS10: adjusted P=.03; DCS11: adjusted P<.001). Regarding psychological well‑being, although total anxiety scores did not differ, the MLSG reported reduced worry (STAI item 6: MD -0.98, 95% CI -1.20 to -0.76; d=-0.89; adjusted P<.001) and increased calmness (STAI item 1: MD 0.30, 95% CI 0.06-0.54; d=0.25; adjusted P=.01). Decision satisfaction was higher in the MLSG across all items (total Satisfaction with Decision score: MD -7.38, 95% CI -8.54 to -6.18; P<.001). Behavioral choices were strongly influenced by the ML recommendation: participants in the MLSG who received an "accept" recommendation were more likely to select "accept" (34/67, 50.7%) than those in the CG (44/182, 24.2%; P<.001). When the system suggested "not now," only 17.8% (21/118) chose "accept," which was lower than in the CG. CONCLUSIONS: Integrating personalized ML recommendations into SDM workflows provides emotional scaffolding for older men, reducing decisional distress and enhancing confidence without undermining autonomy. By addressing geriatric‑specific vulnerabilities through a facilitated digital interface, this ML‑driven approach complements traditional clinical consultations. These findings support the scalable integration of artificial intelligence-assisted decision support to foster patient‑centered care in aging populations.
15 Apr 2026
Read appraisal →Journal of medical Internet research
Digital Monitoring of Pre-Exposure Prophylaxis Users Through Electronic Patient-Reported Outcome Measures and Electronic Patient-Reported Experience Measures: Multicenter Prospective Study on Feasibility, Safety, and Predictive Modeling of Digital Engagement
BACKGROUND: Digital health tools integrating electronic patient-reported outcome and experience measures (ePROMs/ePREMs) enable longitudinal monitoring of health-related quality of life (HRQoL), psychological well-being, and treatment satisfaction in pre-exposure prophylaxis (PrEP) users. However, determinants of sustained engagement with digital follow-up platforms remain insufficiently characterized. OBJECTIVE: To describe the feasibility of the Naveta-Phemium digital platform for longitudinal monitoring of safety, HRQoL, and treatment satisfaction among PrEP users, and to develop and internally validate a machine learning framework to characterize and predict engagement with digital follow-up. METHODS: A prospective observational study was conducted using the Naveta digital follow-up platform. HIV-negative adults at high risk of HIV infection received tenofovir disoproxil fumarate (245 mg) plus emtricitabine (200 mg). Clinical safety, HRQoL, and satisfaction were assessed using laboratory parameters and validated ePROMs/ePREMs (Hospital Anxiety and Depression Scale, Patient-Reported Outcome Measurement Information System Profile-29, Treatment Satisfaction Questionnaire for Medication, and Person-Centered Coordinated Care Experience Questionnaire). Engagement was defined at the questionnaire level and analyzed using the ALGOPROMIA-Classification framework with repeated stratified cross-validation. Model explainability was assessed using permutation-based Shapley Additive Explanations. RESULTS: A total of 81 participants contributed repeated questionnaire-level observations (mean PrEP duration 689 d). PrEP was well tolerated, with no moderate or severe adverse events; mild transient symptoms were mainly gastrointestinal (31/45, 68.9%) and neurological (26/45, 57.8%). Renal function remained stable (creatinine: 0.86 [SD 0.13] mg/dL; estimated glomerular filtration rate: P=.498). Psychological well-being and HRQoL remained stable (Hospital Anxiety and Depression Scale<7; Patient-Reported Outcome Measurement Information System Profile-29 near population norms). Treatment satisfaction was consistently high (Treatment Satisfaction Questionnaire for Medication≈85-87), and satisfaction with the NAVETA telemedicine model remained stable (8/10). Engagement showed clear sociodemographic and behavioral gradients. Ensemble-based machine learning models achieved good discrimination in predicting engagement (area under the curve≈0.82) across ≈12,300 questionnaire-level observations. Random forest was retained for robustness and consistency. Shapley Additive Explanations analysis highlighted lifestyle-related variables as the most influential predictors, with heterogeneous individual-level effects. CONCLUSIONS: Naveta enabled feasible telemedicine-based PrEP follow-up with preserved HRQoL, high satisfaction, and stable safety. Combining longitudinal ePROMs/ePREMs with explainable machine learning allowed detailed characterization of digital engagement, supporting digitally supported PrEP care optimization and informing future comparative and cost-effectiveness studies.
15 Apr 2026
Read appraisal →JMIR formative research
Comparing Large Language Models and Traditional Machine Translation Tools for Translating Medical Consultation Summaries: Quantitative Pilot Feasibility Study
BACKGROUND: Translation of medical consultation summaries is essential for equitable health care communication in culturally and linguistically diverse populations. While machine translation (MT) tools and large language models (LLMs) are widely accessible, their feasibility and safety for health care contexts remain underexplored. OBJECTIVE: This pilot study investigates the feasibility and limitations of using LLMs and traditional MT tools to translate medical consultation summaries from English into the most common languages other than English spoken in Australia-Arabic, Chinese (simplified written form), and Vietnamese. METHODS: Two simulated summaries-a simple patient-facing summary and a complex clinician-oriented interprofessional letter-were translated using 3 LLMs (GPT-4o, Llama-3.1, and Gemma-2) and 3 MT tools (Google Translate, Microsoft Bing Translator, and DeepL). Translations were benchmarked against professional third-party interpreter translations using Bilingual Evaluation Understudy, Character-level F-score, and Metric for Evaluation of Translation with Explicit Ordering metrics. RESULTS: The translation performance varied across languages, tools, and summary complexity when assessed using automatic evaluation metrics. Traditional MT tools outperformed LLMs on surface-level metrics, while LLMs showed relative strengths in semantic similarity for Vietnamese and Chinese. Arabic translations improved with complex input, suggesting morphological advantages. The metric-based evaluation highlighted feasibility but also risks, particularly in Chinese clinical contexts. CONCLUSIONS: This pilot study provides formative evidence of opportunities and limitations in applying artificial intelligence translation for health care communication. Findings underscore the importance of human oversight; domain-specific evaluation metrics; and further formative and clinical research to guide the safe, equitable use of artificial intelligence translation tools.
14 Apr 2026
Read appraisal →Journal of medical Internet research
Using Group Chats to Drive Behavior Change in Digital Health Interventions: Scoping Review and Realist Synthesis
BACKGROUND: Group chats on platforms such as WhatsApp (Meta Platforms, Inc), WeChat (Tencent Holdings Limited), and Telegram (Telegram FZ-LLC) are central to everyday communication in many settings, including across low- and middle-income countries and among groups often overlooked by one-to-one or app-based digital health tools. Yet their roles and underlying mechanisms as intentionally designed health interventions have not been comprehensively examined. OBJECTIVE: This scoping review with realist synthesis aimed to map the characteristics and reported outcomes of group chat-based health interventions and contextual conditions shaping their outcomes. It mapped intervention characteristics and identified context-mechanism-outcome (CMO) configurations driving behavior change. METHODS: We included empirical studies in which group chats were the primary digital component of the health intervention. Searches of PubMed, Embase, MEDLINE, Web of Science, and Scopus (2005-2026) and reference screening identified eligible studies. Data were extracted on study characteristics, health domains, settings, participants, platform, intervention features, and outcome domains. For the realist synthesis, CMO configurations were analyzed using reflexive thematic analysis informed by social cognitive theory and synthesized into a final program theory. RESULTS: Eighty-one studies were included. Publications increased sharply after 2020, with evidence clustering in mental health (25/81, 30.9%) and maternal and child health (20/81, 24.7%). Most studies enrolled adult participants, including pregnant or postpartum women, caregivers, and people managing chronic conditions. WhatsApp (38/81, 46.9%) and WeChat (21/81, 25.9%) were the most frequently studied platforms. Interventions were commonly delivered in clinical or community settings and typically involved mixed membership groups that included health professionals alongside participants (68/81, 84.0%). The realist synthesis identified 12 recurring CMO configurations across 5 domains: capability and actionability, confidence and motivation, modeling and norms, safe and supportive environment and access, and self-regulation and maintenance. These mechanisms were activated or suppressed by facilitation quality, group composition, cultural alignment, technological access, and social norms. The final program theory depicts group chats as dynamic systems where access, facilitation, and group structure shape how the domains amplify or dampen one another, explaining shifts from low engagement to high trust spaces sustaining behavior change. CONCLUSIONS: This review integrates descriptive mapping of interventions with realist program theory to explain how context and facilitation activate mechanisms of change, unlike prior reviews that have focused narrowly on effectiveness of digital modalities. This dual approach provides a practical, mechanism-focused basis for designing and scaling group chat interventions in practice, particularly given their potential as a low-cost, high-reach strategy embeddable within broader digital health programs. Realizing this potential depends on treating group chats as purposefully designed social environments, with deliberate attention to facilitation quality, equity-oriented access, and alignment between group structure, norms, and communication practices.
14 Apr 2026
Read appraisal →Neurology
Wearable Technology and Its Role in Neurologic Care: Emerging Issues in Neurology
Consumer wearable devices are commonly used by patients and consumers for several reasons with increasing application as new technologies are developed. Use of these devices is an emerging issue in Neurology because of increased adoption and the additional data reported to providers by patients. Understanding of possible functions, limitations, and effect on patients of non-US Food and Drug Administration (FDA)-cleared wearable technology to inform neurologic care is needed. A common theme in people with neurologic conditions regarding consumer wearables and associated tracking applications is that there is significant promise in these tools, but adherence (days per use/per week), continued engagement (attrition), and unintended consequences such as heightened anxiety remain important issues. Further understanding and validation of these devices is needed within the field of Neurology before full use and confidence can be achieved. Below, we provide examples of non-FDA-cleared wearable devices used in Neurology in the areas of epilepsy, headache, cardiac monitoring, and sleep.
14 Apr 2026
Read appraisal →JMIR aging
Personalized Digital Care Program Allocation for Older Adults: Reinforcement Learning-Based Simulation Study
BACKGROUND: As the demand for innovative older adult care grows alongside a shortage of care workers, personalization is key to optimizing services and enhancing long-term sustainability. This study proposes an adaptive reinforcement learning (RL)-based framework to promote precision digital care by dynamically assigning care programs based on individuals' unique characteristics and evolving needs. Its effectiveness was evaluated through simulation-based experiments comparing multiple allocation methods within an artificial intelligence (AI)-powered care call service for older adults. OBJECTIVE: This study aimed to develop and evaluate an RL-based model for personalizing digital care program allocation to optimize care engagement and health outcomes among low-income older adults living alone. METHODS: We developed the framework by using contextual bandits, specifically Thompson Sampling, to maximize user outcomes. Four program allocation strategies were tested using a synthetic dataset of user features and program attributes. Simulations were conducted over multiple iterations to evaluate how the model adapts over time and optimizes program assignments compared with static methods. RESULTS: Four program allocation methods were compared across 100 simulation runs (n=3000 assignments per run) using 2 datasets (AI Call: n=1196; Community Health Survey [CHS]: n=72,812): (1) systematic allocation (baseline), (2) single best program based on population average, (3) idealized personalized delivery (theoretical upper bound), and (4) precision digital care using Thompson Sampling. Precision digital care outperformed baseline and population-average approaches, achieving outcomes comparable to the theoretical upper bound. Compared to systematic allocation, call success rates increased by 84.2% (AI Call) and 54.4% (CHS), Patient Health Questionnaire-2 depression scores decreased by 32.1% (AI Call) and 41.4% (CHS), and self-reported health scores improved by 19% (AI Call) and 22% (CHS). It also showed improved learning efficiency, refining assignments dynamically as it learned from user responses. CONCLUSIONS: Our findings emphasize the importance of personalization in digital care. We plan to refine and validate the model through a publicly funded AI care program for community-dwelling, low-income older adults living alone in the Republic of Korea. RL offers a scalable and effective approach to advance precision digital care delivery and support future innovations in aging services.
14 Apr 2026
Read appraisal →Biomedical physics & engineering express
A multi-task cross-attention strategy to segment and classify polyps
Polyps are the main biomarkers for diagnosing colorectal cancer. Their early detection and accurate characterization during colonoscopy procedures rely on expert observations. Nevertheless, such a task is prone to errors, particularly in morphological characterization. This work proposes a multi-task representation capable of segmenting polyps and stratifying their malignancy from individual colonoscopy frames. The approach employs a deep representation based on multi-head cross-attention, refined with morphological characterization learned from independent maps according to the degree of polyp malignancy. The proposed method was validated on the BKAI-IGH dataset, comprising 1200 samples (1000 white-light imaging and 200 NICE samples) with fine-grained segmentation masks. The results show an average IoU of 83.5% and a recall of 94%. Additionally, external dataset validation demonstrated the model's generalization capability. Inspired by conventional expert characterization, the proposed method integrates textural and morphological observations, allowing both tasks, polyp segmentation and the corresponding malignancy stratification. The proposed strategy achieves the state-of-the-art performance in public datasets, showing promising results and demonstrating its ability to generate a polyp representation suitable for multiple tasks.
14 Apr 2026
Read appraisal →JMIR medical informatics
Responsible AI for Predicting Delayed Hospital Discharge Among Older Adults: Development and Evaluation Study for Balancing Accuracy, Equity, and Explainability
BACKGROUND: Amid growing demands and constrained health care resources, effective hospital bed capacity management is crucial. Delayed hospital discharge, where patients remain in the hospital beyond the need for acute care, strains resources, affects patient outcomes, and reduces system efficiency. Predicting such delays facilitates early interventions to avert them and alleviate burdens on patients, care partners, hospitals, and the broader health care system. OBJECTIVE: This study aimed to develop comprehensive predictive analytics for delayed discharges among older adults using explainable machine learning to boost transparency and interpretability, while integrating fairness to mitigate algorithmic biases. METHODS: Leveraging longitudinal data from over 2 decades in Ontario, Canada, we applied extreme gradient boosting and logistic regression models to predict delayed discharges within 90 days post-acute care. Data preprocessing included a 2-year look-back for clinical histories and balanced sampling to address class imbalance. Model performance was assessed via area under the receiver operating characteristic curve, calibration, and clinical utility. Fairness was evaluated across sex, urban or rural residence, and residential instability using several threshold-free metrics. Explainability was examined at the global model level (via partial dependence plots and permutation feature importance) and locally (via Shapley Additive Explanations, breakdown, and ceteris paribus methods), with principal component analysis used to cluster key features for high-risk patients. RESULTS: The extreme gradient boosting model outperformed logistic regression, achieving an area under the receiver operating characteristic curve of 0.82 on the test set, with acceptable within-group and cross-group ranking fairness across subgroups. Explainability clustering analyses identified functional and cognitive declines (eg, care support needs, dementia, and mobility issues) and regional disparities as primary drivers of high-risk predictions. Bias mitigation improved calibration parity, especially when stratifying by residential instability, underscoring the trade-offs policymakers must weigh between accuracy, fairness, and explainability. CONCLUSIONS: This study demonstrates the potential of responsible artificial intelligence in health care, emphasizing the need to balance predictive accuracy, equity, and interpretability. It uncovers systemic gaps and offers actionable insights for enhanced discharge planning, resource optimization, and equitable care delivery.
14 Apr 2026
Read appraisal →Biomacromolecules
Highly Flexible, Adhesive, Antimicrobial, and Self-Powered Hydrogel Strain Sensor for Human Motion Monitoring
Piezoelectric hydrogel sensors are a recent innovation that combines the flexibility of hydrogels with the self-powering of piezoelectric materials. Here, we present a self-powered hydrogel strain sensor by incorporating piezoelectric Na0.5Bi0.5TiO3 nanoparticles into a dual-network hydrogel composed of poly(vinyl alcohol), acrylamide, and 2-acrylamido-2-methyl-1-propanesulfonic acid. The hydrogel exhibits excellent mechanical properties, achieving a tensile strength of 1.28 MPa, an elongation at break of 1127%, and strong tissue adhesion (55.97 kPa on porcine skin). The sensor delivers a maximum output voltage of 237.4 mV, a sensitivity of 17.99 mV under small compressive strain, and a broad tensile strain detection range of 30 to 400%. It reliably monitors both subtle physiological activities (facial expression, swallowing, pulse) and large joint movements. Moreover, it exhibits notable antibacterial performance via ultrasound-driven piezocatalysis, reducing bacterial viability to below 1.5%. This tough, adhesive, and antibacterial piezoelectric hydrogel sensor holds significant promise for wearable electronics and human-machine interfaces.
14 Apr 2026
Read appraisal →Psychological medicine
Cost-utility and cost-effectiveness of SELFIE, a transdiagnostic ecological momentary intervention for improving self-esteem in youth aged 12-26 years exposed to childhood adversity: Findings from a multicenter randomized controlled trial
BACKGROUND: A novel transdiagnostic blended Ecological Momentary Intervention (EMI) for improving self-esteem in youth who experienced childhood adversity has shown beneficial effects. However, evidence on the cost-effectiveness of SELFIE and EMIs overall is lacking. METHODS: In this RCT-based economic evaluation, participants aged 12-26 years with low self-esteem (<26 on the Rosenberg Self-Esteem Scale [RSES]) and experienced childhood adversity were recruited from specialized mental health services and the general population and randomly allocated to CAU (control condition) or CAU + SELFIE (experimental condition). The Trimbos/iMTA Questionnaire for Costs Associated with Psychiatric Illness (TiC-P), the RSES, and EQ-5D-5L were assessed at baseline, post-intervention, and 6-, 18-, and 24-month follow-up. Incremental cost-effectiveness (ICER) and cost-utility (ICUR) ratios, and acceptability curves, were generated. Sensitivity and subgroup analyses assessed robustness. RESULTS: From a societal perspective, the ICER presented €1,219 per improved point of self-esteem, and the ICUR presented €53,986 per QALY gained. The probability of cost-effectiveness was 26% at a willingness-to-pay (WTP) threshold of €20,000/QALY and 49% at €50,000/QALY. At €50,000/QALY, the probability of cost-effectiveness was 77% from a healthcare perspective (ICUR €15,671/QALY gained) and 87% for participants recruited from specialized mental health services (ICUR €-14,567/QALY gained). CONCLUSIONS: At the societal level, the SELFIE intervention exceeded the WTP threshold. Tests for robustness showed higher probabilities of cost-effectiveness from a healthcare perspective, likely reflecting the absence of educational productivity loss costs in the societal perspective, and in youth recruited from specialized mental health services. The innovative field of blended EMIs yields encouraging first results, urging more cost-effectiveness research.
14 Apr 2026
Read appraisal →Psychological medicine
AVATAR versus cognitive-behavioral therapy for medication-resistant auditory hallucination: a systematic review and network meta-analysis
Auditory hallucinations (AH) frequently persist in schizophrenia spectrum disorder despite antipsychotic treatment. Cognitive behavioral therapy (CBT) remains an established psychological intervention, whereas AVATAR (Audio Visual Assisted Therapy Aid for Refractory auditory hallucinations) therapy has recently been introduced as a novel approach integrating interactive digital avatars. This meta-analysis compared the efficacy of AVATAR therapy with CBT for medication-resistant AH. A systematic search of five major databases up to June 1, 2025 identified randomized controlled trials (RCTs) that evaluated either therapy. The primary outcome was AH severity, and secondary outcomes included psychotic symptoms, mood measures, and all-cause discontinuation. Twenty-six RCTs (n = 2273; 65.0% male; mean age 39.3 [SD 4.1] years) met inclusion criteria. Compared with CBT, AVATAR therapy showed no significantly greater reduction in AH severity (standardized mean difference [SMD] = -0.23, 95% confidence interval [CI] = -0.55 to 0.10). However, it demonstrated superior sustained improvement three months post-treatment (SMD = -0.37, 95% CI = -0.69 to -0.05) and greater reduction in overall psychotic symptoms (SMD = -0.41, 95% CI = -0.75 to -0.06). No significant differences were observed in positive, negative, depressive, anxiety, or quality-of-life outcomes, and discontinuation rates were comparable. Interpretation should be cautious given evidence of small-study effects (Egger's tests p < 0.01 for AH severity) and predominantly moderate-to-high risk of bias across included trials. AVATAR therapy therefore shows lasting efficacy, comparable or slightly superior to CBT, and may serve as an alternative for patients with medication-resistant AH.
14 Apr 2026
Read appraisal →Annals of plastic surgery
Evaluating GPT-4 Performance on Plastic Surgery Oral Examination Vignettes: A Quantitative and Qualitative Analysis
BACKGROUND: The use of artificial intelligence (AI) in medicine is rapidly evolving. However, its role in plastic and reconstructive surgery remains underexplored. Plastic surgery requires nuanced, dynamic decision making, and individualized care, making AI integration challenging. This study evaluates GPT-4's ability to respond to American Board of Plastic Surgery (ABPS) Oral Board-style clinical vignettes and assesses its potential as a decision making and educational adjunct. METHODS: Twelve clinical vignettes spanning aesthetic, reconstructive, hand, craniofacial, pediatric, and trauma surgery were input into GPT-4. Each response was scored by 2 board-certified plastic surgeons across 6 clinical domains: Case Introduction, Diagnosis, Treatment Planning, Patient Counseling, Operative Steps, and Complication Management. Domains were scored on a 0-4 scale; scores <2 were considered failing. Interrater reliability was assessed via weighted κ . A structured qualitative interview was conducted. RESULTS: GPT-4 achieved mean domain scores ranging from 2.3 to 3.7 across vignettes, with passing rates of 91.7% (rater 1) and 83.3% (rater 2). The highest-performing domains were Diagnosis (84.4%) and Complication Handling (82.3%), and the lowest-performing domains were seen in Case Introductions (62.5%). Interrater agreement was strong across domains ( κ = 0.70-0.89). Qualitative findings emphasized GPT-4's accuracy in guideline-based cases, limitations in trauma triage, and potential as an educational resource. CONCLUSION: GPT-4 demonstrates concordance with board-level clinical reasoning in structured plastic surgery scenarios. However, limitations in heuristic judgment and real-time adaptability underscore the need for optimization with specialty-specific training datasets and performance rubrics. With appropriate curation, GPT-4 could serve as a valuable supplement in surgical education and oral board preparation.
13 Apr 2026
Read appraisal →Medicine
Bibliometric and visual analysis of artificial intelligence-related research in osteoarthritis: Trends and frontiers (2008-2025)
Osteoarthritis (OA) causes joint pain, functional impairment, and significantly impacts patients' quality of life. Recent advancements in artificial intelligence (AI) technology have provided new opportunities for OA diagnosis, treatment, and management. This study aims to understand the knowledge structure and research frontiers of "AI and OA" using bibliometric methods. Literature on "AI and OA" from January 1, 2008, to March 31, 2025, was retrieved from the Web of Science Core Collection database. VOSviewer, CiteSpace, and the R package "bibliometrix" were used for analysis. The study included 659 articles from 59 countries. Since 2019, the number of scientific outputs related to OA and AI has increased annually. The United States, China, and the United Kingdom are the main contributing countries, with Harvard University, Seoul National University, and the University of California system as leading institutions. The "JOURNAL OF ARTHROPLASTY" and "OSTEOARTHRITIS AND CARTILAGE" are the most contributing journals. Among the 2404 authors, GUERMAZI A and ROEMER FW are the most influential. Thematic trend analysis shows that AI and machine learning are driving new trends in OA research, especially in diagnosis, treatment planning, and patient monitoring. This paper summarizes the bibliometric trends and development in "AI and OA" studies over 18 years, identifying the latest research frontiers and hot topics. It provides valuable references for related research and highlights the importance of interdisciplinary collaboration as research topics diversify.
12 Apr 2026
Read appraisal →PloS one
Privacy-preserving multimodal federated learning pipeline for cyber-resilient healthcare systems
The integration of Internet of Things (IoT) devices and electronic medical records (EMRs) has transformed healthcare delivery but has also created new vulnerabilities to cyberattacks that threaten both data confidentiality and patient safety. Conventional centralized machine learning approaches for intrusion detection are impractical in this domain due to strict privacy regulations, heterogeneous data sources, and the risk of single points of failure. To address these challenges, we propose a secure distributed machine learning pipeline for cyber-resilient healthcare systems. The framework combines federated optimization with split learning for sensitive EMR data, robust aggregation to mitigate poisoned updates, and differential privacy with secure aggregation to protect against inference attacks. Multimodal fusion is enabled through temporal consistency regularization for IoT traffic and cross-layer contrastive alignment to link EMR representations, ensuring improved anomaly detection across diverse healthcare environments. Experiments conducted on representative IoT and EMR datasets demonstrate that the proposed pipeline achieves accuracy of 0.942 on IoT data, 0.931 on EMR data, and 0.953 in the combined setting, with corresponding F1-scores of 0.921, 0.908, and 0.932. Ranking metrics further confirm superiority with AUROC up to 0.961 and AUPRC up to 0.947, outperforming deep baselines by margins of +0.025 to +0.033. Robustness analysis shows graceful degradation under client poisoning ([Formula: see text] at 30% malicious clients) and resilience under severe communication constraints (accuracy [Formula: see text] at 90% update sparsification). Detection latency is reduced to an average of 5.9 time steps, compared to 7.8 for the strongest deep baseline. These results highlight that secure distributed pipelines can deliver both strong detection capabilities and regulatory compliance, providing a practical path toward safeguarding next-generation healthcare infrastructures against evolving cyber threats.
12 Apr 2026
Read appraisal →Journal of medical Internet research
Large Language Model-Based Analysis of Statin Therapy Discussions and Sentiment on Social Media: Cross-Sectional Observational Study
BACKGROUND: Statin therapy, despite proven cardiovascular benefits, remains underused. Social media platforms may capture patient perspectives that are less visible in clinical encounters. OBJECTIVE: This study aimed to characterize themes, sentiment, and decision-making factors related to statin therapy through large language model (LLM)-based analysis of Reddit discussions. METHODS: This cross-sectional observational study analyzed English-language Reddit posts and comments mentioning statins from January 2022 to May 2025, identified via keyword-based Reddit application programming interface searches (≤1000 posts per keyword). A total of 5328 retrieved discussions (n=1661, 31.2% posts and n=3667, 68.8% keyword-containing comments) from public subreddits were included. Themes, sentiments (positive, neutral, or negative), guideline-informed clinical relevance, information-seeking behavior, adverse effect mentions, decision factors, and adherence-related content were extracted using an LLM-based pipeline. RESULTS: Among 5328 discussions, prominent topics included adverse effects (n=1697, 31.9%), decision-making references related to laboratory results and physician advice (n=2767, 51.9% and n=2034, 38.2%, respectively), and alternative approaches (n=2485, 46.6%). Overall sentiment was neutral in 34% (n=1812) of discussions, negative in 30.9% (n=1646), and positive in 16.9% (n=900); the remainder were mixed or unclear. Statin-directed sentiment was neutral in 44.1% (n=2350) of discussions, negative in 25.2% (n=1343), and positive in 12.5% (n=666); the remainder did not express statin-directed sentiment. High clinical relevance was identified in 12.6% (n=672) of discussions. Adherence-related issues were mentioned in 29.8% (n=1587) of discussions. Among adverse effect mentions, muscle pain (n=129, 7.6%) and fatigue (n=110, 6.5%) were common. CONCLUSIONS: LLM-enabled analysis of Reddit discourse highlights substantial negative sentiment, adherence-related concerns, and adverse effect narratives surrounding statin therapy. These findings suggest opportunities for patient-centered communication and shared decision-making strategies that address symptom attribution, uncertainty, and information needs in digital information environments.
12 Apr 2026
Read appraisal →Journal of medical Internet research
Beyond GPT-4: The Rapidly Evolving Potential of Large Language Models for Clinical Guideline Improvement
This commentary reviews the study by Jones et al, which evaluated whether GPT-4 could improve the readability of injectable medication guidelines while preserving important safety information. The study found that GPT-4 produced modest readability gains comparable to manual revision, but also introduced omissions and meaning changes in a minority of sections. These findings highlight both the potential and limitations of early large language models (LLMs) in clinical contexts. However, this study reflects the capabilities of a specific model in a rapidly evolving domain. Since the release of GPT-4, advances in multistep reasoning, model-critique workflows, and structured validation have substantially improved the ability of newer systems to detect omissions, maintain factual fidelity, and support controlled editing. As a result, some documented limitations may stem from the constraints of a single-model, single-pass workflow rather than intrinsic flaws in LLM-assisted guideline revision. This commentary highlights the need for evaluation frameworks that can keep pace with LLM progress and emphasizes that clinical oversight and user-centered testing remain essential. Updated research using contemporary models is needed to determine how emerging architectures can more safely support clarity, consistency, and maintenance of clinical guidelines.
12 Apr 2026
Read appraisal →PloS one
Predicted meta-omics: A potential solution to multi-omics data scarcity in microbiome studies
Imbalances in the gut microbiome have been linked to conditions such as inflammatory bowel disease, diabetes, and cancer. While metagenomics and amplicon sequencing are commonly used to study the microbiome, they do not capture all layers of microbial functions. Other meta-omics data can provide more insights, but these are more costly and laborious to procure. The growing availability of paired meta-omics data offers an opportunity to develop machine learning models that can infer connections between metagenomics data and other forms of meta-omics data, enabling the prediction of these other forms of meta-omics data from metagenomics. We evaluated several machine learning models for predicting meta-omics features from various meta-omics inputs. Simpler architectures such as elastic net regression and random forests generated reliable predictions of transcript and metabolite abundances, with correlations of up to 0.77 and 0.74, respectively, but predicting protein profiles was more challenging. We also identified a core set of well-predicted features for each meta-omics output type, and showed that multi-output regression neural networks performed similarly when trained using fewer output features. Lastly, our experiments demonstrated that predicted features can be used for the downstream task of inflammatory bowel disease classification, with performance comparable to that of experimental data.
11 Apr 2026
Read appraisal →Journal of medical Internet research
Views of People With Psychosis About Algorithm-Based Relapse Prediction and Data Sharing: Qualitative Study
BACKGROUND: Preventing relapses of psychosis is difficult and important. Digital remote monitoring (DRM) systems are being developed and tested to support this. Increasingly, these systems use algorithm-based relapse prediction. Hence, understanding stakeholder views about algorithmic prediction is crucial. Existing qualitative work has explored health professionals' views, but very few studies have examined the perspectives of people with psychosis on this topic. OBJECTIVE: This paper aimed to provide an in-depth examination of the views of people with psychosis regarding algorithmic relapse prediction within a DRM system that incorporates active symptom monitoring and passive sensing data. METHODS: People with psychosis (n=58) were recruited from 6 geographically distinct areas of the United Kingdom. They participated in semistructured qualitative interviews exploring their views about using a DRM system that predicts psychosis relapse based on a machine learning algorithm. Transcripts were analyzed using reflexive thematic analysis. People with lived experience of psychosis were involved extensively in study design, analysis, and reporting. RESULTS: Findings were described across 4 themes. First, accuracy was a prominent theme. Participants emphasized that transparency about algorithm sensitivity and specificity is crucial and discussed the risks of the relapse prediction algorithm producing false positives (flagging that someone was relapsing when they were not) and false negatives (missing actual relapses). In both cases, participants said that errors may be partially mitigated through a human-in-the-loop approach (theme 2), with DRM blended with human oversight, from clinicians or a dedicated digital monitoring team, and calibrated based on service user, carer, and clinician feedback. The third theme, trust, fears, and choice, noted the interplay between users' trust in the DRM system and their relationship with the clinical team. This theme described participants' fears about potential overreactions (hospitalization or excessive medication) or underreactions (no additional support) from the clinical team in response to algorithm-generated relapse predictions. It emphasized the importance of retaining choice around the use of relapse detection algorithms and the sharing of personal data. The final theme described participants' views about the benefits of using a relapse prediction algorithm, including facilitating early intervention, triaging care according to need, minimizing human bias in assessment, and efficiency in saving staff time. CONCLUSIONS: People with psychosis acknowledged potential benefits of algorithm-assisted relapse prediction for receiving timely or efficient care, but with several caveats. Algorithm-generated relapse alerts need to be sufficiently accurate and must be interpreted, with understanding of their limitations, by a trustworthy human who is aware of the relevant context. Algorithm-based relapse predictions should only be used with valid consent, in a way that promotes and respects the autonomy and voice of service users and avoids increasing the use of excessive restriction.
11 Apr 2026
Read appraisal →Journal of medical Internet research
Context-Aware Sentence Classification of Radiology Reports Using Synthetic Data: Development and Validation Study
BACKGROUND: Automated structuring of radiology reports is essential for data utilization and the development of medical artificial intelligence models. However, manual annotation by experts is labor-intensive, and processing real clinical data through commercial large language models (LLMs) presents significant privacy risks. These challenges are particularly pronounced for non-English languages like Japanese, where specialized medical corpora are scarce. While synthetic data generation offers a potential privacy-preserving alternative, its effectiveness in capturing complex clinical nuances-such as negation and contextual dependencies-to train robust classification models without any real-world training data has not been fully established. OBJECTIVE: This study aimed to develop a context-aware sentence classification model for Japanese radiology reports using an entirely synthetic training pipeline, thereby eliminating reliance on real-world clinical data during the development phase. Furthermore, we sought to evaluate the generalizability of this approach by validating the model's performance on diverse, multi-institutional, real-world reports. METHODS: Japanese radiology reports (n=3104) were generated using GPT-4.1 and automatically annotated at the sentence level into 4 categories (background, positive finding, negative finding, and continuation) using GPT-4.1-mini. The synthetic data were partitioned into training (n=2670), validation (n=334), and test (n=100) sets. We fine-tuned several models, including lightweight local LLMs (Qwen3 and Llama 3.2 series) using low-rank adaptation and Japanese text classification models (Bidirectional Encoder Representations from Transformers [BERT]-base Japanese v3, Japanese Medical Robustly Optimized BERT Pretraining Approach [JMedRoBERTa]-base, and ModernBERT-Ja-130M). External validation was performed using 280 real-world reports (3477 sentences) from 7 institutions in the Japan Medical Image Database, with ground-truth labels established by board-certified radiologists. Evaluation metrics included accuracy, macro-averaged F1 (macro F1) score, and positive predictive value for positive findings (PPV_1). RESULTS: All models achieved high performance on the synthetic test set (accuracy: 0.938-0.951; macro F1-score: 0.924-0.940). Overall performance declined on the external validation dataset (accuracy: 0.783-0.813; macro F1-score: 0.761-0.790), reflecting distributional differences between synthetic and real-world reports; however, PPV_1 remained stable and high across datasets (eg, 0.957 on the synthetic test set vs 0.952 on the external validation dataset for Qwen3 [4B]). Parsing errors occurred in LLM-based approaches (19-260 sentences, 0.55%-7.48% in the external dataset). CONCLUSIONS: This study demonstrates the feasibility of developing context-aware sentence classification models for Japanese radiology reports using a training pipeline based entirely on synthetic data. The stability of PPV_1 indicates that the models successfully captured the essential clinical terminology and linguistic patterns required to identify positive findings in real-world reports, despite the observed performance degradation during external validation. This approach substantially reduces manual annotation requirements and privacy risks, providing a scalable foundation for constructing structured radiology datasets to support the development of clinically relevant medical artificial intelligence models.
11 Apr 2026
Read appraisal →Open heart
Effect of a mobile health intervention optimised with artificial intelligence on blood pressure and other cardiovascular risk factors in adults with high blood pressure: rationale and design of My Intelligent Cardiac Assistant (MICArdiac) randomised controlled trial
INTRODUCTION: Control of blood pressure (BP) continues to be a challenge globally. Clinical trials have shown home BP monitoring and text-message interventions to lower BP. Integrating these to personalise supportive messaging in response to changing BP and activity through leveraging artificial intelligence (AI) could improve BP control. The aim of this trial is to examine the impact on BP, compared with a text-message only programme, of the My Intelligent Cardiac Assistant (MICArdiac) programme. MICArdiac is a 6-month programme comprising tracking of BP, physical activity and heart rate informing a content stream of AI-driven personalised messages to encourage BP self-management, as-necessary medical review to up-titrate medicines and cardiovascular preventative behavioural change. METHODS AND ANALYSIS: MICArdiac is a prospective randomised open-blinded endpoint randomised controlled trial with 1:1 (intervention:control) allocation and active control. Individuals aged 35 years old or above with high BP are randomised to the MICArdiac programme or to a text-message cardiovascular education programme. Recruitment started in primary care and hospital clinics in Western Sydney and moved to decentralised direct-to-community recruitment in the Australian Capital Territory, New South Wales and Victoria. Randomisation and allocation concealment occur via a secure web-based system. The primary outcome is mean daytime systolic BP measured by 24-hour Ambulatory Blood Pressure Monitoring at 6 months. Primary analysis will follow intention-to-treat principles; data analysts will be blinded. Process evaluation will be conducted. ETHICS AND DISSEMINATION: Ethics approval was obtained from Western Sydney Local Health District Human Ethics Research Committee (2021/ETH11379). TRIAL REGISTRATION NUMBER: ACTRN12622000091707.
11 Apr 2026
Read appraisal →JMIR medical informatics
Code-Based Versus AutoML Methods for Pill Recognition in Clinical Settings: Comparative Performance Study
BACKGROUND: Visual identification and verification of medications during dispensing and administration are prone to human error, particularly in high-pressure and high-volume clinical settings. Misidentification can lead to medication errors, posing risks to patient safety and placing a burden on health care systems. Recent advances in computer vision and object detection offer promising solutions for automated solid oral dosage form (pill) recognition. However, comprehensive studies comparing code-based and no-code (automated machine learning [AutoML]) approaches for pill recognition are lacking. OBJECTIVE: This study aimed to evaluate and compare the performance, cost, usability, and deployment feasibility of pill recognition models developed with Ultralytics YOLO11 and 3 cloud-based AutoML platforms (Amazon Rekognition Custom Labels, Google Vertex artificial intelligence [AI] AutoML Vision, and Microsoft Azure Custom Vision) using multiple datasets, including real-world clinical images. METHODS: Five training subsets of increasing size (1230, 3450, 7380, 14,400, and 26,880 images) from 30 commonly dispensed medications were used to train models on YOLO11 and 3 AutoML platforms. Models were evaluated on 6 datasets from different environments: clinical images from 3 hospitals, a verification dataset, a laboratory dataset, and an exhaustive testing set. Performance metrics, including accuracy, precision, recall, and mean average precision, were calculated. We evaluated the impact of training data size on performance and benchmarked training time, platform costs, and limitations. RESULTS: No single platform dominated across all test environments. On the verification dataset (optimal conditions), accuracy ranged from 80.83% (YOLO11) to 91.60% (Google Vertex AI) when trained with the full training dataset. YOLO11 showed consistent performance improvement with increasing training data (accuracy: 63.06%-80.83%) and achieved near-perfect precision and mean average precision scores (0.95-1.00). Google Vertex AI reached above 90% accuracy on 3 training subsets but showed unpredictable declines. Amazon Rekognition maintained near-perfect precision (0.92-1.00) but had the highest false negative rates (up to 0.74), missing many pills. Custom Vision demonstrated steady performance improvements (77.08%-85.62% accuracy) but lagged behind other AutoML platforms, probably due to its older YOLOv2-based architecture. On clinical datasets, accuracy fluctuated (20.62%-90%) depending on the dataset and platform. Training costs and time varied: YOLO11 (open-source), Microsoft Azure (US $9.50-US $28.60, allowed user-predefined training duration), Google Vertex AI (US $69.30 with consistent 2.5-3-hour training times), and Amazon Rekognition (US $5.43-US $43.89 with size-dependent training time scaling, reaching nearly 40 hours on the full 26,880-image dataset). CONCLUSIONS: Each platform offers distinct advantages and trade-offs: YOLO11 provides the highest flexibility and lowest platform costs but requires technical expertise, while AutoML platforms can offer high performance at a higher cost but with limited user control, introducing unpredictability. The performance variations demonstrate that successful clinical deployment requires careful platform selection based on specific performance requirements, budget constraints, and available technical resources, followed by rigorous validation using real-world, representative data to ensure patient safety in clinical workflows.
11 Apr 2026
Read appraisal →JMIR aging
Facilitators and Barriers to Digital Self-Management in Older Adults With Depression: COM-B and Theoretical Domain Framework Qualitative Study
BACKGROUND: Depression in older adults presents unique challenges in self-management. Digital tools, such as mobile health (mHealth) apps, have the potential to support this population. This study explored the facilitators and barriers to digital self-management in older adults with depression to inform the design of effective mHealth apps. OBJECTIVE: This study aims to explore the facilitators and barriers to digital self-management in older patients with depression with the aim of informing the design and development of mHealth apps for older adults. METHODS: A purposive sampling method was used to recruit 25 older patients with depression from July to September 2024. Semistructured interviews were conducted to capture real-life experiences. Directed content analysis ensured objective and accurate data interpretation, and the Capability, Opportunity, Motivation, and Behavior (COM-B) model and the Theoretical Domains Framework were applied to identify facilitators and barriers related to behavior. RESULTS: Six themes were identified based on the COM-B model and the Theoretical Domains Framework: perception of illness and accumulation of personal experience; dual challenges of cognitive abilities and physical limitations; integration of digital technologies and acceptance differences; social influences and access to and utilization of support resources; environmental constraints and accommodations; and intertwined influences of beliefs, emotions, and motivation. A total of 13 barriers and 11 facilitators were identified. CONCLUSIONS: Digital self-management in older adults with depression is complex and influenced by multiple interrelated factors. Effective mHealth apps must integrate the cognitive, emotional, and social contexts of patients to provide user-friendly, personalized solutions.
11 Apr 2026
Read appraisal →Journal of medical Internet research
Digital Health Technology Use Among Rehabilitation Professionals in China: Multi-Province Cross-Sectional Survey
BACKGROUND: The rapid expansion of rehabilitation needs in China has intensified pressure on a workforce that remains unevenly distributed. Digital health technologies (DHTs) offer potential to increase service reach and efficiency. However, little is known about how rehabilitation professionals currently gather and document clinical information, nor about their readiness to integrate digital tools into routine practice within China's rapidly digitalizing health system. OBJECTIVE: This study aimed to describe how rehabilitation professionals in China collect subjective and objective clinical information, document patient data in routine practice, and assess their willingness to use DHTs in clinical settings. METHODS: We conducted a multi-province observational cross-sectional survey using a culturally adapted questionnaire based on the World Health Organization Digital Health Interventions framework. The instrument assessed participant characteristics, information collection methods, documentation practices, and willingness to adopt digital functions across rehabilitation activities. Descriptive analyses and subgroup comparisons were performed on 324 complete responses from certified rehabilitation professionals. The multi-province cross-sectional online survey was conducted among licensed rehabilitation professionals in China with internet access. Participants were recruited through professional networks and social media platforms. RESULTS: Respondents were drawn from 20 provincial-level administrative regions across China, including Fujian (n=72), Guangdong (n=77), and Shanxi (n=45), among others, with 82.7% (268/324) employed in public sector rehabilitation services. Traditional methods dominated clinical work. Face-to-face communication was used frequently for subjective assessment by 96.3% (312/324) of respondents, whereas digital channels such as email (22/324, 6.8%) and telephone (47/324, 14.5%) saw limited use. For objective information, visual observation (271/324, 83.7%) and manual measurement tools (195/324, 60.2%) remained the primary approaches, while motion capture technology (45/324, 13.8%) and wearable sensors (13/324, 4%) were rarely used. Documentation practices also relied heavily on analogue formats, with 82.1% (266/324) using handwritten notes and 60.2% (195/324) using paper templates. In contrast, willingness to adopt DHTs was consistently high, with 80.6% (261/324) of respondents indicating readiness to use digital systems for identity verification, 79.0% (256/324) for progress tracking, and 78.1% (253/324) for outcome measurement. Subgroup analyses revealed that educational level significantly influenced the adoption of advanced technologies, with master's or doctoral degree holders reporting higher use of sensor-based assessment, motion capture, and wearable devices. In contrast, professional title and clinical specialty showed limited influence, with no significant differences observed for most digital health functions. CONCLUSIONS: Rehabilitation professionals in China demonstrate strong readiness to use DHTs, yet their routine practice remains largely paper-based and analogue. These findings provide evidence to inform implementation strategies, workforce training, and system-level planning aimed at accelerating digital transformation in rehabilitation services.
11 Apr 2026
Read appraisal →Biomedical physics & engineering express
Differentiating long QT syndrome genotypes using electrocardiographic geometric parameterization and machine learning approaches
Long QT Syndrome (LQTS) is an inherited cardiac disorder characterized by dysfunctional cardiac ion channels, which result in prolonged QT intervals on electrocardiograms (ECGs). LQTS can lead to severe clinical manifestations, including syncope, ventricular arrhythmias, and sudden cardiac death. Effective genotype-specific management strategies are essential to mitigate the risk of life-threatening arrhythmias. This study aims to achieve automatic discrimination among the LQT1, LQT2, and LQT3 genotypes to enable targeted treatment and prevention strategies. Utilizing ECG data from the Telemetric and Holter ECG Warehouse's LQTS database, our methodology involves an automated extraction process of short ECG signals, geometric parameterization techniques, and classification using a two-stage cascade of binary support vector machine classifiers. The input features for the classifiers are derived from Lead I ECG signals sampled at 200 Hz, highlighting the potential application in developing single-lead ECG monitoring devices and applications, such as widely used smartwatches, for which short recording duration, low sampling frequency, and arm-to-arm lead measurements are fundamental prerequisites for practical use. The proposed classifier achieved 71% weighted accuracy on out-of-sample data (LQT1: 65% recall, 58% precision; LQT2: 79% recall, 82% precision; and LQT3: 71% recall, 77% precision). Our findings demonstrate the feasibility of noninvasive genotype differentiation for LQTS based on the morphological analysis of ECG signals, providing an advancement in the field of personalized cardiology and the development of portable diagnostic tools.
11 Apr 2026
Read appraisal →JMIR human factors
Evaluation of Wellby, a Cocreated Mobile App and Wearable to Support Stress Management and Overall Well-Being: Mixed Methods Acceptability and Usability Study
BACKGROUND: Digital well-being support tools can offer adolescents tailored interventions embedded in their digital environments. However, there is a lack of high-quality, evidence-based digital interventions specifically designed for young people's well-being needs. Wellby is a mobile app and wearable device cocreated with Irish secondary school students to support stress management and overall well-being. OBJECTIVE: This study aimed to evaluate the acceptability and usability of the Wellby system using the technology acceptance model, focusing on ease of use, perceived usefulness, and behavioral intention among Irish secondary school students. METHODS: A mixed methods acceptability and usability study was conducted with Irish secondary school students (n=43) across 3 schools: 2 mainstream secondary schools and 1 Youthreach center for early school leavers. Students accessed the Wellby mobile app for 8 weeks and received the custom wearable device for the final 4 weeks (April-May 2024). Wellby included mood tracking, to-do lists, educational resources, text-based health coaching, and heart rate variability biofeedback with guided breathing exercises. Acceptability and usability were evaluated using the user version of the Mobile App Rating Scale (uMARS), structured focus groups, and app engagement analytics. Secondary measures included baseline well-being questionnaires (Perceived Stress Scale, Pittsburgh Sleep Quality Index, and EPOCH Measure of Adolescent Well-Being). RESULTS: Among survey respondents (n=29), uMARS subscale scores were high: information quality (mean 4.2/5.0), functionality (4.1/5.0), and aesthetics (4.1/5.0). The perceived impact subscale showed the highest scores for well-being awareness (3.8/5.0) and help-seeking intention (3.9/5.0). Of survey respondents who participated in prior co-design sessions, 88% (21/24) and 83% (20/24) felt the wearable and app, respectively, aligned with their co-design feedback. App engagement more than doubled following the introduction of the wearable and revealed that the wearable tab was most accessed (643/1688, 38% of interactions), followed by the home (430/1688, 25%), resources (338/1688, 20%), and coaching (277/1688, 16%) tabs. Focus group feedback highlighted the mood tracker as the most valued feature and identified technical improvements, including better battery life, enhanced Bluetooth connectivity, and more personalization options. CONCLUSIONS: This study demonstrates that cocreated digital well-being tools can achieve high acceptability and usability with Irish secondary school students. Applying the technology acceptance model effectively captured student experience through mixed methods feedback. Students particularly valued self-tracking and personalization features over coaching or educational features. These findings highlight the importance of aligning digital technologies with adolescents' needs and preferences, such as features that encourage increased autonomy and identity formation. Future iterations of Wellby should address the technical limitations while continuing the involvement of students to adapt to their developmental needs and maintain high acceptability.
11 Apr 2026
Read appraisal →Journal of epidemiology and community health
Utilisation of in-person and telehealth care among US adults with loneliness and social isolation
BACKGROUND: Telehealth has become a critical tool for improving access to care among populations facing physical barriers to in-person care. However, its potential role in delivering care to adults with social barriers remains underexplored. We examined whether adults with loneliness or social isolation are more likely to use telehealth care versus in-person care compared to adults without loneliness or social isolation. METHODS: This cross-sectional study analysed data for US adults in the 2021 Medical Expenditure Panel Survey. Loneliness and social isolation were measured using the UCLA Loneliness Scale and the Berkman-Syme Social Network Index. Three indicators of healthcare utilisation were used as outcomes: in-person care only, telehealth care only and a combination of both. RESULTS: Among adults experiencing loneliness and social isolation, the likelihood of using only in-person care was lower by 4.1 (95% CI -6.1 to -2.1) and 2.8 (95% CI -5 to -0.6) percentage points. In contrast, the likelihood of using only telehealth care was higher by 2.6 (1.1-4.2) and 5.4 (3.9-6.9). The likelihood of using both in-person and telehealth care was also higher by 2.3 (0.7-3.9) and 5.2 (3.7-6.7), but telehealth was used more frequently than in-person care. Furthermore, telehealth utilisation for mental health services was higher among adults experiencing loneliness or social isolation. CONCLUSION: Compared with adults who are not lonely or socially isolated, adults experiencing loneliness and social isolation are more likely to use telehealth and less likely to use in-person care. Telehealth may serve as a viable alternative for individuals facing social challenges in accessing in-person services.
11 Apr 2026
Read appraisal →Scientific data
Continuous observation of Parkinsonian symptoms using symptom diaries & wearable accelerometry
Treatment adjustments in Parkinson's Disease (PD) are often based on clinical evaluations at single time points which are insufficient to adequately assess real-life motor fluctuations. Patient-written symptom diaries on the other hand are highly subjective and require well-educated and adherent patients to provide reliable results. Wearable accelerometry might provide a reliable, objective, and continuous diagnostic method to assess PD motor symptoms & fluctuations. However, large datasets of simultaneous sensor data and symptom diaries are needed for such method development and validation. We here provide a well-described, open-science dataset of simultaneous, bilateral, wrist-worn accelerometry and symptom diary data from 66 participants (41 male, 25 female) with PD. On average, participants provided data for 6.0 consecutive days resulting in a total of 393.8 days for the dataset as a whole. Symptom diaries include data on kinetic state, tremor, freezing of gait, falls, and PD-related medication intake. Further demographic information is also provided. This dataset will support the development and validation of accelerometry-based approaches to assessing motor symptoms and fluctuations in PD.
11 Apr 2026
Read appraisal →Journal for immunotherapy of cancer
Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application
The tumor microenvironment (TME), composed of tumor cells together with stromal cells, immune cells, vascular networks, and other components, constitutes a complex ecosystem that plays a decisive role in tumor initiation, progression, metastasis and therapeutic response. Traditional pathological diagnosis mainly relies on pathologists manually examining H&E-stained tissue sections under the microscope, a method that not only suffers from substantial interobserver variability but also has relatively low analytical efficiency. With the rapid development of computational pathology, the integration of whole-slide imaging technology and deep learning algorithms has provided powerful tools for characterizing tumor microenvironment. These techniques enable automated characterization of cellular, spatial and molecular heterogeneity within the tumor microenvironment, providing integrated insights that advance precision diagnostics and improve prediction of therapeutic response and patient outcomes. Based on a comprehensive review of existing research, this paper highlights recent advances in deep learning-driven recognition of panoramic TME features from H&E slides and their clinical applications and further discusses both the translational potential and current limitations of this technology in oncology research and clinical applications.
11 Apr 2026
Read appraisal →Journal of medical Internet research
Innovations in Deaf Health Care Communication: Systematic Review of Sign Language Recognition Systems
BACKGROUND: Deaf individuals often face communication challenges when interacting with those who can hear. Within health care settings, these challenges may pose risks to their safety, potentially resulting in misdiagnoses, treatment errors, and decreased quality of care. OBJECTIVE: This study aims to systematically review the evidence on communication systems reported in the literature that use human-computer interaction techniques to support communication between deaf individuals who use sign language and hearing health professionals in health care settings. The review focuses on systems that are either currently in use or proposed for use in health care and that have been tested using human participants or videos of human users. METHODS: A comprehensive search was performed via MEDLINE, Web of Science, ACM, IEEE Xplore, Scopus, and Google Scholar in March 2025. The inclusion criteria comprised studies developing a sign language recognition system within a health care context and testing with human users. Eligible studies underwent screening by 2 independent investigators (LRV and LMMSR or LFRdO and GTdSS), with any disagreements resolved by a senior researcher (MSM). RESULTS: The search retrieved 21,778 publications, and screening of reference lists identified 2 additional studies, resulting in a total of 23 studies meeting the eligibility criteria. Most systems (15/23, 65.2%) were image-based, while 34.8% (8/23) relied on sensors (glove-based or depth-sensing). Applications varied across health care settings, including general hospital care (10/23, 43.5%), emergencies (8/23, 34.8%), and primary care (4/23, 17.4%). All systems were in the development and testing stage, with no data on security and psychological impacts. Accuracy ranged from 25% to 100% for image-based and 72% to 99.7% for sensor-based systems. Bidirectionality and facial expression recognition, crucial for effective communication, were largely overlooked. CONCLUSIONS: Image-based systems were more common than sensor-based ones, though both showed wide variability in accuracy in recognizing and interpreting signs. Most systems failed to address critical aspects such as bidirectional communication and the recognition of facial expressions, essential for effective communication. None fully addresses the requirements for integration into health care settings. These findings highlight the need for further research on implementation, usability, and impact on the quality of care for deaf patients.
10 Apr 2026
Read appraisal →JMIR mHealth and uHealth
Trust and Use of Recommendations for Health Apps Among European Residents: Cross-Sectional Survey
BACKGROUND: There is growing recognition of the role of health apps in addressing health care system challenges, yet app quality and evidence vary widely, and consumers have little decision support at the point of download. OBJECTIVE: This study aimed to explore which sources of recommendations European residents use and trust when choosing health apps and whether residents support government review and rating of health apps. METHODS: We conducted a cross-sectional online survey (December 7, 2022, to February 16, 2023) in 26 languages targeting residents of the European Economic Area, the United Kingdom, and Ukraine. The survey contained 11 questions covering demographics, types of apps used, sources of advice used and trusted, and views on government review and rating. We included only fully completed responses (N=1228). Descriptive statistics are presented as counts and percentages. For subgroup analyses, we dichotomized trust responses ("I trust" vs other responses) and tested associations with gender (Fisher exact test), age group, and education level (chi-square test); an adjusted significance threshold was applied (P<.004) to account for multiple testing. We followed the CHERRIES (Checklist for Reporting Results of Internet E-Surveys) guidelines for reporting online surveys and STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) recommendations for observational studies. RESULTS: A total of 1228 respondents from 33 countries completed the survey; 1110 (90.4%) reported using one or more health apps. COVID-19 apps (763/1228, 62.1%) and activity apps (737/1228, 60.0%) were the most frequently used, whereas disease management (86/1228, 7.0%), diagnostic (77/1228, 6.3%), and treatment apps (70/1228, 5.7%) were the least used. Sources used to choose apps included family and friends (434/1228, 35.3%), health professionals (412/1228, 33.6%), and government or health authorities (358/1228, 29.2%). The most trusted sources were health professionals (987/1228, 80.4%), pharmacists (750/1228, 61.1%), and government or health authorities (736/1228, 59.9%). No statistically significant differences in trust by gender were observed (all P>.0038). Some differences by age and education were observed for select sources (eg, government or health authorities: χ21=8.546; P=.003; family and friends: χ2=19.133; P<.001). Overall, 1060 (86.3%) of 1228 respondents supported government review and rating of health apps (either directly or by commissioning another organization). CONCLUSIONS: In this large multilingual European survey, most respondents reported experience with health apps and placed greatest trust in health professionals, pharmacists, and government or health authorities, yet professional recommendations were used less often than informal sources. There is clear public support for government-led review and rating schemes to guide consumer choice. Efforts to make trustworthy, easy-to-find information available at the point of download and to support health care professionals in recommending high-quality apps could help bridge the gap between trusted and used sources.
10 Apr 2026
Read appraisal →Journal of medical Internet research
The Alberta Quality Assessment Tool: Risk of Bias (AQAT:RoB) for the Evaluation of Medical Large Language Model Question-Answer Studies: Development and Pilot Validation
BACKGROUND: Despite the transformative potential of large language models (LLMs) in health care, the rapid development of these tools has outpaced their rigorous evaluation. While artificial intelligence-specific reporting guidelines have been developed to address standardized reporting of artificial intelligence studies, there is currently no specific tool available for risk of bias assessment of LLM question-answer (QA) studies. Existing risk-of-bias tools for medical research are not well suited to the unique challenges of evaluating LLM-QA studies, which creates a critical gap in assessing their safety and effectiveness. OBJECTIVE: This study aims to develop the Alberta Quality Assessment Tool: Risk of Bias (AQAT:RoB) for LLM-QA studies to systematically evaluate the validity and risk of bias in LLM-QA studies. METHODS: We conducted 2 literature reviews. The first was on quality assessment tools for LLM-QA studies, and the second was on LLM-QA studies, which informed the first draft of the AQAT:RoB. The draft AQAT:ROB was further refined through a prespecified iterative process of modified Delphi, consensus meeting, and validation. The first Delphi process occurred between May 1 and May 20, 2025, and the first consensus meeting was held on May 22. The first round of validation was completed by 4 evaluators, who were not part of the consensus meeting, on 16 randomly selected studies. As this first round of validation surpassed our a priori threshold of ≥80% agreement and a Cohen κ of ≥0.61 between evaluators, no further rounds of development and validation were undertaken. A second Delphi process occurred between February 20 and February 23, 2026, to vote on postpilot changes in response to peer review. RESULTS: The AQAT:RoB consists of 5 high-level domains (Questions, Reference Answers, LLM Answers, Evaluators, Outcomes). These domains are subdivided into 9 subdomains. Each subdomain includes at least one "Support for Judgment" and at least one "Type of Bias" and is to be rated "low," "high," or "unclear" for risk of bias. A pilot evaluation was completed by internal validators who were not part of the consensus discussion and were asked to complete the AQAT:RoB form for each assigned study. Each of the 16 studies was evaluated by 2 evaluators independently. Pilot validation showed a percent agreement of 86.1% and a Cohen κ of 0.70 between assessors. CONCLUSIONS: The AQAT:RoB demonstrates promising initial reliability for assessing the validity or risk of bias in LLM-QA studies. The tool will benefit from future refinements, external validation, and periodic updates to keep pace with evolving technology.
10 Apr 2026
Read appraisal →JMIR mHealth and uHealth
Effectiveness of mHealth-Based Nutritional Interventions on Iron Status of Pregnant Women: Systematic Review of Randomized Controlled Trials
BACKGROUND: Anemia is a global health concern. It is disproportionately prevalent among pregnant women in low-resource regions, where iron deficiency is the leading cause. Given the multifactorial nature of anemia, a range of nutritional interventions is recommended. However, effective implementation is often hindered by limited health care access, poor adherence to supplementation, and gaps in nutrition knowledge and counseling. To address these challenges and optimize hemoglobin (Hb) levels among pregnant women, mobile health (mHealth)-based nutritional interventions offer a promising alternative. OBJECTIVE: The aim of the study is to review available evidence on the effectiveness of mHealth-based nutritional interventions on iron status (Hb and/or serum ferritin concentration) among pregnant women. METHODS: Searches were conducted in Embase, CINAHL, Cochrane Library, PubMed, Web of Science, and Scopus, and supplemented by snowballing to identify additional relevant studies from citation lists. The key search strings comprised 4 concepts: "mobile health," "nutritional intervention," "Hb, anemia or iron deficiency anemia," and "pregnant women." Predefined inclusion and exclusion criteria were applied during screening. The methodological quality of included studies was assessed using the Risk of Bias 2 tool. The primary end point was the change in mean Hb concentration or serum ferritin level. Effect sizes (ESs) were calculated as standardized mean differences, including Cohen d and Hedges g. RESULTS: Of the 14,284 studies identified, only 11 randomized controlled trials were included. These studies used various modes of delivery, including mobile phone calls (n=1), SMS text messaging (n=3), and mobile apps (n=4), with some using more than 2 modes (n=3). The effect of mHealth-based nutritional interventions on iron status varied significantly. In total, 4 studies demonstrated a large ES (>0.8), with 3 relying on WhatsApp Messenger as an mHealth delivery mode. Approximately 82% (9/11) of the included studies reported a positive effect (P values ranging from <.001 to .047) of the intervention on Hb level, whereas 2 studies reported no statistically significant association (P=.33 and P=.35, respectively). Notably, interventions with the largest ES achieved clinically significant improvements in Hb concentration, with within- and between-group differences exceeding 1 g/dL. However, including behavioral change theories and nutrition-sensitive components was not consistently associated with larger ESs. Due to high heterogeneity (I2>95%), attributed to variations in mHealth delivery modes, functions, and interactive features across the included studies, meta-analysis could not be performed. CONCLUSIONS: This review demonstrates that mHealth-supported nutritional interventions effectively optimize Hb concentration in pregnant women. While SMS text messaging was less effective in improving Hb concentration, combining it with another mHealth delivery mode, such as phone calls, improved intervention effectiveness. However, the variability in mHealth delivery modes, functions, and interactive features underscores the need for tailored strategies that account for context-specific challenges, digital literacy, and access to technology to enhance effectiveness.
10 Apr 2026
Read appraisal →JMIR mental health
Predicting Momentary Suicidal Ideation From Smartphone Screenshots Using Vision-Language Models: Prospective Machine Learning Study
BACKGROUND: Passive smartphone sensing shows promise for suicide prevention, but behavioral metadata (GPS, screen time, and accelerometry) often lacks the contextual information needed to detect acute psychological distress. Analyzing what people actually see, read, and type on their phones-rather than just usage patterns-may provide more proximal signals of risk. OBJECTIVE: This study aimed to test whether vision-language models (VLMs) applied to passively captured smartphone screenshots can predict momentary suicidal ideation (SI). METHODS: Seventy-nine adults with past month suicidal thoughts or behaviors completed ecological momentary assessments (EMA) over 28 days while screenshots were captured every 5 seconds during active phone use. We fine-tuned open-source VLMs (Qwen2.5-VL [Alibaba Cloud], LFM2-VL [Liquid AI]), and text-only models (Qwen3 [Alibaba Cloud]) to predict SI from screenshots captured in the 2 hours preceding each EMA. We evaluated performance with temporal and subject holdouts. RESULTS: The analytic sample comprised 2.5 million screenshots from 70 participants. Temporal holdout models achieved strong discrimination at the EMA level (AUC=0.83; AUPRC=0.77), with image-based models outperforming text-only models (AUC=0.83 vs 0.79; 95% CI 0.003-0.07). Subject holdout generalization was near chance (AUC≈0.50), though a simple lexical screening method retained modest discrimination (AUC=0.62). Smaller models performed comparably to larger models, supporting feasible on-device deployment. CONCLUSIONS: Screen content predicts short-term SI with clinically meaningful accuracy when models are personalized but does not generalize across individuals. These findings support a 2-stage clinical architecture, coarse lexical screening for new patients, with personalized VLM-based monitoring after a calibration period. On-device inference may enable privacy-preserving deployment.
10 Apr 2026
Read appraisal →PloS one
MSCNet: Efficient and accurate semantic segmentation of LiDAR data using Multi-scale Convolution
In autonomous driving and intelligent robotics, the semantic information of LiDAR (Light Detection and Ranging) sensor data is crucial for understanding the surrounding environment. However, directly operating on point clouds is computationally expensive. To address this, some researchers have projected three-dimensional LiDAR data onto a two-dimensional spherical range view and used two-dimensional convolutional neural networks to segment the projected images. While the results are promising, many of these models are structurally complex, with high spatiotemporal complexity, which makes them unsuitable for real-time applications. To solve these issues, this paper proposes a multi-scale LiDAR data semantic segmentation method, MSCNet, with fewer parameters and higher segmentation accuracy. In the encoding phase, a single-channel multi-scale feature fusion block is introduced to alleviate the distribution differences between input channels. To obtain more stable local features, multi-scale dilated convolution residual blocks are designed to encode information from different receptive fields. To quickly capture global features, a pyramid pooling module is introduced. Experimental results on the SemanticKITTI, SemanticPOSS, and Pandaset datasets show that MSCNet achieves a good balance between parameter, accuracy, and running time. Particularly on the SemanticPOSS and Pandaset datasets, MSCNet achieves the best performance. Under the same parameter conditions, this method outperforms existing point cloud-based and projection-based methods.
10 Apr 2026
Read appraisal →PloS one
Enhancing intrusion detection in encrypted DoH traffic through a robust ensemble learning framework
The DNS over HTTPS (DoH) protocol enhances user privacy by encrypting DNS queries and responses within HTTPS. However, this encryption enables attackers to tunnel malicious traffic through covert DoH channels and make detection difficult for network security and enterprises. In this study, we propose a stacked ensemble approach that employs four sequential base learners' - two Long Short-Term Memory (LSTM) and two Gated Recurrent Unit (GRU) neural networks as base classifier networks with varied hyperparameters and XGBoost (eXtreme Gradient Boosting) as the meta-learner. An XGBoost model trained on out-of-fold predictions generated by base learners supported through stratified 5-fold cross validation. We also applied Recursive Feature Elimination (RFE) with XGBoost for cross-validated feature selection and decreased high-dimensional feature spaces from 29 to 13 and 20 features on the CIRA-CIC-DoHBrw-2020 and DoH-Tunnel-Traffic-HKD datasets respectively. The proposed framework was evaluated on the CIRA-CIC-DoHBrw-2020 and DoH-Tunnel-Traffic-HKD datasets achieved 0.9996 recall, 0.9999 F1-score and 1.0000 AUC-PR on CIC-DoH dataset and 0.9998 recall, 0.9999 F1-score, and 1.0000 for both AUC-ROC and AUC-PR on HKD-DoH dataset. The proposed model outperforms all individual base classifiers, evaluated ML models, and existing state-of-the-art approaches. The proposed XGBoost meta-model needs only 1.26 seconds for training and 0.129 seconds for inference on HKD-DoH dataset, thus making the proposed framework both highly accurate and computationally efficient for real-world intrusion detection in encrypted DoH traffic.The preprocessed data and the source code supporting the findings of this study are available at: https://github.com/soreettihussein/Stacked-ensemble-meta-learning-IDS-for-DoH-network-traffic/tree/main.
10 Apr 2026
Read appraisal →Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
The need for verification in artificial intelligence-driven scientific discovery
Artificial intelligence (AI) is transforming the practice of science. Machine learning (ML) and large language models (LLMs) can generate hypotheses at a scale and speed far exceeding traditional methods, offering the potential to accelerate discovery across diverse fields. However, the abundance of hypotheses introduces a critical challenge; without scalable and reliable mechanisms for verification, scientific progress risks being hindered rather than advanced. In this article, we trace the historical development of scientific discovery, examine how AI is reshaping established practices for discovery and review the principal approaches, ranging from data-driven methods and knowledge-aware neural architectures to symbolic reasoning frameworks and LLM agents. While these systems can uncover patterns and propose candidate laws, their scientific value ultimately depends on rigorous and transparent verification, which we argue must be the cornerstone of AI-assisted discovery. This article is part of the discussion meeting issue 'Symbolic regression in the physical sciences'.
10 Apr 2026
Read appraisal →ACS applied materials & interfaces
Physically Cross-Linked Biopolymer-Based Strong and Stretchable Organohydrogel with Self-Healing and Biocompatibility for Environmentally Tolerant Strain Sensors
The development of wearable strain sensors increasingly focuses on multifunctional materials that integrate mechanical robustness, self-repairing capacity, temperature-resilient conductivity, and biocompatibility. Herein, we developed a physically cross-linked biopolymer-based organohydrogel by incorporating gelatin (Gel) and β-cyclodextrin-grafted chitosan (CDCS), designated as Gel/CDCS, reinforced through hydrogen bonding, ionic interactions, and host-guest complexation. The optimized Gel/CDCS1 organohydrogel with 1% of CDCS exhibited outstanding stretchability, with a tensile strain at break of 410.9% and a tensile strength of 92.2 kPa, while maintaining 85% of its mechanical properties after self-healing at 40 °C. The introduction of kosmotropic salts (Na3Cit and NaCl) and a glycerol-water binary solvent system conferred remarkable environmental adaptability, allowing stable performance from -20 to 37 °C. Notably, it achieved a high ionic conductivity of 0.946 S m-1 at room temperature, alongside 0.606 S m-1 at -20 °C and 0.330 S m-1 at 37 °C, ensuring operation under extreme conditions. The strain sensor displayed high sensitivity with a gauge factor (GF) increasing from 1.06 to 1.34 over a broad strain range and maintained a stable electrical response over 600 consecutive stretching cycles. Additionally, it enabled reliable detection of diverse human motions, including facial expressions, joint bending, and breathing, demonstrating excellent conformability and signal stability during on-body monitoring. Additionally, Gel/CDCS1 exhibited excellent biodegradability, degrading by over 80% in soil within 4 days, and showed high biocompatibility with fibroblast viability of around 80%. These attributes establish Gel/CDCS1 as a promising eco-friendly candidate for next-generation flexible electronics, wearable strain sensors, and biomedical applications.
10 Apr 2026
Read appraisal →ACS applied materials & interfaces
Bioinspired Janus Electronic Textiles with Moisture and Thermal Management
Wearable electronics have attracted great attention in recent decades. However, excessive sweat accumulation on skin often has a significant effect on the signal stability, comfort, and safety of electronic skin in practical applications. Herein, a skin-comfortable Janus electronic textile (e-textile) that integrates unidirectional liquid transport and passive radiative cooling properties is developed. This e-textile is fabricated through laser ablation and single-side plasma treatment of carbon nanotubes (CNTs) doped with polydimethylsiloxane (PDMS). The resulting e-textile features a conical pore structure and asymmetric wettability, enabling unidirectional sweat transport from the skin surface to a superhydrophilic silk layer, thereby maintaining a dry and comfortable skin microenvironment. The Janus e-textile achieves a solar reflectivity of 69.2% and a mid-infrared emissivity of 98.3%, reducing the temperature by approximately 5.0 °C compared with a cotton fabric under solar intensity, demonstrating a superior radiative cooling performance. Moreover, the Janus e-textile exhibits stable resistance changes under cyclic stretching and bending, accurately detecting subtle movements across various body parts while reliably acquiring electromyography (EMG) signals. This work positions the Janus e-textile as a pioneering solution for next-generation wearable technologies, integrating thermal and moisture management with advanced resistive strain sensing and EMG capabilities for applications in sports, rehabilitation, and health monitoring.
10 Apr 2026
Read appraisal →Journal of medical Internet research
Preferences for Personalized Text Message Appointment Reminders Among Outpatients in a Universal Health System: Cross-Sectional Study
BACKGROUND: SMS text messaging reminders are widely used to reduce missed outpatient appointments; however, evidence remains limited regarding which types of reminder content patients prefer, particularly within East Asian universal health systems. In Taiwan, minimal financial barriers to care and unrestricted access to secondary and tertiary hospitals contribute to high outpatient visit volumes and persistent no-show rates. These contextual features underscore the need for behaviorally informed and demographically tailored reminder strategies rather than uniform messaging approaches. OBJECTIVE: This study aimed to examine patient preferences for 6 theory-guided SMS appointment reminder types and to identify the predictors of reminder preference related to demographic characteristics and health care utilization, with the goal of informing personalized reminder design for a forthcoming randomized controlled trial. METHODS: We conducted a cross-sectional online survey among adults in Taiwan with prior outpatient experience. Six SMS reminder prototypes were developed based on behavioral communication principles and validated by a multidisciplinary expert panel using item-level content validity indices. Participants selected their preferred SMS reminder type and reported sociodemographic characteristics and recent health care utilization. Bivariate associations were examined using chi-square tests and one-way ANOVA, with Benjamini-Hochberg false discovery rate correction applied to control for multiple testing. To identify independent predictors of SMS reminder preference while adjusting for potential confounding, we fitted a multinomial logistic regression model with all covariates entered simultaneously. RESULTS: A total of 1095 respondents completed the survey. General reminders and messages referencing prior missed appointments were most frequently preferred, whereas empathy-based or relationally framed messages were selected less often. In false discovery rate-adjusted univariate analyses, both age and sex were associated with SMS reminder preference. However, in the fully adjusted multinomial logistic regression model, age emerged as the only statistically significant independent predictor. Participants younger than 50 years were significantly more likely to prefer alternative reminder message types compared with the general reminder (adjusted odds ratio 1.64, 95% CI 1.18-2.28; P=.003). Sex did not retain statistical significance after multivariable adjustment. Other sociodemographic characteristics and health care utilization variables, including education level, employment status, residential region, outpatient visit frequency, and recent missed appointments history, were not independently associated with reminder preference. CONCLUSIONS: Preferences for outpatient SMS reminder content vary systematically, with age representing the most robust independent predictor. Across the sample, concise and behavior-focused reminders were preferred over empathy-oriented or relational formats. These findings support age-informed tailoring of SMS reminder content and provide content-validated SMS prototypes for use in subsequent interventional research. The results offer formative evidence to guide the design of randomized trials aimed at reducing outpatient no-shows and improving the efficiency of ambulatory care delivery in Taiwan's universal health care system.
10 Apr 2026
Read appraisal →PloS one
MaxGRNet: A multi-axis vision transformer with improved generalization for eye disease classification using explainable AI with insertion-deletion operations on fundus images
Eye diseases, including diabetic retinopathy (DR), glaucoma, and cataracts, represent a major global health concern and can lead to severe visual impairment or blindness if not identified in a timely manner. This study proposes a novel eye disease classification framework based on a multi-axis vision transformer (MaxViT) applied to color fundus images with Explainable Artificial Intelligence (XAI) techniques to enhance model transparency. The proposed architecture integrates transformer-based attention mechanisms with Global Response Normalization (GRN)-based multi-layer perceptron (MLP) layers to capture complex spatial and contextual relationships within fundus images effectively. The model was evaluated on a publicly available eye disease classification dataset using a five-fold cross-validation strategy to assess its robustness and generalization. The experimental results show that the proposed approach consistently outperforms conventional Convolutional Neural Networks (CNNs) and Vision Transformer (ViT) variants, including ResNet50, Swin-T, MaxViT-T, and ViT-B16. The model achieved a macro-averaged test accuracy, precision, and recall values of 96.75%, 96.70%, and 96.80%, respectively, with paired statistical t-tests confirming that these improvements were statistically significant. Rigorous preprocessing techniques were employed to improve data consistency, and XAI-based visual explanations provided insights into the model's decision-making process, supporting interpretability in ophthalmic image analysis. Overall, the proposed MaxViT-based framework is robust and computationally feasible for research-oriented evaluation approaches for automated fundus image classification, highlighting the potential of advanced transformer architectures for future decision-support and research-oriented ophthalmic applications.
10 Apr 2026
Read appraisal →JMIR formative research
A Gamified Pain Management Intervention for Adults With Chronic Pain in Mainland China: Single-Arm Pre-Post Pilot Study With Machine Learning Predictive Modeling
BACKGROUND: The widespread prevalence of chronic pain (CP) significantly impacts daily functioning worldwide. In mainland China, maintaining engagement in biopsychosocial interventions remains challenging. Gamification, designed based on self-determination theory, can enhance motivation, while machine learning (ML) algorithms can assist clinicians in dynamically optimizing pain management. OBJECTIVE: This study aimed to (1) evaluate the preliminary effectiveness of a gamified pain management (GPM) program on CP and psychological outcomes and (2) identify key factors of significant pain improvements through the application of ML to guide intervention adjustments. METHODS: A single-arm, pre-post study was conducted with 16 participants with CP in mainland China, recruited via social media using convenience sampling. Participants engaged in a 10-week web-based GPM intervention consisting of education, physical activities, and gamified elements, including points, avatars, and feedback. Primary outcomes were pain intensity and interference measured by the Brief Pain Inventory. Secondary outcomes included anxiety, depression, and quality of life. Analysis included paired t tests, and ML models were trained to predict clinically meaningful pain reductions. Shapley additive explanations, least absolute shrinkage and selection operator regression, association rule mining, and Kaplan-Meier survival analysis were used to identify key predictors and optimal sessions and intervention durations across subgroups. RESULTS: A total of 16 participants were engaged, with a mean age of 27.63 (SD 9.584) years. Results from paired t tests reported significant improvements in pain intensity (decreased by 27.3%, 95% CI 1.061 to 3.064; P=.001), pain interference (decreased by 27.3%, 95% CI 8.159-17.216; P<.001), and psychological distress, including anxiety (t15=3.538, 95% CI 0.969 to 3.906; P=.003) and depression (t15=4.559, 95% CI 2.230 to 6.145; P<.001). The gradient boosting model demonstrated the highest predictive accuracy (area under the curve=0.89 and accuracy=0.82). Least absolute shrinkage and selection operator regression identified session 3 (β=-0.45, 95% CI -0.68 to -0.22; P<.001) and session 5 (β=-0.32, 95% CI -0.59 to -0.05; P=.02) as most predictive of clinical success, while association rule mining revealed effective session combinations for different patient subgroups. Time-to-event analyses indicated that individuals with low back pain and higher baseline severity required longer intervention durations for improvement (5 wk; P=.03). CONCLUSIONS: This pilot study presents an innovative method that combines ML with dynamic engagement data from a GPM program during interventions, rather than relying on static baseline data in prior studies. The results show preliminary efficacy and identify specific optimal session combinations and personalized treatment durations for different pain subgroups. These exploratory findings contribute to the field by providing a data-driven method for adaptive, personalized digital health interventions that move beyond one-size-fits-all strategies, potentially enabling clinicians to modify content and dosage to improve engagement and outcomes if validated in larger sample trials.
10 Apr 2026
Read appraisal →ACS applied materials & interfaces
Evolving Beyond Nature: AI-Driven Rapid Advancements of Bio-Inspired Electronic Skin
Electronic skin (e-skin) is a rapidly developing technology at the intersection of humans and machines with tremendous applications in robotics, healthcare, sports performance, and other fields of wearable electronics. With the recent popularization of artificial intelligence (AI), data-driven approaches to materials design and geometric functional optimization have accelerated e-skin development, enabling optimized microstructures, novel functionalities, and improved usability. Recent progress in bioinspired materials design and functionality enhancements for e-skin is paving the way for future-facing machine learning-powered tools to further accelerate their development. In this review, we discuss the traditional properties of e-skins, their sensing mechanisms, and suitable applications. Then, we investigate and categorize bioinspired structures from material, structural, and functional perspectives. Next, we outline the fundamentals of AI for data-driven optimization and how it can be utilized toward bioinspired e-skin design. This blended e-skin design framework culminates in novel applications, from customizable layouts for personalized therapeutics to dynamic environment recognition for enhanced robotics. Finally, we will explore the challenges of e-skin human-machine interfaces, including their hardware limitations, sensitivity, and longevity issues, and offer insights into future-proof research.
10 Apr 2026
Read appraisal →JMIR research protocols
Cultural Adaptation of a Digital Mobile App for Bipolar Disorder (PolarUs): Protocol for a Qualitative Co-Design Study
BACKGROUND: Bipolar disorder (BD) affects approximately 40 million people worldwide and is a chronic, potentially disabling mood disorder. Although effective treatments exist, access to evidence-informed psychosocial care remains limited, particularly for culturally and linguistically diverse populations, contributing to persistent global treatment gaps. Digital mental health interventions (DMHIs), such as smartphone apps, offer a promising means to improve access to self-management support and quality of life (QoL), an outcome prioritized by people with BD and in clinical guidelines. However, most apps for BD lack quality and are not culturally adapted or co-designed with people with BD, limiting relevance and engagement. PolarUs (mobile app) is an evidence-informed DMHI developed using co-design with people with BD. The app is structured on the core 14 domains from the Quality of Life in BD scale, the only BD-tailored scale, combined with psychoeducation on self-management strategies and QoL. A recent pilot study demonstrated promising QoL, clinical, and feasibility outcomes. OBJECTIVE: This study aims to culturally and linguistically adapt the PolarUs app into French, Chinese, and Spanish for the North American context using qualitative and co-design methods. METHODS: Guided by community-based participatory research principles, whereby end users are engaged throughout the research process, and the Ecological Validity Framework of Bernal et al, we will engage advisory groups of people with lived experience from each linguistic community throughout the cultural adaptation process. Semimonthly virtual meetings will support systematic cultural adaptation of the self-management strategies, affirmations, and resources while maintaining fidelity to core evidence-based components. This will include cultural tailoring of app content and the identification of culturally appropriate resources. Advisory groups will also contribute to the cointerpretation of findings and the co-design of culturally appropriate recruitment and implementation strategies of PolarUs for a future clinical trial. Meetings will be recorded and coanalyzed as research data with advisory groups using qualitative reflexive thematic analysis to capture advisory group perspectives and experiences. RESULTS: This study was funded in October 2024. As of January 31, 2026, we enrolled 7 participants, and the results are expected to be published in the fall of 2026. CONCLUSIONS: The findings will support the development of a culturally appropriate DMHI for BD for additional linguistic communities, advance cultural adaptation methodologies, and inform preparation for a future clinical trial. This study will produce the first culturally adapted, BD-specific DMHI developed through co-design using a community-based participatory research approach with multilingual end users from traditionally underserved communities, advancing equitable access, engagement, and scalability of DMHIs for BD and digital health care more broadly.
10 Apr 2026
Read appraisal →Scientific reports
Research on lung nodule detection in X-ray plain films based on improved YOLOv12 model
To improve automatic lung nodule detection in chest X-ray images, this study proposes an improved YOLOv12-based detection framework by integrating space-to-depth convolution (SPDConv), a dynamic upsampling module (DySample), and a lightweight feature aggregation module (VoVGSCSP). SPDConv enhances spatial information preservation during feature extraction, DySample replaces conventional upsampling to improve multi-scale feature fusion, and VoVGSCSP strengthens feature representation while reducing computational redundancy. The optimized YOLOv12-DSV model was trained and evaluated using a publicly available chest X-ray dataset with annotated lung nodules from the Roboflow platform, with performance assessed through five-fold cross-validation and external testing. Experimental results show that the proposed model achieved an mAP50 of 0.735 and an mAP50-95 of 0.426, outperforming the original YOLOv12 model (mAP50: 0.704; mAP50-95: 0.411). In addition, the proposed model reduced the number of parameters from 2.52 to 2.21 M, decreased FLOPs from 6.0 to 5.2 G, and increased inference speed from 97.6 to 107.8 FPS. These results indicate that the proposed YOLOv12-DSV model improves detection accuracy while reducing computational cost, achieving a more favorable balance between detection performance and model complexity for lung nodule localization in chest X-ray images.
10 Apr 2026
Read appraisal →JMIR human factors
Predicting Electronic Health Record Usability: Scoping Review of Adoption Models, Metrics, and Future Directions
BACKGROUND: Electronic health records (EHRs) play an essential role in modern health care, enabling data sharing and improving patient safety; however, even though vendors must adhere to International Organization for Standardization-related usability standards for EHR certification, persistent usability issues continue to undermine efficiency, contribute to clinician burden, and increase the risk of preventable errors. OBJECTIVE: This scoping review synthesizes existing research on EHR adoption and usability, emphasizing theoretical models, measurement approaches, factors, and analytic methods used to assess or predict usability. We identify gaps and opportunities for integrating predictive analytics and artificial intelligence (AI) to advance research and improve the usability of EHRs. METHODS: Following Joanna Briggs Institute and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we systematically searched MEDLINE, Web of Science, IEEE Xplore, and Scopus library databases for studies published between January 1, 2009, and April 9, 2025. Inclusion criteria focused on empirical research using predictive methods or models related to EHR usability. Data were charted and synthesized thematically. RESULTS: Of the 2323 screened papers, 47 studies met inclusion criteria. Most research examined or predicted EHR adoption (not usability) using dominant frameworks, such as the technology acceptance model, unified theory of acceptance and use of technology, and the information system success model, which comprised usability. Factors related to usability-particularly perceived usefulness, perceived ease of use, effort expectancy, and facilitating conditions via the EHR adoption models-appeared frequently. Regression-based methods and structural equation modeling were the most common analytic techniques. No studies applied predictive modeling or AI to predict EHR usability. CONCLUSIONS: The focus of this study on the prediction of EHR usability and adoption for the past 15 years is distinctive in the literature. It extends prior usability reviews (mostly focusing on adoption, not prediction of usability). Predictive modeling for EHR usability remains underdeveloped throughout 2009 to 2025. Dominant frameworks in the EHR literature continue to prioritize predicting adoption over operational usability. These models rely heavily on self-reported, cross-sectional measures captured at a single postimplementation time point, embedding systematic bias and obscuring longitudinal usability dynamics. Despite the application of increasingly sophisticated predictive techniques-primarily variants of regression and structural equation modeling-usability has remained analytically subordinate to adoption and acceptance constructs for more than 15 years. As a result, widely used models, such as the technology acceptance model and unified theory of acceptance and use of technology, position usability merely as an antecedent to intention or use, rather than as an independent, system-level property that can be empirically measured, modeled, and predicted. Therefore, there is substantial opportunity to integrate predictive analytics, AI, and longitudinal usability measures to build dynamic models.
10 Apr 2026
Read appraisal →Sexual health
Co-creation and evaluation of people-centered mobile health messages to promote PrEP adherence among GBMSM in China: a feasibility study
BACKGROUND: Co-creation has the potential to generate people-centered mobile health (mHealth) interventions to improve PrEP adherence. This study generated people-centered mHealth messages with Chinese gay, bisexual and other men who have sex with men (GBMSM) via a structured co-creation approach, and evaluated the messages quality, and assessed the feasibility of the co-creation approach. METHODS: A three-step co-creation framework was implemented with 19 GBMSM PrEP users and six HIV researchers between 2021 and 2023. Messages were assessed via 10-point scales, and the degree of usefulness by co-creators and 783 participants from a PrEP demonstration trial. Thematic analysis identified PrEP adherence barriers and participants' reflections from meeting notes and materials, with descriptive statistics summarizing evaluation results. RESULTS: Four levels of PrEP adherence barriers were identified (insufficient education, efficacy concerns, inadequate behavioral skills, PrEP stigma). Based on these barriers, 25 mHealth messages (19 posters, three videos, three text messages) were co-created, covering nine themes (e.g. missed dose coping, side-effect management). Seventeen high-quality messages were selected, with mean scores of 8.25 (relevance), 7.86 (appropriateness) and 7.25 (elaboration). Among GBMSM PrEP users who received the messages, 90.4% (708/783) valued the co-created messages as helpful. Reflections from the co-creators highlighted the value of the participatory process, confirming that co-creation effectively engages GBMSM PrEP users. CONCLUSIONS: Co-creation groups are feasible for Chinese GBMSM to identify context-specific PrEP adherence barriers and develop people-centered mHealth messages, supporting the value of co-creation for PrEP adherence intervention development in this population.
9 Apr 2026
Read appraisal →JMIR mHealth and uHealth
Promoting Self-Regulated Social Media Use on Smartphones With a Mobile Intervention App (Wellspent): Randomized Controlled Trial
BACKGROUND: Problematic social media use has been linked to reduced well-being and impulse control difficulties. While digital self-control apps show potential for reducing general app usage, they often lack customization, leading to limited effectiveness and increased user resistance. Their impact on problematic social media use remains uncertain. OBJECTIVE: This study evaluates the effectiveness of the Wellspent app, a customizable mobile intervention app designed to promote self-regulated social media use by targeting user-defined problematic app use and offering tailored behavioral nudges. METHODS: In a 3-week randomized controlled trial, 70 iPhone users (mean age 26.2, SD 5.6 years; 47/70, 67% female), regularly using at least 1 social media app, were randomly assigned to an intervention (n=35) or control group (n=35). The intervention group received personalized full-screen reminders with the option to quit or continue social media app use whenever an app session exceeded a self-defined time limit. Participants completed weekly online surveys measuring problematic social media use, problematic smartphone use, self-efficacy, and daily screen time on their most problematic app. Linear mixed models tested intervention effects. RESULTS: While no significant reduction in problematic social media use or increase in self-efficacy was observed, the intervention group showed a significant reduction in daily screen time on their most problematic app by approximately 29 minutes (estimate=-29.35, SE 6.84, 95% CI -42.79 to -15.99; P<.001), and a significant decrease in perceived problematic smartphone use (estimate=-0.46, SE 0.18, 95% CI -0.80 to -0.11; P=.01). CONCLUSIONS: The Wellspent app demonstrated short-term efficacy in reducing problematic smartphone use. By allowing users to tailor interventions to their personal goals, the app shows promise as a self-directed tool to support healthier digital habits. Further research should explore long-term effects and feature-specific impacts.
9 Apr 2026
Read appraisal →JMIR medical informatics
Exploring the Role of AI in Managing Treatment Recommendations for Lymphedema: International, Multidisciplinary, Multiprofessional Survey Study of Trust, Reliability, and Impact on Decision-Making
BACKGROUND: Upper and lower extremity lymphedema is a chronic, progressive condition that significantly impairs the quality of life of affected patients. Despite the recently established effectiveness of physical therapy and supermicrosurgical interventions, current guidelines frequently lag behind emerging evidence and commonly do not offer stage-specific treatment algorithms. This gap in evidence-based guidance may prompt clinicians with limited experience to seek support from large language models such as ChatGPT. OBJECTIVE: Given the potential of artificial intelligence to rapidly integrate emerging research, this study evaluated how clinicians from different professional backgrounds rate the quality and reliability of personalized lymphedema management recommendations generated by ChatGPT. METHODS: In this exploratory cross-sectional study, ChatGPT generated treatment recommendations for 6 standardized lymphedema case scenarios. An international panel of 67 participants (resident doctors, board-certified specialists, physiotherapists, and advanced practice nurses) from 34 institutions across 11 countries assessed the recommendations using a modified DISCERN questionnaire with a 9-point agreement scale ranging from 1 (completely disagree) to 9 (completely agree). Ratings were summarized as pooled means with variability measures and compared across clinician groups (residents vs board-certified physicians vs physiotherapists or advanced practice nurses) using group comparison testing. RESULTS: ChatGPT was rated most favorably for diagnostic accuracy and treatment relevance, with higher ratings among residents than board-certified physicians. Residents assigned significantly lower scores for source indication, source currency, and communication of uncertainty. Between-group differences were observed across multiple DISCERN items, consistent with systematically more critical appraisal by experienced specialists. Participants reported moderate to high trust and willingness to consider ChatGPT as a supplementary resource, with more favorable perceptions among younger respondents. CONCLUSIONS: Clinicians perceived ChatGPT as potentially useful for preliminary orientation and educational support in lymphedema management, especially for less experienced users. Despite not being blinded, lower ratings in evidence transparency and uncertainty communication, particularly among experienced specialists, suggest that current artificial intelligence outputs should not be used as stand-alone guidance. Future work should test clinically integrated, citation-grounded workflows in prospective settings and evaluate whether they improve decision quality and efficiency.
9 Apr 2026
Read appraisal →JMIR cancer
Acceptance, Perceived Usefulness, and Data Sharing in Mobile Health Apps Among Patients With Breast Cancer: Cross-Sectional Survey Study
BACKGROUND: Mobile health (mHealth) apps promise to enhance patient empowerment, enable real-time health monitoring, and support self-management. Patients with breast cancer stand to benefit particularly from these capabilities, given the demanding pre- and posttreatment procedures they face. However, the effective adoption of these tools is challenged by issues including accessibility, usability limitations, and privacy concerns. OBJECTIVE: This study aimed to investigate the current usage behavior and attitudes toward mHealth apps among patients with breast cancer in Germany, focusing on acceptance, usefulness, empowerment, and data-sharing. Specifically, we aimed to identify the incentives and barriers influencing use and the intention to use mHealth apps at the time of this writing to guide their further implementation and use. METHODS: A cross-sectional online survey was conducted between November 2023 and June 2024 using a structured questionnaire developed in German. Participants with a self-reported breast cancer diagnosis were recruited via university hospital channels, patient associations, and social media. Topics included technology and health app usage, perceived usefulness, personal empowerment, and attitudes toward data sharing. The survey comprised both closed- and open-ended questions to capture quantitative patterns and qualitative insights into participants' experiences and perspectives. RESULTS: We collected responses from 90 participants with breast cancer. Participants demonstrated high engagement with digital technology; 97% (87/90) reported daily smartphone use, and 70% (63/90) owned a smartwatch or fitness tracker. Approximately 67% (60/90) of respondents had used health apps in the past year, with many relying on general fitness and lifestyle apps to support their health management. Most participants (62/90, 69%) indicated that health apps contribute to a better understanding of their personal health. Health apps were generally perceived as useful, with users highlighting their ability to provide continuous support and timely, reliable health information (n=31). This, in turn, was seen as instrumental in promoting self-management and empowerment. Respondents also showed a conditional willingness to share personal health data for research, particularly when clear benefits for treatment advancements were evident (n=82). Nonetheless, concerns were raised regarding data privacy, app usability, the need for multiple apps to cover all necessary features, and the potential increase in fear of illness (cyberchondria). CONCLUSIONS: The findings underscore the potential of mHealth apps to empower patients with breast cancer by improving health literacy and supporting self-management. To maximize their impact, future digital health tools must align closely with patient needs by incorporating adaptive, interactive features and integrating seamlessly with clinical care. Addressing key barriers-particularly data privacy and usability issues-is essential for broader adoption. Longitudinal studies are warranted to evaluate the long-term effects of health app usage on patient empowerment, quality of life, and clinical outcomes.
9 Apr 2026
Read appraisal →Physiological measurement
Towards fair and trustworthy heart rate estimation from wrist-worn photoplethysmography: a multi-wavelength dataset and uncertainty-aware deep learning approach evaluated across skin tones, sexes, and motion conditions
Objective.To improve fairness (reduced disparities across skin tones and sexes) and trust (well-calibrated uncertainty metrics that indicate unreliable predictions) in wrist-worn photoplethysmography (PPG) heart rate estimation. While PPG offers a convenient, low-cost method for continuous heart rate monitoring, accuracy is often degraded by motion artefacts and demographic variation.Approach.We collected a multi-wavelength (blue, green, red, IR) wrist-worn PPG dataset covering diverse activities, skin tones, and sexes, and developed a multi-branch PPG-accelerometer fusion convolutional neural network quantifying both aleatoric and epistemic uncertainty (the latter via Monte Carlo dropout, concrete dropout, and deep ensembles). We compared wavelength sets, quantified demographic effects, and applied uncertainty-aware post-processing to filter out high-uncertainty predictions. Evaluation used leave-one-subject-out cross validation across both the collected multi-wavelength dataset and five commonly used single-wavelength datasets.Main results.Multi-wavelength fusion improved accuracy under motion, with blue-green-red-IR achieving the lowest mean absolute error (MAE). Baseline error was higher for participants with darker skin tones and for females; uncertainty-guided rejection reduced MAE and effectively eliminated these gaps (reducing parity gaps from 1.2 BPM to 0.0 BPM for skin tone). Concrete dropout provided the best calibration-accuracy-efficiency trade-off, achieving the lowest average miscalibration area (MA=0.12) while maintaining competitive MAE.Significance.Combining calibrated uncertainty with group-aware evaluation yields a more fair and trustworthy wrist-worn PPG heart rate estimation pipeline, supporting responsible deployment in consumer and sports contexts and informing bias-aware clinical development.
9 Apr 2026
Read appraisal →Translational vision science & technology
OptiDON-Guard: An Ensemble Model for Identification of Dysthyroid Optic Neuropathy Using Optical Coherence Tomography Angiography
PURPOSE: To explore the value of machine learning models based on optical coherence tomography angiography (OCTA) in identifying dysthyroid optic neuropathy (DON). METHODS: A retrospective, dual-center, cross-sectional study was conducted involving 346 thyroid eye disease patients (with/without DON) from two institutions. Participants underwent ophthalmic examinations, including OCTA scans centered on the macula and optic nerve head. The OCTA data from Center 1 were split into a training set and an internal validation set, while the data from Center 2 served as an external test set. Features were extracted from OCTA images, and a wrapper-based feature selection strategy identified the optimal subset. A two-tiered stacking modeling framework was used, incorporating baseline model construction, data augmentation using pseudo-labeling, and ensemble integration. Model performance (area under the curve [AUC], accuracy, sensitivity, specificity, precision) was evaluated alongside receiver operating characteristic, calibration, and decision curve analyses. Additionally, the SHapley Additive exPlanation method was used to interpret feature contributions. RESULTS: The ensemble model, OptiDON-Guard, demonstrated superior performance to individual models on both internal validation and external test sets. In the internal validation set, it achieved an AUC of 0.872, accuracy of 0.943, sensitivity of 0.667, specificity of 0.979, and precision of 0.943. In the external test set, it maintained strong performance with an AUC of 0.854, accuracy of 0.816, sensitivity of 0.767, specificity of 0.842, and precision of 0.854. CONCLUSIONS: The OptiDON-Guard is a reliable, interpretable, and clinically applicable tool for identifying DON. TRANSLATIONAL RELEVANCE: The OptiDON-Guard offers a valuable clinical tool to enhance the diagnostic precision of DON.
9 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Digital and AI-enhanced psychosocial interventions in breast cancer patients: a systematic review and meta-analysis
PURPOSE: Digital and AI-supported psychosocial interventions are increasingly implemented to address psychological distress among women with breast cancer. However, their effectiveness and sustainability remain uncertain. This review aimed to systematically evaluate randomised controlled trials (RCTs) assessing the impact of digital and AI-enhanced psychosocial interventions on stress, anxiety, depression, and health-related quality of life (HRQoL). METHODS: Following PROSPERO registration and PRISMA 2020 guidelines, comprehensive searches of multiple databases were conducted up to June 2025. Eligible studies included RCTs involving breast cancer populations and reporting validated psychosocial outcomes. Risk of bias was assessed using the Cochrane RoB 2.0 tool, and certainty of evidence was evaluated with GRADE. Random-effects meta-analyses using standardised mean differences (Hedges' g) were conducted to pool outcomes measured with different instruments. RESULTS: Thirty-five RCTs (n ≈ 7551 participants) met inclusion criteria. Pooled analyses indicated modest but consistent reductions in stress, anxiety, and depressive symptoms, alongside improvements in HRQoL. Interventions grounded in cognitive behavioural therapy, mindfulness, or psychoeducation produced the most reliable outcomes. AI-assisted and blended formats tended to show stronger effects compared with static or minimally interactive platforms. However, heterogeneity in intervention design, short follow-up periods, reliance on self-reported outcomes, and small single-centre samples limited certainty. Cultural adaptation and representation from low- and middle-income countries were minimal. CONCLUSION: Digital and AI-supported psychosocial interventions show promise in reducing distress and improving HRQoL among women with breast cancer. Evidence certainty remains moderate, constrained by methodological limitations. Larger, multicentre trials with longer follow-up and diverse populations are needed to establish sustainability, generalisability, and clinical integration. RELEVANCE: These findings suggest that digital and AI-enhanced psychosocial interventions may serve as adjuncts to conventional care, with potential to improve access to supportive oncology services. Further high-quality, long-term studies are needed before informing guidelines, policy, or widespread implementation. TRIAL REGISTRATION: PROSPERO (CRD420251074813; registered 16 June 2025).
9 Apr 2026
Read appraisal →PloS one
Hybrid deep learning and feature selection approach for autism detection from rs-fMRI data
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that is primarily characterized by deficits in social communication and restricted or repetitive behavioral patterns. Although psychologists contribute significantly to the understanding of ASD, offering insights into its cognitive, emotional, and behavioral dimensions through assessments, diagnoses, therapeutic approaches, and family support, the diagnostic process remains complex. This complexity arises from the diverse manifestations of the disorder and the challenges associated with data sharing. In addition, conventional machine learning approaches for ASD detection may struggle with high-dimensional neuroimaging data and may require careful feature engineering. Consequently, this motivated us to enhance ASD diagnosis by incorporating deep learning (DL) techniques for feature extraction alongside a modified exponential-trigonometric optimization (ETO) algorithm as a feature selection (FS) technique. The modified ETO integrates the Arithmetic Optimization Algorithm (AOA) and the Guided Learning Strategy (GLS) to improve diagnostic performance. To evaluate the effectiveness of the proposed model, we utilized resting-state functional MRI (rs-fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE I). Furthermore, the performance of the proposed model was compared with that of established models. The results indicate that the proposed model achieves competitive and, in most cases, superior performance compared with the benchmark methods, demonstrating superior accuracy, sensitivity, and AUC in diagnosing ASD. On average across the three atlas-based feature sets, the proposed model has an accuracy, sensitivity, and AUC of 73%, 78%, and 79%, respectively.
9 Apr 2026
Read appraisal →BMJ open
A double-blind, crossover, non-inferiority randomised controlled trial where primary care providers and patients compare human-generated and AI-generated digital health messages: the AI-CARE study protocol
INTRODUCTION: Primary care is facing multiple crises, including an increase in health misinformation. Digital health messaging by primary care providers has been shown to reach a diverse patient population. With the uptake of Generative Artificial Intelligence (GenAI) usage in healthcare, there is an important opportunity to rapidly create messages that are tailored to different populations and conditions. However, thoroughly assessing artificial intelligence (AI)-generated content is essential, as GenAI raises concerns regarding its accuracy, understandability, actionability and bias perpetuation. We aim to investigate whether digital health messages created by GenAI are evaluated as non-inferior compared with those created by human experts. METHODS AND ANALYSIS: The AI-CARE (AI to Create Accessible and Reliable patient Education materials) study is a double-blind, crossover, non-inferiority randomised controlled trial. Data collection began on 30 May 2025, and is expected to be completed at the end of May 2026. Over 12 months, 192 messages on 48 topics will be written: half by primary care and public health experts and half by a GenAI tool (OpenAI's ChatGPT). Review Panels composed of 24 primary care providers and 24 patients will evaluate these messages using an Evaluation Grid developed to assess the messages' quality of information, adaptation to the target audience, relevance and usefulness, and readiness to be shared with patients. Evaluations will be completed via online REDCap (Research Electronic Data Capture) surveys and the order in which the 192 messages appear will be randomised and will vary between individuals. Participants and analysts will be blinded to the generation source. The primary outcome will be the Clarity and Understandability score. ETHICS AND DISSEMINATION: The Research Ethics Boards of the Hôpital Montfort (24-25-11-038) and the University of Ottawa (S-12-24-11153) formally approved this study in December 2024. Reported data will be grouped and anonymised for dissemination in peer-reviewed scientific journals and conferences. TRIAL REGISTRATION NUMBER: NCT06997107.
9 Apr 2026
Read appraisal →JMIR mHealth and uHealth
Effectiveness of a Telecare-Based Intervention Program in Supporting Informal Caregivers of Community-Dwelling Older Adults in Reducing Stress Levels: Randomized Controlled Trial
BACKGROUND: Informal caregivers are essential in supporting community-dwelling older adults, especially as global populations age. However, caregiving responsibilities often result in high stress, depressive symptoms, and diminished quality of life. Traditional support services are fragmented, and many programs overlook caregivers' emotional and informational needs. Telecare-the remote delivery of health and social care via digital technologies-offers a promising approach to provide accessible, personalized, and timely support. OBJECTIVE: This study evaluated the effectiveness of a telecare-based intervention program, developed by a health-social partnership team, in improving psychological well-being and caregiving outcomes among informal caregivers of community-dwelling older adults. We hypothesized that caregivers in the intervention group would show greater improvements in stress, self-efficacy, depression, quality of life, and caregiving burden compared to those receiving usual care. METHODS: A single-blind randomized controlled trial was conducted from January to December 2023, with 75 informal caregivers providing care to older adults (aged ≥60 y) for at least 4 hours per week. Participants were randomized to an intervention group (n=38, 51%) or a control group (n=37, 49%). The 3-month intervention included (1) biweekly online nurse case management supported by a multidisciplinary health-social team, (2) personalized WhatsApp videos on caregiving skills, and (3) a password-protected caregiver website offering resources and peer discussion. The control group attended 6 in-person educational sessions at a community center. Outcomes such as stress, self-efficacy, depression, quality of life, and caregiving burden were measured at baseline and postintervention. RESULTS: Participants had a mean age of 65.5 (SD 9.617) years, and 81.3% (61/74) were female. Baseline characteristics were similar between groups. Compared to controls, the intervention group showed significantly higher self-efficacy (t=1.98; P=.04) and lower depression (t=-2.24; P=.03) at follow-up. Within-group improvements in the intervention group were also observed for stress, depression, self-efficacy, mental quality of life, and caregiving burden. No significant between-group differences were found for stress, quality of life, or caregiving burden. No significant within-group improvements were observed in the control group. CONCLUSIONS: This study provides novel evidence that a digitally delivered, nurse-led telecare program-integrating health and social services-can improve psychological outcomes among informal caregivers. These findings have practical implications for health systems seeking scalable, low-barrier solutions to support caregivers. Telecare models may be a valuable complement to traditional services, enhancing caregiver resilience and promoting aging in place while alleviating long-term strain on institutional care systems.
9 Apr 2026
Read appraisal →JMIR human factors
e-Mental Health Intervention Preferences Among Informal Caregivers of Adults Living with Chronic Kidney Disease: Semistructured Interview Study
BACKGROUND: Informal caregivers of people living with chronic kidney disease commonly experience mental health difficulties. However, there is currently limited access to scalable psychological interventions such as those using e-mental health platforms. Furthermore, there is an absence of such interventions tailored for this group of caregivers, which may limit acceptability. OBJECTIVE: To inform future e-mental health intervention development, this study aimed to explore preferences for e-mental health interventions among informal caregivers of people living with chronic kidney disease. METHODS: A total of 13 informal caregivers of people living with chronic kidney disease participated in semistructured interviews. Eligible informal caregivers lived in the United Kingdom and were recruited via nonprofit organizations and social media advertisements. Semistructured interviews followed a topic guide exploring caregivers' e-mental health intervention preferences. Data were analyzed using manifest content analysis. RESULTS: A total of 4 categories and 13 subcategories were generated. "Design preferences" illustrates key intervention design features that were important to caregivers, such as involving caregivers and other interest-holders in the design process. "Content preferences" describes the information caregivers valued in an intervention, such as information about chronic kidney disease and resources to support caregivers. "Support preferences" reflects the characteristics of support caregivers preferred if support was available as part of the intervention, such as peer support and providing support that accommodates individual preferences. Lastly, "Dissemination preferences" describes methods caregivers recommended to promote intervention uptake, such as engaging communication strategies and trusted and accessible sources of information. CONCLUSIONS: Caregivers identified a number of e-mental health intervention design and delivery preferences that should be considered when developing e-mental health interventions for caregivers of people living with chronic kidney disease. To ensure such interventions are acceptable, feasible, and relevant, co-design approaches should be adopted during the intervention development process.
8 Apr 2026
Read appraisal →JMIR formative research
Real-World Data From a Group Parent Management Training Program Enhanced Using Artificial Intelligence: Qualitative Study
BACKGROUND: Parent management training (PMT) is an evidence-based intervention for children with disruptive behavior problems. However, access to care is often limited by cost, availability of clinicians, scheduling, and transportation barriers. Integrating artificial intelligence (AI) into group PMT may improve accessibility, personalization, and adherence, while preserving therapeutic quality. OBJECTIVE: This study explored caregivers' experiences with a hybrid PMT program that combined live therapist-led group sessions with asynchronous support from Pat, an AI conversational agent designed to augment the therapists' support to caregivers. METHODS: A total of 88 caregivers of children aged 3-14 years (mean 7.98, SD 2.45 years) from Argentina and Paraguay participated in eight weekly online group sessions led by human therapists and supplemented by Pat. Caregivers were asked to provide the net promoter score (NPS) and their perceived contribution of their experience in remote group sessions and with Pat. Caregiver perspectives were analyzed using thematic analysis by multiple coders with consensus and interrater reliability assessment. RESULTS: The average NPS was 76.92, indicating excellent satisfaction. Regarding participants' perceptions of the overall program, the most frequent theme was useful strategies (73/202, 36.1%), reflecting the value placed on clear, structured, and practical tools to address everyday parenting challenges. Most comments about Pat were positive (156/164, 95.1%), particularly highlighting its 24/7 accessibility and constant availability (69/164, 42.1%). Recommendations for improvement mainly focused on enhancing the user experience and incorporating additional functionalities. Regarding perceived contribution to progress, caregivers attributed, on average, 61% to Pat and 46% to the group sessions. CONCLUSIONS: Combining therapist-led PMT group sessions with AI support appears feasible, acceptable, and valued by caregivers and may expand reach without sacrificing quality. Overall, caregivers valued the useful strategies and the professional and peer support and reported positive changes. Regarding Pat, the most valued aspect is the constant and immediate support. This model integrates human expertise with the accessibility and continuity provided by AI, reducing barriers, such as time, cost, and clinician availability.
8 Apr 2026
Read appraisal →Journal of medical Internet research
A Gamified Mobile Health Intervention to Promote Physical Activity, Executive Function, and Mental Health in College Students: Randomized Controlled Trial
BACKGROUND: College students commonly experience suboptimal health conditions, including insufficient physical activity (PA), excessive body weight, and declining physical fitness. Traditional interventions face low adherence, while gamified mobile health (mHealth) programs may improve engagement and outcomes. OBJECTIVE: This study aimed to evaluate the feasibility and effectiveness of a novel gamified, incentive-based mHealth intervention on primary outcomes (PA and adherence) and secondary outcomes (physical fitness, body composition, executive function [EF], and mental health). METHODS: A 2-arm parallel-group randomized controlled trial (RCT) was conducted in 2025 at Yantai University with 160 college students (18-25 years; BMI 18.5-30.0) who were randomized 1:1 (computer-generated, sex-stratified blocks of 4; concealed allocation) to the intervention group (IG) or control group (CG; n=80 each); major exclusions were contraindications to exercise, severe physical/mental illness, recent PA interventions, or psychotropic medication use. Both used the same fitness watch-app system and identical PA targets (≥150 min moderate-to-vigorous physical activity [MVPA] per week or ≥900 metabolic equivalent-minutes [MET-min] per week); IG additionally received team-based gamification (competition, points/leaderboards, feedback, and rewards), while CG received monitoring only. PA and adherence were monitored throughout the 8-week intervention; other outcomes were assessed at baseline and 8 weeks (fitness, body composition, EF, and mental health). Open-label with blinded outcome assessors/analysts; intention-to-treat (ITT) with multiple imputation. RESULTS: At 8 weeks, data were available for 154 participants (IG 78; CG 76); all 160 were analyzed per ITT. Compared to the CG, the IG demonstrated significantly higher mean levels in all primary PA outcomes over 8 weeks (daily steps: mean 10,356, SD 1245 versus 8242, SD 1087; Δ=2114; d=1.81, 95% CI 1.44-2.18; P<.001; daily MVPA: mean 71, SD 15 versus 43, SD 12 min; Δ=28 min; d=2.06, 95% CI 1.68-2.45; P<.001; and weekly MET-min: mean 1650, SD 310 versus 1340, SD 285; Δ=310; d=1.04, 95% CI 0.71-1.37; P<.001). Adherence was also higher in the IG (both P<.001). Key secondary outcomes, including skeletal muscle mass (Δ +0.54 kg), body fat percentage (Δ -0.46 percentage points), 800/1000 m run time (Δ -6.5 s), and depressive symptoms (Δ -2.0 points), significantly favored the IG (all P<.001). No serious adverse events occurred. CONCLUSIONS: This RCT demonstrated that integrating gamification into an mHealth platform significantly enhanced PA, intervention adherence, and selected health outcomes among college students. The key innovation lies in the trial design, which held the technology platform and activity goals constant across groups, thereby allowing a clearer estimate of the incremental contribution of gamification. Unlike many existing studies, this approach reduces confounding from co-occurring intervention components in between-group comparisons. It therefore provides more comparable and methodologically robust evidence on the specific efficacy of gamification in mHealth interventions. For real-world implementation, this model leverages the existing smartphone and wearable ecosystem, offering a low-cost, scalable strategy for university health-promotion programs.
8 Apr 2026
Read appraisal →JMIR human factors
Use of Digital Peer Support for Employee Well-Being: Retrospective Analysis Across Five Large Employers
BACKGROUND: Anonymous, 24/7 digital peer support (DPS) offers a scalable solution to support employees' emotional well-being. Understanding sociobehavioral factors, such as timing of engagement and the impact of shared resources, can help employers and employee assistance programs (EAPs) integrate digital tools to better support workforce well-being. OBJECTIVE: This study examines (1) outcomes via overall sentiment changes among DPS users; (2) sociobehavioral differences between employees who accessed real-time, anonymous DPS during versus outside business hours; (3) differences in sentiment outcomes based on time of use; and (4) the impact of in-session resource sharing on sentiment improvement. METHODS: Using OpenAI's large language model (LLM) GPT-4o-mini with a few-shot learning approach, 24,818 anonymous chat conversations from 13,879 employees at 5 large employers were evaluated for subclinical sentiment variables, including loneliness, sadness, stress, anxiety, depression, despair, helplessness, and optimism. RESULTS: Distinct activity patterns were observed between employees during and outside business hours, with a median user age of 36 years. During business hours, employees reported higher baseline stress (Δ1.6%), whereas outside business hours, baseline depression (Δ1.7%) and loneliness (Δ1.2%) were higher. After DPS use, LLM-derived negative sentiment scores decreased (loneliness 46% reduced, sadness 45%, stress 46%, anxiety ~39%, depression ~40%, despair ~40%, and helplessness ~38%), and optimism increased (~77%). Outside business hours, users engaged more (~68% of all sessions), remained in sessions 19% longer, and discussed 13% more topics, whereas users during business hours reported greater improvements in depression (Δ2.3%), helplessness (Δ1.9%), and loneliness (Δ0.5%). Resource sharing was associated with greater improvements in loneliness (Δ2.9%), stress (Δ1.3%), anxiety (Δ1.9%), depression (Δ7.3%), despair (Δ3.0%), helplessness (Δ2.8%), and optimism (Δ8.8%), but not sadness. CONCLUSIONS: DPS complements employers' EAPs by addressing employee engagement gaps, reducing barriers to mental health care, and promoting emotional well-being among the workforce.
8 Apr 2026
Read appraisal →Journal of medical Internet research
Social Media Intervention Based on the Information-Motivation-Behavioral Skills Model Promotes HIV Testing and Reduces High-Risk Behaviors Among Men Who Have Sex With Men in Resource-Limited Settings in China: Randomized Controlled Trial
BACKGROUND: Social media intervention may enhance HIV prevention among men who have sex with men, but the effect of this intervention in resource-limited settings remains unclear. OBJECTIVE: This randomized controlled trial evaluated whether a social media intervention grounded in the information-motivation-behavioral skills (IMB) model could be beneficial for HIV prevention among men who have sex with men in resource-limited settings. METHODS: Participants were recruited in Nanning, China, between April 2023 and April 2024. Eligible participants were randomly assigned to either the social media intervention group or the routine HIV prevention services control group. Participants in the intervention group received a 3-month social media intervention, which included completing video-based tasks. Baseline surveys were conducted, followed by follow-up surveys every 3 months, for a total of 2 follow-ups. Outcomes included HIV testing uptake, high-risk behavior, AIDS-related knowledge, safe sex self-efficacy, and attitude. RESULTS: A total of 180 eligible men who have sex with men were enrolled (90 per group). Follow-up rates were 97.8% (88/90) and 95.5% (86/90) for the intervention and control groups, respectively. At the follow-ups, the intervention group demonstrated significantly higher uptake of HIV testing, a lower proportion of participants reporting high-risk sexual behaviors, and higher condom use self-efficacy compared to the control group (all P<.05). After controlling for sociodemographic variables, generalized estimating equations analysis revealed that the intervention group had significantly higher odds of HIV testing (risk ratio [RR] 1.739, 95% CI 1.110-2.730), HIV self-testing (RR 2.306, 95% CI 1.593-3.340), and consistent condom use (RR 2.457, 95% CI 1.636-3.690) than the control group. Cochran-Armitage trend tests within the intervention group revealed that with increasing intervention duration, both HIV testing and HIV self-testing significantly increased, while high-risk sexual behaviors significantly decreased (all P<.05). CONCLUSIONS: The social media intervention guided by the IMB model demonstrated a positive effect on expanding HIV testing coverage, reducing high-risk behavior, enhancing AIDS-related knowledge, and improving safer sex self-efficacy among men who have sex with men in resource-limited settings. These findings provide valuable guidance for future HIV prevention and control efforts targeting this population.
8 Apr 2026
Read appraisal →JMIR mHealth and uHealth
A Mobile App-Based Individualized Nonpharmacological Intervention for Behavioral and Psychological Symptoms in Dementia: Pilot Randomized Controlled Trial
BACKGROUND: Behavioral and psychological symptoms of dementia (BPSD) are common and negatively impact both individuals with dementia and their families. Although nonpharmacological interventions are recommended as the first-line treatments, their use in community settings is limited by access and caregiver resources. Existing approaches are often not individualized and rely on institutional or clinician-led delivery. OBJECTIVE: We developed a caregiver-initiated and individualized multimodal mobile app. The app delivers tailored nonpharmacological interventions-such as music therapy, exercise, and reminiscence therapy-based on each user's preferences and functional abilities. This study aimed to evaluate the effectiveness of this mobile app-based intervention in reducing BPSD in community-dwelling persons living with dementia. METHODS: This study used a single-blinded randomized controlled trial design. Participants were recruited from an outpatient clinic of a tertiary hospital, a dementia care center, and 5 home care service centers. A total of 36 dyads participated, each comprising a community-dwelling person living with dementia aged 60 years or older and their primary family caregiver. The dyads were randomly allocated to either the intervention or control group. The intervention group received a caregiver-initiated, multimodal, mobile app-based individualized intervention for 4 weeks, whereas the control group continued with usual care. The primary outcomes were overall BPSD, agitated behavior, and depression. The secondary outcomes were nighttime sleep efficiency and caregiver competency in managing BPSD. Assessments were conducted at baseline, immediately after the intervention, and at a 2-week follow-up. RESULTS: Of the 36 randomized dyads, 33 were included in the final analysis. Although the intervention group showed greater reductions in overall BPSD, agitated behavior, and depression after the intervention, no significant group-by-time interaction effects were observed in the total sample. In the subgroup analysis of participants with clinically significant baseline BPSD, a statistically significant improvement in overall BPSD was found in favor of the intervention group (β=-12.885, 95% CI -24.530 to -1.240; P=.03). No significant effects were observed for either nighttime sleep efficiency or competence in the management of BPSD. CONCLUSIONS: A mobile app-based individualized intervention may offer a flexible, caregiver-initiated approach to managing BPSD in home-care settings. While overall effects were limited, exploratory subgroup findings provided meaningful insights, indicating potential benefits for those with higher baseline symptom severity. The results highlight the need for further research on adaptive personalization and optimized intervention delivery to enhance the clinical effectiveness of digital dementia care.
8 Apr 2026
Read appraisal →Journal of medical Internet research
Electrocardiogram-Based Mental Stress Detection Amid Everyday Activities Using Machine Learning: Model Development and Validation Study
BACKGROUND: Frequent, sustained stress is linked to poor health and requires monitoring for early intervention. Electrocardiograms (ECG) are promising biomarkers because they can be recorded noninvasively and continuously using wearable devices. However, tracking stress with ECG is challenging because daily activities elicit responses similar to mental stress (MS), and various mental stimuli that individuals encounter complicate the use of machine learning (ML) models trained on a limited set of stressors. OBJECTIVE: We (1) evaluated the ability of ML models to distinguish MS episodes from a composite "no-stress" background, including rest and low- to moderate-intensity activities; (2) assessed their generalizability to new stressors and participants; and (3) tested robustness to lower sampling rates and fewer features, to explore their suitability for lightweight wearables. METHODS: We used a comprehensive ECG dataset sampled at 1000 hertz from 127 participants who underwent various mental stressors and engaged in diverse physical activities. A 30-second window was used to extract 55 features from time, frequency, nonlinear, and morphological domains. We trained a logistic regression (LR) model and an extreme gradient boosting (XGBoost) model, splitting the data into 60/20/20 for training, validation, and testing. Shapley additive explanation values were computed to explain model predictions. Additional analyses included leave-one-stressor-out; downsampling to 500, 250, and 125 hertz; a time-window sensitivity analysis; and reducing the number of features to as few as 5. RESULTS: XGBoost achieved an area under the receiver operating characteristic curve (AUROC) of 0.741 (95% CI 0.701-0.783) and an area under the precision-recall curve (AUPRC) of 0.706 (95% CI 0.658-0.753), compared with 0.724 (95% CI 0.678-0.772) and 0.691 (95% CI 0.639-0.742) for LR. The mean performance difference between XGBoost and LR was 0.017 for AUROC (95% CI 0.001-0.032) and 0.015 for AUPRC (95% CI -0.001 to 0.037; clustered bootstrap analysis using 2000 participant-level resamples), suggesting that LR performs comparably to the nonlinear XGBoost model. Both models were robust to downsampling and feature reduction (10 features retained >93% of performance). Extending the analysis window to 60 seconds improved model performance across all sampling rates, highlighting a trade-off between rapid detection and overall performance. When evaluating discrimination from physical activity, models achieved acceptable specificity for light physical activity (XGBoost: 0.787; LR: 0.794) but poor specificity for moderate physical activity (XGBoost: 0.418; LR: 0.444). Both models generalized to most unseen stressors, although performance varied across stressors, with limited transfer to the social-evaluative stressor. Feature importance analysis revealed fuzzy entropy and frequency-based features as key predictors. CONCLUSIONS: ML models can detect MS with high sensitivity and remain robust to lower sampling rates and fewer features. Generalization to novel stressors was stressor-dependent. Importantly, our results highlight challenges in distinguishing stress-related cardiac responses from those caused by physical exertion, revealing critical limitations of single-sensor ECG approaches for MS detection.
8 Apr 2026
Read appraisal →Physiological measurement
NeuroSleep: neuromorphic event-driven single-channel EEG sleep staging for edge-efficient sensing
Objective. Reliable, continuous neural sensing on wearable edge platforms is fundamental to long-term health monitoring; however, for electroencephalography (EEG)-based sleep monitoring, dense high-frequency processing is often computationally prohibitive under tight energy budgets.Approach. To address this bottleneck, this paper proposes NeuroSleep, an integrated event-driven sensing and inference system for energy-efficient sleep staging. NeuroSleep first converts raw EEG into complementary multi-scale bipolar event streams using Residual Adaptive Multi-Scale Delta Modulation, enabling an explicit fidelity-sparsity trade-off at the sensing front end. Furthermore, NeuroSleep adopts a hierarchical inference architecture that comprises an Event-based Adaptive Multi-scale Response module for local feature extraction, a Local Temporal-Attention Module for context aggregation, and an epoch-leaky integrate-and-fire module to capture long-term state persistence.Main results. Experimental results using subject-independent 5-fold cross-validation on the sleep-EDF expanded sleep-cassette subset (78 subjects, 153 overnight recordings) with single-channel EEG demonstrate that NeuroSleep achieves a mean accuracy of 74.2% with only 0.932 M parameters while reducing sparsity-adjusted effective operations by approximately 53.6% relative to dense processing. Compared to the representative dense Transformer baseline, NeuroSleep improves accuracy by 7.5% with a 45.8% reduction in computational load.Significance. By coupling neuromorphic event encoding with state-aware context modeling, NeuroSleep offers a deployment-oriented framework for single-channel sleep staging that reduces redundant high-rate processing and improves energy scalability for wearable and edge platforms.
8 Apr 2026
Read appraisal →JMIR research protocols
Targeted Real-Time Assessment of Chronic Pain (TRAC-Pain) in Youth: Protocol for a Digital Biosignature Development Through a Prospective Observational Cohort Study
BACKGROUND: Approximately 1 in 5 children and adolescents live with chronic pain, with musculoskeletal (MSK) pain being one of the most prevalent subtypes. Unfortunately, some studies show that less than half of youth (<18 years) experience improvements with existing evidence-based treatments. Self-report measures-the current gold standard for monitoring the pain experience-are limited in their use as a single point-of-care assessment and their vulnerability to recall bias. The ubiquitous adoption of wearable technology presents a promising solution for improved monitoring of the pain experience via real-time tracking through a multisystemic lens. OBJECTIVE: The purpose of this study is (1) to develop a digital biosignature of the pain experience in youth with chronic MSK pain and (2) to assess the feasibility, acceptability, and appropriateness of the captured data for future applications. METHODS: All aspects of this study were designed in partnership with people with lived experience and patient advocacy partners. This is a longitudinal observational cohort study, with all study activities taking place remotely over a 12-week period. Up to 500 youth (between 14 and 24 years old) with chronic MSK pain will be enrolled through a multipronged recruitment strategy to ensure a representative sample. The participants will wear an Apple Watch throughout the study to continuously monitor physiological, sleep, and physical activity metrics. In addition, participants will complete brief "daily check-in" surveys that include gold-standard measures of the pain experience (eg, pain interference, mood, fatigue) and the option to report a "pain flare" (ie, a temporary but noticeable worsening of the usual pain symptoms). Participants will also complete a modified online Trier Social Stress Test and a 30-second sit-to-stand task to capture individual responses to standardized challenges. Traditional machine learning and deep representation learning methods will be used to develop a digital biosignature of the pain experience. The accuracy of the biosignature will be assessed through measures of model performance as compared to gold-standard self-reports. RESULTS: This study was funded in September 2024, with data collection beginning in March 2025. As of December 15, 2025, 190 participants are enrolled, with data collection and analysis ongoing. CONCLUSIONS: This is the first study to leverage wearable health technology for real-time monitoring of the pain experience in youth with chronic MSK pain. The resulting digital end points are expected to heighten the rigor of clinical trials and provide opportunities for individually tailored interventions. The second phase of this study will investigate the implementation of "wellness alerts" triggered by abnormal smartwatch readings. Alerts would empower users toward preemptive self-management strategies, thereby enhancing self-efficacy in those living with chronic MSK pain. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/84781.
8 Apr 2026
Read appraisal →Progress in biomedical engineering (Bristol, England)
Trustworthy AI in digital health: a comprehensive review of robustness and explainability
Ensuring trust in artificial intelligence (AI) systems is essential for the safe and ethical integration of machine learning systems into high-stakes domains such as digital health. Key dimensions, including robustness, explainability, fairness, accountability, and privacy, need to be addressed throughout the AI lifecycle, from problem formulation and data collection to model deployment and human interaction. While various contributions address different aspects of trustworthy AI, a focused synthesis on robustness and explainability, especially tailored to the healthcare context, remains limited. This review addresses that need by organizing recent advancements into an accessible framework, highlighting both technical and practical considerations. We present a structured overview of methods, challenges, and solutions, aiming to support researchers and practitioners in developing reliable and explainable AI (XAI) solutions for digital health. This review article is organized into three main parts. First, we introduce core pillars of trustworthy AI and discuss the technical and ethical challenges they pose, particularly in the context of digital health. Second, we explore application-specific trust considerations across domains such as intensive care, mental health, metabolic disease, and public health surveillance, highlighting how explainability, clinical validation, and human oversight support trust. Lastly, we present recent advancements in techniques aimed at improving robustness under data scarcity and distributional shifts, as well as XAI methods ranging from feature attribution to gradient-based interpretations and counterfactual explanations. This paper is further enriched with detailed discussions of the contributions toward robustness and explainability in digital health, the development of trustworthy AI systems in the era of large language models, and various evaluation metrics for measuring trust and related parameters such as validity, fidelity, and diversity, offering a roadmap for building safer and more reliable AI systems.
8 Apr 2026
Read appraisal →JMIR formative research
Evaluation of GPT-5 in Periodontitis Staging and Grading: Retrospective Observational Study
BACKGROUND: Periodontitis is a chronic gum disease affecting approximately 42% of adults aged 30 years and older in the United States. Training dental students to accurately diagnose and manage periodontitis is a critical component of dental education and clinical care. Recent advances in large language models offer new opportunities to support both domains, yet their performance in periodontal diagnosis remains largely unexplored, particularly for newer models such as GPT-5. OBJECTIVE: This study conducted an exploratory evaluation of GPT-5's ability to stage and grade periodontitis. METHODS: A total of 25 publicly available clinical cases explicitly reporting periodontitis stage and grade were identified through Google and PubMed searches. Each case description was entered into GPT-5 using a zero-shot prompting approach to assess guideline-based reasoning without exemplar conditioning. The model's predictions were compared with the published reference diagnoses. Performance was measured using accuracy, 95% CI, unweighted Cohen κ, and weighted Cohen κ. RESULTS: Across these cases, GPT-5 showed marked class-dependent performance and a tendency to overestimate disease severity. Grading performance was notably imbalanced, with high recall for grade C but substantially lower discrimination for grade B. GPT-5 achieved a staging accuracy of 68% (95% CI 48.4%-82.8%) and a grading accuracy of 77.3% (95% CI 56.6%-89.9%), with corresponding Cohen κ values of 0.454 (95% CI 11.0%-75.6%) and 0.179 (95% CI -15.8% to 63.8%), respectively. While staging performance showed fair agreement beyond chance, the low κ for grading indicates poor agreement and limited reliability in distinguishing periodontal disease severity. CONCLUSIONS: These findings suggest that although GPT-5 demonstrates potential for guideline-based periodontitis staging and grading, its current diagnostic performance, particularly for periodontitis grading, limits its use in clinical assessment and educational training. Meaningful application in periodontal diagnosis and training will require substantial improvements in reliability and rigorous validation in larger, more diverse, and prospectively collected datasets.
7 Apr 2026
Read appraisal →JMIR human factors
Factors Influencing User Satisfaction in Accessing Health Data: Cross-Sectional Survey of United Kingdom Adults
BACKGROUND: While patient health records (PHRs) are becoming ubiquitous, nationwide evidence on the drivers of user satisfaction in the United Kingdom remains scarce. OBJECTIVE: This study aimed to quantify determinants of PHR user satisfaction in a nationally representative sample of United Kingdom adults and to contrast real-world experiences with hypothetical expectations among individuals without PHR exposure. METHODS: We conducted a cross-sectional online survey in March 2022 using stratified quota sampling (eg, age, sex, ethnicity) through the Prolific platform. Of 1001 respondents, 533 (53%) were female (mean age 41, SD13) years, 468 (47%) reported previous PHR use ("experienced" cohort), and 533 (53%) respondents did not ("hypothetical" cohort). Primary outcomes were five satisfaction items (5-point Likert), overall PHR evaluation (0-100), and stated/anticipated PHR functions. Two-sample t-tests with unequal variances examined between-group differences; multivariate analysis of variance (MANOVA) assessed demographic equivalence. Significance was set at P<.05. RESULTS: Respondents with PHR experience rated their record easy-to-use in 79% (370/468) of cases versus an anticipated 93% (498/533) among nonusers (P<.001). Experienced users rated appointment-scheduling time-saving at 68% (319/468) compared with 72% (387/533) of nonusers (P=.17). Mean overall evaluation scores were 71.0 (SD 19.5) versus 74.2 (SD 18.6) (t₉₉₇=2.25, P=.02). MANOVA confirmed demographic balance (Wilks Λ=.99, F₇,₉₉₃=1.48, P=.18). Preferred functions across cohorts were viewing personal health information (experience 81%, hypothetical 90%) and lab results (50% vs 76%). Users lacking experience consistently over-estimated future use of carer-related functions (eg, children's data: 36% vs 12%, P<.001). CONCLUSIONS: Adults in the United Kingdom value PHRs primarily for personal data access and scheduling convenience. Nonusers displayed optimistic expectations about carer-related features and breadth of functionality, indicating an information gap. Enhancing transparency about current capabilities and expanding features that facilitate caregiving could increase satisfaction and adoption.
7 Apr 2026
Read appraisal →JMIR formative research
Performance of DeepSeek V3, DeepSeek R1, ChatGPT 4o, and ChatGPT o1 on the National Health Professional and Technical Qualification Examination (Intermediate Level) in China: Comparative Analysis
BACKGROUND: In recent years, large language models (LLMs) have undergone swift cycles of refinement and iteration. However, in the realm of clinical medicine, different LLMs' capability of logical reasoning and disease diagnosis needs further investigation. OBJECTIVE: The aim of our study was to evaluate the performance of 4 different LLMs in the National Health Professional and Technical Qualification Examination in China. METHODS: A total of 398 multiple-choice questions of 5 different question types were integrated within the examination with respect to the diagnosis or care of cases. These questions were categorized into different cardiology subspecialties and different clinical disciplines. DeepSeek V3 and R1 were accessed through an application programming interface, while ChatGPT 4o and o1 were queried via its public chat-based interface. We offered the same prompts instructing LLMs to assume the role of a physician and provide answers with explanations at the beginning of each conversation. We assessed different LLMs' performance by the accuracy in the responses to the multiple-choice questions. For the first 3 examination sections, McNemar test was used to compare the accuracy among the models, with post hoc pairwise comparisons performed using partitions of chi-square method and Bonferroni correction (significance set at P<.008). For the fourth section involving partially credit scoring, one-way ANOVA was performed to compare the mean scores among the models, with statistical significance set at P<.05. RESULTS: Both DeepSeek V3 and R1 showed superior performance in the first 3 sections of the Chinese National Health Professional and Technical Qualification Examination, achieving an overall performance of 93% and 93.6%, respectively. ChatGPT 4o and o1 achieved accuracies of 73.3% and 69%, respectively (all P<.001 compared with DeepSeek V3). For the fourth section, the performance of all 4 LLMs markedly declined compared to their results in the preceding sections. Particularly, in the section of gastroenterology and hematology, DeepSeek V3 achieved the highest accuracy, while R1 ranked first in cardiology and neurology. ChatGPT o1 achieved the highest accuracy in the topic of coronary artery disease, with no statistical significance. CONCLUSIONS: DeepSeek V3 and R1 showed remarkable potential in facilitating clinical decision-making in the Chinese professional examination, with both outperforming ChatGPT 4o and o1. Nonetheless, future research should continue evaluating their economic efficiency and susceptibility to hallucination.
7 Apr 2026
Read appraisal →JMIR human factors
Chatbot Usability Scale in Chinese Users: Cross-Cultural Adaptation and Validation Study
BACKGROUND: Chatbots are increasingly deployed across various domains; however, systematic evaluation of their usability remains limited, particularly in non-Western contexts. The 11-item Chatbot Usability Scale (BUS-11), a multidimensional instrument grounded in human-computer interaction theory, has demonstrated strong psychometric properties in prior studies; however, a validated Chinese version does not exist, despite China being one of the largest chatbot markets. OBJECTIVE: This study aimed to translate, culturally adapt, and validate the BUS-11 for Chinese users. METHODS: Following established cross-cultural adaptation procedures, the scale was forward- and back-translated, reviewed by an expert committee, and pilot-tested for clarity and feasibility. A main validation study was then conducted with 214 participants, who completed 438 evaluations of the chatbots across 10 widely used systems. RESULTS: Psychometric analyses demonstrated excellent scale-level content validity index (ie, 0.92), strong internal consistency (Cronbach α=0.92), and a clear 3-factor structure (Accessibility, Interaction Process Quality, and Information Quality), explaining 56.1% of the variance. Meanwhile, privacy or security and response time were retained as single-item indicators. The Chinese BUS-11 was concise, user-friendly, and psychometrically robust. CONCLUSIONS: This work fills a critical gap by providing the first validated instrument for assessing chatbot usability in Chinese contexts, enabling reliable cross-cultural comparisons and supporting both research and practical design evaluation in human-computer interaction.
7 Apr 2026
Read appraisal →PloS one
Appreciating the complexity of frailty and user context in digital health intervention design: A qualitative study with personas
Frailty is a complex and dynamic process that is becoming increasingly common as the population ages. Early intervention has been shown to reverse or slow progression, but requires support from increasingly limited healthcare resources. Digital health interventions could potentially assist, but first it is vital to understand older adults' experiences and perspectives. This study describes feedback from older adults with symptoms of pre or early frailty on the design concept of a digital device intended to function as an adjunct to physiotherapy and occupational therapy by increasing frequency and quality of safe movement at home. Objectives were to better understand experiences of frailty, and to identify potential influencing factors on adoption and adherence. In-person audio-recorded qualitative interviews were conducted with 17 older adults and 5 carers recruited from community settings. Reflexive thematic analysis generated two themes: 'autonomy and prompts: importance of personalisation'; and 'digital interfaces and accountability: importance of usability and purpose'. In addition, three personas were generated from the data, each describing different needs and preferences for such a device. Situating the results of analysis in the context of current literature, a set of Guiding Principles was created to support future design development in this area. These highlight important factors for consideration when designing for this population, and provide key design feature suggestions and implementation strategies to address these factors including: ensuring the device has clear purpose; is adaptable to the diversity of needs within this clinical population; any form of feedback is safe, relevant, useful and rewarding to multiple users; and technical support is accessible and ongoing. It is recommended that the target population, and clinical purpose be more clearly defined within the healthcare context to encourage personal rehabilitation through enhancing the therapeutic alliance rather than focussing on individual behaviour change of diverse older adults.
7 Apr 2026
Read appraisal →Journal of medical Internet research
Supporting Access to Care Through Peripheral Devices and Patient-Generated Health Data: Qualitative Study
BACKGROUND: In 2016, the US Department of Veterans Affairs (VA) implemented a national initiative to distribute video-enabled tablets and peripheral devices, such as blood pressure monitors and weighing scales, to patients facing geographic, clinical, or socioeconomic challenges. Such patients could potentially benefit from health monitoring in conjunction with video-based care, as peripheral devices offer opportunities to enrich care received during a video visit and support tracking of health-related data collected outside of clinical care, or patient-generated health data. However, little is known about experiences with the devices and how they could support improved access to care. OBJECTIVE: We explored patients' experiences with VA-issued peripheral devices and their impact on video-based care and health monitoring outside of clinical visits. METHODS: We conducted in-depth semistructured interviews among patients who received VA-issued tablets and peripheral devices between 2023 and 2024. Purposive sampling was used to gather views based on gender, age, race or ethnicity, and rurality. Interviews were transcribed and analyzed using rapid qualitative analysis, guided by the Unified Theory of Acceptance and Use of Technology. RESULTS: Among 25 patients, most received a blood pressure monitor (21/25, 84%), a weight scale (14/25, 56%), and/or a pulse oximetry device (12/25, 48%). The majority reported using their peripheral devices (23/25, 92%) and tablets (19/25, 76%) to monitor their vital signs and attend video visits. Qualitative analysis yielded ten themes reflecting experiences and impacts of the devices, organized by the Unified Theory of Acceptance and Use of Technology constructs: "effort expectancy" consisted of (1) familiar and easy to use devices and (2) challenges of Bluetooth pairing and measurement; "performance expectancy" consisted of (3) integration with video visits, (4) health monitoring for peace of mind, (5) perceptions of improved vital signs and lifestyle behaviors, (6) removing obstacles to in-person care, and (7) desiring an overall picture of health; "social influence" consisted of (8) fostering care team connections and (9) promoting awareness of tablets and peripheral devices; and "facilitating conditions" consisted of (10) supportive help desk infrastructure. Overall, patients described using peripheral devices during virtual visits by syncing data to the tablet for real-time access by their care team. They also reported manually tracking and sharing patient-generated health data with their care team. Despite some challenges with Bluetooth pairing, patients found the devices easy to use and contributed to improved health and motivation. Devices also reduced logistical burdens of in-person visits, especially for those with limited mobility, visual impairments, mental health needs, or transportation barriers. CONCLUSIONS: Patients perceive that peripheral devices can enhance video-based care and support health care access and chronic disease management. Patients reported benefits to health, behavior, and communication with care teams. To maximize the impact, program enhancements should prioritize device interoperability, accessible training, and expanded outreach.
7 Apr 2026
Read appraisal →JMIR research protocols
Remote Management of Patients With Diabetic Macular Edema Receiving Long-Term Intravitreal Anti-Vascular Endothelial Growth Factor Therapy (RE-SHINE Study): Protocol for a Real-World Study
BACKGROUND: Long-term intravitreal anti-vascular endothelial growth factor therapy is the gold standard for treating diabetic macular edema (DME). However, poor treatment adherence in real-world settings often leads to suboptimal visual outcomes. Evidence-based remote management strategies are required to bridge the gap between clinical protocols and patient self-management. OBJECTIVE: This study aims to describe a remote management program based on the Behavior Change Wheel model designed to increase treatment adherence and improve disease outcomes for patients with DME receiving long-term anti-vascular endothelial growth factor therapy, and to report the study protocol. METHODS: The RE-SHINE study was structured into three phases: theoretical modeling, digital platform development, and a validation cohort study. The intervention included a smartphone-based app providing personalized health education, self-monitoring of vision (Amsler grid), injection reminders, and direct communication with health care providers. The efficacy of the remote management model was tested in a real-world cohort study. Participants were assessed at baseline and at each follow-up visit over 12 months. The primary outcome measure was best-corrected visual acuity, and secondary outcome measures included central retinal thickness and treatment adherence (injection rate and follow-up frequency). RESULTS: A total of 1006 patients with DME underwent initial screening, and 958 (95.2%) patients met the inclusion criteria, with only 7 patients declining follow-up. We anticipate that the remote management system will be both feasible and acceptable for patients with DME. It is hypothesized that the intervention group would demonstrate significantly higher levels of treatment adherence and better visual preservation compared to the retrospective control group. In addition, we intend to assess the impact of the digital intervention on patient-reported outcomes, encompassing quality of life and self-efficacy. Participant recruitment began in September 2023 and was completed by the time of submission. The data analysis is yet to begin. The results are expected to be published in 2026. CONCLUSIONS: A Behavior Change Wheel-based remote management approach could be a scalable and sustainable method to enhance adherence in patients requiring long-term intravitreal therapy. If proven effective, this model could be integrated into routine ophthalmic care to mitigate the risk of vision loss due to undertreatment in DME.
7 Apr 2026
Read appraisal →JMIR medical informatics
Deep Learning for Content-Based Medical Image Retrieval in Picture Archiving and Communication Systems for Brain Tumor Detection: Algorithm Development and Validation
BACKGROUND: Advances in medical imaging have led to massive archives, yet navigating these datasets remains challenging due to the limitations of traditional text-based search engines. While content-based medical image retrieval (CBMIR) offers a visual feature-based solution to enhance clinical workflows and research, its operational integration into picture archiving and communication systems (PACS) remains a significant bottleneck. Despite the progress in deep learning for feature extraction, CBMIR tools are rarely integrated and effectively implemented within existing radiology information systems due to complex protocol barriers. OBJECTIVE: To address these challenges, this study develops a CBMIR system meticulously designed to cater to 7 distinct types of brain tumors as seen in brain magnetic resonance images. Our system is tailored to assist radiologists and health care professionals in efficiently retrieving pertinent historical medical images, thereby providing quantitative decision support for radiologists and facilitating evidence-based case comparison, with the potential to improve retrieval efficiency and clinical workflow, rather than directly enhancing diagnostic accuracy. METHODS: The dataset used in this study was collected from a single medical center and is not publicly available. The core innovation is a state-of-the-art deep learning-based feature extraction algorithm specifically engineered for the CBMIR system. We use GoogLeNet as the primary architecture, incorporating generalized mean pooling to capture nuanced local features and an embedding layer for dimension reduction. Crucially, we address the integration gap by harmonizing 2 open-source projects to successfully embed the CBMIR system into a functional PACS environment via standard protocols. RESULTS: The image dataset contains 658 participants with 15,873 images collected from 2000 to 2017. The empirical findings of our research demonstrate the performance and robustness of the proposed CBMIR system. Our system achieves a remarkable mean average precision score of 89.16% and an equally impressive Precision@10 score of 94.08%. These metrics affirm the system's efficacy in retrieving relevant medical images. Furthermore, we successfully integrate the CBMIR system into a PACS by successfully harmonizing 2 open-source projects. CONCLUSIONS: This study presents the design and implementation of a PACS-integrated CBMIR system for brain magnetic resonance imaging, and the experimental results demonstrate that the system can achieve efficient and accurate image retrieval within a clinical workflow.
7 Apr 2026
Read appraisal →JMIR aging
AI-Enhanced Automatic Life Story Structuring for Reminiscence Therapy in Older Adults: Technical Feasibility Study
BACKGROUND: Storytelling interventions have demonstrated substantial potential in improving emotional well-being, cognitive function, and quality of life for older adults. However, its effectiveness is often limited by the challenges of processing disorganized and redundant life stories, which impose substantial cognitive demands on caregivers. Although storytelling interventions are a well-established therapeutic approach, current practices depend heavily on manual narrative organization, restricting both the scalability and consistency of treatment delivery. Prior research has primarily focused on validating the clinical outcomes of storytelling interventions, with insufficient attention given to technological solutions that could enhance narrative processing while preserving therapeutic integrity. Digital approaches to life story structuring remain underexplored, despite their potential to amplify storytelling benefits by reducing cognitive load and improving recall accuracy. OBJECTIVE: This study aims to design an event timeline generation algorithm to optimize the prior work of the Story Mosaic system. The optimized system enables (1) the automatic extraction of event elements from life narratives, (2) the automatic organization of fragmented life stories into structured timelines, and (3) the preservation of clinically relevant contextual details during compression. The goal is to reduce manual intervention costs while increasing treatment efficacy through artificial intelligence-driven narrative structuring. METHODS: We have designed a novel method, CARE event timeline (CARE-ET), which combines a temporal attention mechanism with graph-based event relationship modeling. Furthermore, we used the CARE-ET algorithm to optimize existing story collage systems. The system uses multifeature extraction technology to capture event clues from oral histories, prioritizes the 6 elements of events through a hierarchical attention mechanism, and uses adaptive compression algorithms to reduce redundancy while maintaining narrative continuity. To verify the effectiveness of the CARE-ET method, this paper adopts a multidimensional evaluation framework, which encompasses event summary assessment, timeline quality evaluation, and usability testing of the optimized system. RESULTS: The proposed CARE-ET algorithm outperforms the baseline in both narrative flow and temporal accuracy. The Story Mosaic system, optimized by the CARE-ET algorithm, underwent usability evaluation by 10 caregivers recruited for this study. Based on standardized assessment metrics, the system received an A rating for usability. The comprehensive experimental results demonstrate that the CARE-ET method can effectively structure fragmented narratives from older adults, enhancing the usability of the Story Mosaic system. CONCLUSIONS: The proposed method enables the structured extraction of representative event summaries, transforming disorganized life stories into an event timeline for caregiver-supported older adult well-being interventions. Future research should investigate longitudinal effects on cognitive preservation and explore integration with existing dementia care protocols. This work establishes a critical foundation for intelligent assistive technologies in geriatric mental health interventions.
7 Apr 2026
Read appraisal →International ophthalmology
Retinal image-based cardiovascular risk prediction using AI-CRS: a multi-modal deep learning framework
This study presents AI-CRS, an AI-driven deep learning framework for cardiovascular risk assessment using retinal images. By combining convolutional neural networks (CNNs), attention mechanisms, and vasculature segmentation, AI-CRS overcomes the limitations of traditional methods, such as reliance on handcrafted features and single-modality data. The model integrates multi-modal information, fusing vasculature segmentation maps with raw image data to capture subtle vascular changes indicative of cardiovascular diseases. Experimental validation demonstrates that AI-CRS outperforms conventional diagnostic techniques in cardiovascular risk stratification, achieving superior sensitivity, specificity, and accuracy. The model excels in detecting early-stage disease and subtle vascular anomalies, and it shows strong generalizability across diverse datasets, patient demographics, and varying image qualities. Beyond cardiovascular risk, AI-CRS also shows promise in assessing systemic health conditions like hypertension and diabetes, demonstrating the broader utility of retinal imaging in health monitoring. The attention-driven architecture enhances model interpretability, providing clinicians with visual explanations of disease-associated vascular patterns, which is essential for clinical adoption. AI-CRS offers a non-invasive, scalable solution that can be integrated into routine clinical practice, supporting early diagnosis, personalized care, and population-wide screening. Its automated analysis reduces the need for manual feature extraction and subjective interpretation, streamlining workflows and improving efficiency. Ultimately, AI-CRS represents a significant step toward precision medicine by enabling timely interventions, reducing healthcare costs, and improving patient outcomes through non-invasive digital biomarkers.
7 Apr 2026
Read appraisal →JMIR dermatology
Community-Based Teledermatology for Urgent Suspected Skin Cancer: Health Economic Cost-Comparison and Discrete Event Simulation Study
BACKGROUND: The increasing incidence and financial burden of skin cancer place immense pressure on the UK's National Health Service (NHS). Systemic challenges, including dermatologist shortages and long waiting lists, complicate timely assessment of skin lesions for patients under the urgent suspected cancer pathway. While teledermatology offers an innovative solution compared to traditional face-to-face appointments, standard teledermatology models still face limitations in addressing health care access barriers. Community-based decentralized models may reduce such barriers, but the cost and operational impact of such specific models remain largely underresearched. OBJECTIVE: This study evaluated the differences in financial cost to the NHS and patient waiting times at the Northern Care Alliance NHS Foundation Trust by comparing a community-based teledermatology model using Pathpoint eDerma against the Trust's standard-of-care for patients in the urgent suspected skin cancer pathway. METHODS: This study used an ambidirectional design involving 2 distinct analyses. The cost comparison analysis (CCA) compared costs incurred under the teledermatology model (intervention arm, n=563) against the Trust's standard care, represented by a synthetic comparator arm (n=4011). The discrete event simulation (DES) modeled the operational impact on patient waiting times over a 1-year period. Data for the intervention arm were collected prospectively from December 2022 to May 2023 for CCA and up to November 2023 for DES, while comparator data were collected retrospectively from September 2021 to December 2022. Publicly available resource costs were incorporated to ensure the robustness of the analyses. RESULTS: The community-based teledermatology model was associated with significant improvements in both cost to the NHS and patient waiting times. The CCA revealed a mean cost saving of £45 (£1=US $1.24) per referral (95% CI £22-£60; P<.001). This cost saving was associated with a 26% reduction in the proportion of patients requiring a full diagnostic biopsy, falling from 48% (1925/4011) in standard care to 22% (124/563) in the teledermatology model as well as time savings in face-to-face clinics and administration. Furthermore, the DES demonstrated that, on average, the teledermatology pathways decreased the time to reach a clinical diagnosis by 9.90 (95% CI 9.64-10.16) days; to communicate a diagnosis to patients by 54.18 (95% CI 50.76-57.61) days; and to reach a histopathological diagnosis by 62.8 (95% CI 59.76-65.83) days compared to standard care. CONCLUSIONS: The implementation of the community-based teledermatology model appears to be a highly effective, cost-efficient strategy associated with shortened patient journeys. The intervention showed a faster initial triage phase, but the study identified the histopathology process as the next major systemic constraint that could deter further pathway efficiency. Achieving timely diagnosis for all patients, including those requiring diagnostic biopsies, will necessitate continued strategic investment in innovative technologies to accelerate this downstream process.
7 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
User experiences of digital health interventions among older adults with cancer: a systematic review of qualitative data
PURPOSE: The aim of this review was to synthesize and analyze the results of several qualitative studies to understand the perceptions of older adults with cancer using digital health interventions to manage cancer in a digital context. METHODS: Six databases were systematically queried to identify qualitative studies of digital health interventions in older cancer populations, published from the inception of the databases until December 10, 2024. Studies that met the inclusion and exclusion criteria were selected, and the quality of the literature was assessed according to established guidelines. The results were synthesized using a thematic synthesis approach. RESULTS: A total of 12 papers were included. Three analytic themes emerged, (1) positive experiences, (2) barriers to use, (3) user expectations. The first theme was with three descriptive themes, including "improved self-management," "safe and accessible support," and "better access to health information"; the second theme with three descriptive themes, including "inadequate content design," "technical difficulties," and negative user perceptions and behaviors"; the third theme with two descriptive themes: "enhanced functionality" and "optimized user experience." CONCLUSIONS: This study found that older adults with cancer recognize the benefits of digital health Interventions in improving their health. However, their experiences are influenced by various factors. Patients expressed diverse needs regarding the functionality, information support, and social support aspects of digital health interventions.
6 Apr 2026
Read appraisal →PloS one
Vision Transformer attention alignment with human visual perception in aesthetic object evaluation
Visual attention mechanisms play a crucial role in human perception and aesthetic evaluation. Recent advances in Vision Transformers (ViTs) have demonstrated remarkable capabilities in computer vision tasks, yet their alignment with human visual attention patterns remains underexplored, particularly in aesthetic contexts. This study investigates the correlation between human visual attention and ViT attention mechanisms when evaluating handcrafted objects. We conducted an eye-tracking experiment with 30 participants (9 female, 21 male, mean age 24.6 years) who viewed 20 artisanal objects comprising basketry bags and ginger jars. Using a Pupil Labs eye-tracker, we recorded gaze patterns and generated heatmaps representing human visual attention. Simultaneously, we analyzed the same objects using a pre-trained ViT model with DINO (Self-DIstillation with NO Labels), extracting attention maps from each of the 12 attention heads. We compared human and ViT attention distributions using four complementary metrics-Kullback-Leibler divergence, Structural Similarity Index (SSIM), Pearson's Correlation Coefficient (CC), and Similarity (SIM)-across varying Gaussian parameters ([Formula: see text]), yielding 1,152,000 distance evaluations. Additionally, we performed Areas of Interest (AOI) analysis to quantify ViT attention concentration within object regions. Statistical analysis revealed optimal correlation at [Formula: see text], with attention head #12 showing the strongest alignment with human visual patterns across all metrics. Significant differences were found between attention heads, with heads #7 and #9 demonstrating the greatest divergence from human attention ([Formula: see text]), Tukey HSD test). AOI analysis confirmed that all ViT heads concentrated attention significantly more within object regions than background areas ([Formula: see text]), with heads #12, #1, and #3 achieving lift values of +30 to +40 percentage points. Results indicate that while ViTs exhibit more global attention patterns compared to human focal attention, certain attention heads can approximate human visual behavior, particularly for specific object features like buckles in basketry items. These findings suggest potential applications of ViT attention mechanisms in product design and aesthetic evaluation, while highlighting fundamental differences in attention strategies between human perception and current AI models.
6 Apr 2026
Read appraisal →Genetics
Mondo: integrating disease terminology across communities
Precision medicine aims to enhance diagnosis, treatment, and prognosis by integrating multimodal data at the point of care. However, challenges arise due to the vast number of diseases, differing methods of classification, and conflicting terminological coding systems and practices used to represent molecular definitions of disease. This lack of interoperability artificially constrains the potential for diagnosis, clinical decision support, care outcome analysis, as well as data linkage across research domains to support the development or repurposing of therapeutics. There is a clear and pressing need for a unified system for managing disease entities-including identifiers, synonyms, and definitions. To address these issues, we created the Mondo disease ontology-a community-driven, open-source, unified disease classification system that harmonizes diverse terminologies into a consistent, computable framework. Mondo integrates key medical and biomedical terminologies, including Online Mendelian Inheritance in Man (OMIM), Orphanet, Medical Subject Headings (MeSH), National Cancer Institute Thesaurus (NCIt), and more, to provide a comprehensive and accurate representation of disease concepts with fully provenanced and attributed links back to the sources. Mondo can be used as the handle for curation of gene-disease associations utilized in diagnostic applications, research applications such as computational phenotyping, and in clinical coding systems in clinical decision support by pointing the clinician to the numerous knowledge resources linked to the Mondo identifier. Mondo's community-centric approach, stewarded by the Monarch Initiative's expertise in ontologies, ensures that the ontology remains adaptable to the evolving needs of biomedical research and clinical communities, as well as the knowledge providers.
5 Apr 2026
Read appraisal →European journal of cardiovascular nursing
A review and promise of digital health technologies to promote global cardiovascular health
Digital health technologies include an array of mobile or digital devices and apps that may be used by patients to support lifestyle modification or medical apps that monitor vital parameters and provide data that may influence treatment decisions. The use of digital health technologies is a growing opportunity for disease prevention and management of cardiovascular disease (CVD) for both patients to engage in self-management as well as nurses to monitor patients remotely and facilitate clinical decision support. The aims of this review article are to provide an overview of pertinent digital health technologies in CVD care, address social determinants of health and other enablers/barriers to digital health implementation, and provide perspectives on novel models of care incorporating digital technologies. Based on the latest evidence, recommendations for special considerations and implementation of digital health technologies in low- and middle- vs high-income countries are presented. In addition, tables reviewing functionality of the digital tools and global examples of their use are provided. Extensive empirical evidence from diverse populations suggests adoption of these innovative tools has been low in clinical settings. We provide a table summarizing these barriers and include the facilitators that can improve the adoption of digital health tools to improve behavioral lifestyle modification. For learning purpose, implementation of an innovative model of care is illustrated graphically and in text. We include a call to action to achieve the maximum impact of digital technology to support nurses who work in almost all settings improving CVD prevention and management globally. The multiple roles of nurses using digital health for global CVD prevention and management are discussed in the settings of clinical practice, research, education, public health, health policy/leadership, and informatics.
5 Apr 2026
Read appraisal →PloS one
A robust deep learning approach for impulse noise filtering using hybrid auto-encoder with fuzzy median filter
De-noising convolutional neural networks (DnCNNs), are a powerful nonlinear mapping models in image processing for impulse noise removal. During training and validation, a set of 12 standard testing images is used to evaluate model performance. DnCNNs demonstrate strong capability in classification of impulse noise with excellent results. To evaluate de-noising performance, a suitable noise ratio should be added so that most appropriate DnCNN model can be used for impulse noise detection. This research proposes an effective image restoration technique that integrates DnCNN and an autoencoder with a fuzzy median filter to detect and eliminate high-density impulse noise. The proposed deep learning de-noising technique used to classify noisy and clean pixels, and result are presented in different metrics such as accuracy, FPR, FNR and f1 score. Further, to remove impulse noise an auto-encoder with fuzzy median filter are used that then reconstructs the clean image based with parametric values. Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), are used to assess our methodology, it is compared to conventional impulse noise filtering techniques, experimental results indicate a significant improvement in image quality. Based on the final de-noised images, this research contributes to developing deep learning-based, de-noising techniques that enhance image restoration quality while preserving image details and essential features.
5 Apr 2026
Read appraisal →Cancer causes & control : CCC
A preference study for applying mHealth to improve the quality of cervical cancer screening management from a supply and demand perspective: a discrete choice experiment.
OBJECTIVES: This study aims to investigate the preferences of women of cervical cancer screening age regarding the use of mobile health (mHealth) technologies in Xinjiang. The purpose is to enhance adherence and acceptance of mHealth applications, thereby improving the quality of cervical cancer screening follow-up for women residing in rural areas of Xinjiang. METHODS: The attributes and levels were established based on a comprehensive literature review and qualitative interviews. The choice set questionnaire was developed using SAS 9.4 through D-efficiency analysis. The research site was identified using a multi-stage sampling method, and the survey was launched in December 2024. An Optimal Logit model was selected for preference analysis, utilizing Stata 17.0. RESULTS: A total of six attributes were identified through the literature review and subsequently combined with qualitative interviews. The mixed logit regression results indicated that, among the six attributes, five were statistically significant (P < 0.05): mHealth approach, frequency of receiving notifications, reminder content, screening notification time, missionary form, and mission strategy. The results demonstrated a preference for WeChat as the mHealth approach (β = 0.230, P < 0.001, 95%CI 1.214-1.500), not accepting receiving notifications everyday (β = - 0.144, P = 0.001, 95%CI 0.797-0.940), preferred reminders containing positive information (β = 0.159,P = 0.001,95%CI1.068-1.288), opted for screening notifications 1 month in advance (β = 0.208, P < 0.001, 95%CI 1.133-1.339), and preferred the image form of education for missionary form (β = 0.118, P < 0.001, 95%CI 1.102-1.322). CONCLUSION: A one-size-fits-all approach should be avoided in the design of mHealth solutions to effectively address the diverse needs of individuals with varying preferences, thereby promoting the adoption of mHealth and improving the feasibility of its application.
5 Apr 2026
Read appraisal →Biomedical physics & engineering express
Detecting early signs of patient deterioration at home using wearable sensors: a personalized anomaly detection approach
In the rapidly emerging field of transferring healthcare from hospitals to patients' homes, it is essential that signs of remote deterioration at home should be detected early to timely upscale care when needed. As patient deterioration is a rare event, datasets have insufficient event data for supervised machine learning. Personalized anomaly detection (AD) may be a good alternative. Therefore, this study aimed to obtain insight into the use of personalized AD models in detecting early signs of patient deterioration at home using wearable sensor data. To address this, Isolation Forest and Local Outlier Factor models were applied to detect signs of deterioration in terms of mortality or unplanned readmission in the 24 preceding hours in two datasets: one of heterogeneous patients, the other of postoperative patients. A pipeline was developed for continuously updating personalized AD models over time and applying them to detection windows twice per day. Results were compared with the Remote Early Warning Score. Isolation Forest (AUROC: 0.69) and Local Outlier Factor (AUROC: 0.67) models were able to find some early signs of deterioration in heterogeneous patients (n = 113). However, in postoperative patients (n = 193), Isolation Forest (AUROC: 0.41) and Local Outlier Factor (AUROC 0.44) performed badly. The Remote Early Warning Score was able to find some early signs of deterioration for both groups (AUROC: 0.63-0.76). Based on these findings, three requirements were formulated that should be fulfilled for a potentially successful application of personalized AD. First, the training set should be normal and representative of non-deteriorating patients. Second, signs of deterioration should exhibit abnormal characteristics. Third, non-deteriorating patients should not exhibit abnormal characteristics.
5 Apr 2026
Read appraisal →PloS one
Patterns and predictors of variability in patient-generated daily pain severity collected via a mobile health smartphone app.
Digital-health technologies support the collection of patient-generated health data that is frequent, longitudinal, and collected in participant's own environments. Such high-frequency data could detect patterns of variation in disease and associated symptoms, but characterizing, interpreting, and understanding the reasons for this variability remain open questions. Here, we examine 2070 people living with chronic pain to quantify daily variability in pain severity across seven-day periods and identify factors associated with that variability. Data were collected via a smartphone application from a population-based mobile-health study, Cloudy with a Chance of Pain. Summary statistics and distributions of pain changes on consecutive days were calculated within 13,052 complete weeks of data, which had been assigned to one of four clusters via a previously published k-mediods clustering algorithm: no/low pain, mild pain, moderate pain, and severe pain. Cumulative-probit models were used to identify associations between changes in pain severity and changes in exposure data. Across the four clusters, the no/low-pain cluster had the highest proportion of weeks with no within-week changes (59%) in pain severity compared to the other clusters (48-53%). When pain did change, it changed one unit (out of five) about 20% of the time, but larger changes of two to four units also occurred. Changes in pain severity were associated most strongly with changes in pain interference (i.e., how pain impacts daily activities) and were also associated with changes in fatigue, morning stiffness, mood, and participant well-being. Thus, this study showed that data collected frequently through digital-health technologies can be used to explore variability in symptoms and their associations with other variables. That pain severity was associated with changes in modifiable variables (e.g., fatigue, mood) suggests opportunities for different treatment and self-management regimes for different patient subtypes within the four clusters.
5 Apr 2026
Read appraisal →Annals of medicine
An explainable online frailty prediction model for community-dwelling older adults based on machine learning algorithms: a cross-sectional study based on retrospective health data
BACKGROUND: Frailty is a significant health concern associated with diminished physiological reserves and increased healthcare burdens. Currently, effective models for predicting frailty risk are lacking. This study utilizes machine learning-based models to early identify community-dwelling older adults, enhancing risk assessment accuracy and guiding targeted interventions to slow frailty progression. METHODS: A cross-sectional analysis of data from 1,156 older adults across 31 community health centers in Nanjing, conducted between January and October 2024, was performed. Independent predictors of frailty were identified using univariate analysis and the least absolute shrinkage and selection operator. The dataset was divided into 70% training and 30% testing subsets. Six machine learning (ML) models were developed and their performances compared. The SHapley Additive exPlanations (SHAP) method was applied to interpret the models, and a web-based risk calculator was created. RESULTS: Our dataset showed that 22.3% of older adults were frail. Significant predictors of frailty were identified as age, education, medicine, vegetable, cognitive status, number of diseases, hemoglobin, total cholesterol, and neutrophil-to-lymphocyte ratio. Among the six ML models, Categorical Boosting (CatBoost) exhibited the highest performance, attaining an AUROC of 0.886 in the training set and 0.831 in the testing set. CONCLUSIONS: The developed CatBoost model and web calculator can be employed by general practitioners to proactively identify high-risk community-dwelling older adults, thereby enabling timely interventions to mitigate the progression of frailty. The tool's simplicity and replicability effectively facilitate the promotion and management of frailty prevention within the community. An effective model was developed to predict frailty risk in community-dwelling older adults.The integration of biomarkers and self-reported data enhances frailty prediction and optimizes risk-based intervention strategies.The CatBoost model demonstrates superior predictive performance compared to other models.The web-based tool integrates a frailty prediction model for proactive risk management.
5 Apr 2026
Read appraisal →The International journal of oral & maxillofacial implants
Accuracy and Reliability of Artificial Intelligence Chatbots as Public Information Sources in Implant Dentistry
PURPOSE: To evaluate the accuracy, completeness, comprehensibility, and reliability of widely available artificial intelligence (AI) chatbots when addressing clinically significant queries pertaining to implant dentistry. MATERIALS AND METHODS: A total of 20 questions were devised based on a compiled list that were most frequently asked or encountered during patient consultations by three experienced prosthodontists. These questions were asked to ChatGPT-3.5, Gemini, and Copilot AI chatbots on three separate occasions in 12-day intervals. A three-point Likert scale (0 = incorrect; 1 = incomplete or partially correct; 2 = correct) and a two-point scale (true and false) were employed by the authors to grade the accuracy of the responses independently. Completeness and comprehensibility were also evaluated using a three-point Likert scale (scored 1, 2, or 3, with lower scores indicating a worse result). In addition to the questions generated by the specialists, AI chatbots were asked to provide five frequently asked questions and responses regarding dental implant prostheses. A comparison of total scores obtained from the chatbots was made with one-way analysis of variance (ANOVA). Twopoint scale data were analyzed via chi-square test. The reliability of the responses for each chatbot was analyzed by assessing the consistency of repeated responses via calculating Cronbach's alpha (α) coefficients. RESULTS: When the total accuracy scores of the chatbots were analyzed out of a 40-point total score (ChatGPT-3.5 = 28.78 ± 4.06; Gemini = 30.89 ± 4.08; Copilot = 29.11 ± 3.22), one-way ANOVA revealed no statistically significant differences (P = .461). For the evaluation of two-point accuracy scale data, which were analyzed via chi-square test, no statistical differences were revealed among the chatbots (P = .336). Gemini showed a higher completeness level than ChatGPT-3.5 (P = .011). There was no statistically significant difference among AI chatbots in terms of comprehensibility. Copilot demonstrated the greatest overall consistency among the three chatbots, with a Cronbach's α value of .863. This was followed by ChatGPT-3.5 with a Cronbach's α value of .779 and Gemini with a Cronbach's α value of .636. CONCLUSIONS: The accuracy of all three chatbots were found to be similar. All three chatbots demonstrated an acceptable level of consistency. However, given the low accuracy rate of chatbots in answering questions, it is clear that they should not be relied upon as a decision-maker, and the clinician's opinion must be given priority.
4 Apr 2026
Read appraisal →Sexual medicine reviews
Internet-based cognitive behavioral therapy, web-based counseling, and online/digital psychoeducation for male sexual dysfunction: a systematic review
INTRODUCTION: Male sexual dysfunctionhas a substantial impact on psychological well-being and intimate partner relationships. The expanding availability of digital health services has enabled online interventions, such as internet-based cognitive behavioral therapy (ICBT), digital psychoeducation, and web-based counseling, to emerge as alternatives to conventional therapies. OBJECTIVE: To evaluate the effectiveness, feasibility, and acceptability of digital and online interventions for male sexual dysfunction, and to synthesize evidence from randomized, non-randomized, and observational studies on clinical outcomes and patterns of digital engagement. METHODS: This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A comprehensive literature search was conducted across several databases using predefined keywords. Eligible studies included randomized controlled trials, experimental studies, and analytical observational studies published between 2007 and 2024. Extracted data comprised country, participant characteristics, type and duration of intervention, control conditions, outcome measures, and main findings. The primary outcome was improvement in erectile/sexual function using certain parameters, while secondary outcomes included relationship satisfaction, anxiety, and depression. RESULTS: Of 2482 records identified, and 21 met the inclusion criteria. Overall, web-based ICBT and psychoeducational interventions improved erectile function and sexual satisfaction compared with control conditions. Most interventions lasted 6-12 weeks and incorporated therapist feedback or automated systems. Remission rates for erectile dysfunction ranged from 20% to 30%; however, participant adherence was low (<60%). Several studies also reported reductions in performance anxiety and improvements in sexual confidence, with no serious adverse effects. CONCLUSION: Online interventions are promising approaches for managing male sexual dysfunction. Nevertheless, low adherence rates and heterogeneity in intervention formats remain major challenges. The findings also suggest heterogeneous benefits of digital interventions, alongside improved accessibility, reduced stigma, and increased engagement in help-seeking among men with sexual dysfunction. Future research should prioritize strategies to enhance adherence, integrate mobile health technologies, and assess long-term outcomes through large-scale clinical trials.
4 Apr 2026
Read appraisal →JMIR aging
Digital Resources and Social Connectedness Among Ethnic Minority Older Adults: Systematic Review and Meta-Analysis
BACKGROUND: Being socially connected is essential for health and well-being. Nonetheless, many older adults face social isolation, especially in ethnically diverse societies. Digital technologies offer a pragmatic approach to addressing problems with social connectedness; however, a consolidated understanding of their association with social connectedness among ethnic minority older adults remains unaddressed. OBJECTIVE: We conducted a systematic review and meta-analysis to investigate the prevalence and utility of digital resources in promoting social connectedness among ethnic minority older adults. METHODS: This systematic review and meta-analysis was guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were retrieved from 4 electronic databases. A random-effects meta-analysis was used to estimate the pooled prevalence along with its 95% CI. RESULTS: Twelve studies were included. The research approaches used by the included studies were as follows: quantitative (n=5), qualitative (n=5), and mixed methods (n=2). Overall, the estimated prevalence of internet use, which was defined as engaging in online activities through digital devices, was 81.8%. Commonly used digital devices were computers (66.7%), smartphones (63.2%), and tablets (29.3%). Sociodemographic, economic, and health-related factors influenced the adoption and use of digital resources among ethnic minority older adults. These older adults relied on digital resources to build meaningful social connections through social participation, maintaining cultural and religious ties, promoting transnational social networks, and fostering their potential for social connectedness. However, digital resources were not always useful for social connectedness and adversely affected in-person contact. CONCLUSIONS: Digital resources can promote social connectedness among ethnic minority older adults by enabling them to maintain their cultural and social values. Cities and regions aiming to promote the social connectedness and well-being of ethnic minority older adults should consider intersectional factors that affect access to digital resources and the sustainable adoption of digital resources.
4 Apr 2026
Read appraisal →JMIR mental health
AI Chatbots for Mental Health Self-Management: Lived Experience-Centered Qualitative Study
BACKGROUND: Large language models (LLMs) now enable chatbots to engage in sensitive mental health conversations, including depression self-management. Yet their rapid deployment often overlooks how well these tools align with the priorities of people with lived experiences, which can introduce harms such as inaccurate information, lack of empathy, or inadequate crisis support. OBJECTIVE: This study explores how people with lived experience of depression experience an LLM-based mental health chatbot in self-management contexts, and what perceived benefits, limitations, and concerns inform harm-mitigating design implications. METHODS: We developed a technology probe (a GPT-4o-based chatbot named Zenny) designed to simulate depression self-management scenarios grounded in prior research. We conducted interviews with 17 individuals with lived experiences of depression, who interacted with Zenny during the session. We applied qualitative content analysis to interview transcripts, notes, and chat logs using sensitizing concepts related to values and harms. RESULTS: We identified 3 themes shaping participants' evaluations: (1) informational accuracy and applicability, including concerns about incorrect or misleading information, vagueness, and fit with personal constraints; (2) emotional support vs need for human connection, including validation and a judgment-free space alongside perceived limits of machine empathy; and (3) a personalization-privacy dilemma, where participants wanted more tailored guidance while withholding sensitive information and using privacy-preserving tactics. CONCLUSIONS: People with lived experience of depression evaluated LLM-based mental health chatbots through intertwined priorities of actionable information, emotional validation with clear limits, and personalization that does not require unsafe data disclosure. These findings suggest concrete design strategies to mitigate harms and support LLM-based tools as complements to, rather than replacements for, human support and recovery.
4 Apr 2026
Read appraisal →Journal of medical Internet research
Effectiveness of the Components of a Digital Multiple Health Behavior Change Intervention Among Individuals Seeking Help Online (Coach): Factorial Randomized Trial
BACKGROUND: Extant digital multiple health behavior change interventions have shown promise in various populations; however, evidence for a broader approach among the general population is lacking. Moreover, existing interventions often contain several components but are typically assessed as a whole, meaning it remains unclear to what extent individual components contribute to intervention effects and how they may interact to influence health outcomes. OBJECTIVE: This study estimates the effects of 6 components of a digital health behavior change intervention on alcohol, diet, physical activity, and smoking outcomes among individuals searching for help online. METHODS: A double-blind randomized factorial trial design with 6 two-level factors was used. Adults from the general public in Sweden who were seeking help to change their behaviors were recruited through web searches and social media. Participants were eligible if they were 18 years or older and had at least one health behavior classified as unhealthy. Effects of 6 components were estimated: screening/feedback, goal-setting/planning, motivation, skills/know-how, mindfulness, and self-authored SMS text messages. Primary outcomes were weekly alcohol consumption and frequency of heavy episodic drinking, average daily fruit and vegetable consumption, weekly moderate-to-vigorous physical activity, and 4-week point-prevalence smoking. RESULTS: A total of 5419 individuals were randomized. Overall, the screening/feedback component was the most effective for changing health behaviors, along with goal-setting/planning and motivation to change. In particular, there was evidence that screening/feedback increased average daily portions of fruit and vegetables at 2 months (mean difference 0.17, compatibility interval [CoI] 0.09-0.25, probability of effect [POE] >99.9%) and at 4 months (mean difference 0.13, CoI 0.04-0.21, POE 99.9%) and reduced the frequency of heavy episodic drinking at 4 months (incidence rate ratio 0.91, CoI 0.81-1.03, POE 94.2%). Components also interacted to further improve health outcomes, most notably the combination of screening/feedback with motivation to change, which further increased fruit and vegetable consumption (2 months: mean difference 0.20, CoI 0.09-0.30, POE >99.9%; 4 months: mean difference 0.17, CoI 0.05-0.29, POE 99.8%). CONCLUSIONS: The results from this study contribute to the development of more effective interventions by providing novel insights into the effects of individual and pairwise components of complex digital health behavior change interventions. TRIAL REGISTRATION: ISRCTN Registry ISRCTN16420548; http://www.isrctn.com/ISRCTN16420548. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2022-061024.
4 Apr 2026
Read appraisal →Journal of medical Internet research
The Role of Digital Biomarkers in Physiological Signal-Based Depression Assessment: Systematic Review and Meta-Analysis
BACKGROUND: Digital biomarkers are increasingly being used to support depression assessment by providing objective, continuous, and real-time physiological and behavioral data. However, most existing studies have focused on individual biomarkers, such as sleep or cardiac parameters, while integrative evaluations that capture the multidimensional nature of depression remain limited. OBJECTIVE: This systematic review evaluated digital biomarkers for depression and synthesized evidence on differences between individuals with depression and controls. METHODS: Eligible studies included observational or interventional studies examining digital biomarkers for depression with validated outcome measures. We searched major international and Korean databases, including MEDLINE, PsycINFO, CINAHL, IEEE Xplore, Web of Science, Cochrane Library, KISS, RISS, KMbase, and KoreaMed, from inception to December 28, 2025. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool and the Scottish Intercollegiate Guidelines Network checklist. Meta-analyses were conducted using random-effects models with the Hartung-Knapp-Sidik-Jonkman method, and other outcomes were narratively summarized. RESULTS: The search yielded 39,617 records, of which 132 studies involving 57,852 participants met the inclusion criteria. These studies encompassed various digital biomarkers, including sleep, physical activity, cardiac measures, smartphone-derived data, speech, GPS data, and circadian rhythms. A meta-analysis of 22 studies (6947 participants) revealed that individuals with depression had significantly longer sleep onset latency (5 studies; n=292; +4.75 min, 95% CI 2.46-7.04; P=.005; 95% prediction interval [PI] 0.01-10.27) and time in bed (3 studies; n=236; +31.81 min, 95% CI 18.22-45.39; P=.01; 95% PI 2.28-55.16). Physical activity counts were also significantly lower (5 studies; n=462; standardized mean difference -0.71, 95% CI -1.33 to -0.09; P=.03; 95% PI -2.18 to 0.71). Although individuals with depression showed a lower sleep efficiency, higher mean heart rate, and lower SD of normal-to-normal intervals, these differences were not statistically significant. Other digital markers yielded inconsistent results. Overall, these findings indicate that no single digital biomarker sufficiently captures depression-related changes. Instead, the results support the superiority of personalized, multimodal approaches. However, the generalizability of these findings is limited by the lack of standardized data collection protocols and high clinical heterogeneity across studies, as reflected in wide PIs. CONCLUSIONS: Certain digital biomarkers, particularly sleep onset latency and physical activity counts, showed consistent average differences between the depression and control groups. However, wide PIs indicate substantial variability across settings, suggesting that no single marker is sufficient for reliable detection. This study advances the field by providing a comprehensive meta-analysis of multidimensional digital biomarkers, establishing a quantitative foundation for objective depression screening and monitoring. These findings support the use of personalized, multimodal digital phenotyping approaches and highlight the need for standardized, clinically interpretable frameworks for real-world depression monitoring.
4 Apr 2026
Read appraisal →Clinical chemistry
Discovery of an Artificial Intelligence Label Feedback Loop: How the Success of a Clinically Implemented Artificial Intelligence Algorithm Has Created an Unforeseen Challenge to Algorithm Retraining
BACKGROUND: Artificial intelligence (AI) augmented laboratory tests can improve quality, efficiency, cost-effectiveness, and staff satisfaction. However, the clinical success of these tests can introduce unforeseen challenges for model retraining. This study describes the discovery of an "AI label feedback loop" in a clinically implemented AI-augmented kidney stone composition test. METHODS: An AI-augmented kidney stone composition test (V1) has been previously deployed for clinical kidney stone characterization. After several years of clinical use, a retrained model (V2) was developed using 6 times more data. Model performance of both V1 and V2 were evaluated across 3 datasets: a recent production validation (hold-out) set (mostly AI-influenced labels), the original V1 validation set (pre-AI, entirely human-labeled), and a subset of recent cases with exclusively human-generated or human-corrected labels. RESULTS: V2 demonstrated a 10% lower concordance rate than V1 when evaluated on the recent production hold-out set, despite a much larger training dataset. Performance between V1 and V2 was similar when applied to the pre-AI validation set. Notably, V2 outperformed V1 on the recent subset of cases with human-only or human-corrected labels, particularly for less-common stone types. These findings revealed an AI label feedback loop, confounding retraining and evaluation. CONCLUSIONS: The integration of AI into clinical practice can potentially influence reported test results, complicating the development and evaluation of future models. To mitigate AI label feedback loops, ongoing human annotation and careful validation set construction are essential. These strategies can ensure reliable performance assessment and support the safe evolution of clinical AI systems.
4 Apr 2026
Read appraisal →European radiology
Large language model-based uncertainty-adjusted label extraction for artificial intelligence model development in upper extremity radiography
OBJECTIVES: To evaluate GPT-4o's zero-shot ability to extract structured diagnostic labels (with uncertainty) from free-text radiology reports and to test how these labels affect multi-label image classification of musculoskeletal radiographs. MATERIALS AND METHODS: This retrospective study included radiography series of the clavicle (n = 1170), elbow (n = 3755), and thumb (n = 1978). After anonymization, GPT-4o filled out structured templates by indicating imaging findings as present ("true"), absent ("false"), or "uncertain." To assess the impact of label uncertainty, "uncertain" labels of the training and validation sets were automatically reassigned to "true" (inclusive) or "false" (exclusive). Label-image pairs were used for multi-label classification using the ResNet50 architecture. Label extraction accuracy was manually verified on internal (clavicle: n = 233, elbow: n = 745, thumb: n = 393) and external test sets (n = 300 for each). Performance was assessed using macro-averaged receiver operating characteristic (ROC) area under the curve (AUC), precision, recall curves, sensitivity, specificity, and accuracy. AUCs were compared with the DeLong test. RESULTS: Automatic extraction was correct in 98.6% (60,618 of 61,488) of labels in the test sets. Across anatomic regions, label-based model training yielded competitive performance measured by macro-averaged AUC values for inclusive (e.g., elbow: AUC = 0.80 (range, 0.62-0.87)) and exclusive models (elbow: AUC = 0.80 (range, 0.61-0.88)). Models generalized well on external datasets (elbow (inclusive): AUC = 0.79 (range, 0.61-0.87); elbow (exclusive): AUC = 0.79 (range, 0.63-0.89)). No significant differences were observed across labeling strategies or datasets (p ≥ 0.15). CONCLUSION: GPT-4o extracted labels from radiologic reports to train competitive multi-label classification models with high accuracy. Detected uncertainty in the radiologic reports did not influence the performance of these models. KEY POINTS: Question Can GPT-4o automatically extract high-accuracy, uncertainty-aware diagnostic labels from routine radiologic reports of the clavicle, elbow, and thumb for use in training multi-label image classifiers? Findings GPT-4o extracted labels with > 98% accuracy, and multi-label classifiers for clavicle, elbow, and thumb radiographs performed consistently regardless of how uncertainty was handled. Clinical relevance Automated GPT-4o-based labeling of routine clavicle, elbow, and thumb radiologic reports enables the rapid conversion of radiologic reports into structured multi-label training datasets, supporting scalable development of dedicated image classification models.
4 Apr 2026
Read appraisal →Journal of diabetes investigation
A machine learning approach to predicting severe diabetes distress in adults with type 1 diabetes mellitus.
BACKGROUND: Diabetes distress is common in patients with type 1 diabetes mellitus (T1DM). The aim of this study was to construct and validate prediction models for diabetes distress in adults with T1DM using continuous glucose monitoring (CGM) metrics. METHODS: The CGM metrics were collected from 259 adults with T1DM. Severe diabetes distress was defined as 40 points on the Problem Areas in Diabetes scale. Prediction models were developed based on ten machine learning algorithms: random forest (RF), support vector machine (SVM), Naive Bayes (NB), Neural Network (NN), k-nearest neighbor (k-NN), XGBoost (XGB), SGDClassifier (SGDC), XGB_limitet_depth (CGB_ld), L1LogisticRegression (L1), and LightGBM. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score. RESULTS: Among the ten models, accuracy in the NN model was the highest (NN: 0.744, L1: 0.731, NB: 0.718, SVM: 0.692, SGDC: 0.654, RF: 0.628, k-NN: 0.628, LightGBM: 0.615, XGBoost: 0.564, and XGB_ld: 0.564). The NN model achieved the highest AUC of 0.728 (95% confidence interval: 0.608-0.845). CONCLUSIONS: This study developed a predictive model for severe diabetes distress using machine learning, incorporating both demographic and CGM metrics in adults with type 1 diabetes mellitus. The NN model demonstrated potential as a practical tool to assist clinicians in identifying individuals at risk of severe diabetes distress.
4 Apr 2026
Read appraisal →The American journal of gastroenterology
A Large Language Model Assistant for Summarizing Hepatology Referral Documents
INTRODUCTION: To use a large language model (LLM) to create accurate and useable summaries of patient records for clinicians triaging new hepatology referrals. METHODS: We developed a comprehensive list of data elements required to triage a new hepatology referral and engaged in an iterative prompt engineering process to instruct an LLM to extract relevant data from patient referral documents. The final prompt was used on 50 original patient records from June to July 2025 to generate corresponding artificial intelligence (AI) summaries, which were assigned to 2 providers to review and triage according to their usual process. We assessed time to triage original vs AI files and accuracy of the AI files. A linear mixed-effects model was used to determine an adjusted time ratio comparing the time to triage AI files vs original files. RESULTS: AI-generated summaries were significantly shorter than original files (median [interquartile range] 2 [2-3] vs 23 [10.2-38.8] pages, P < 0.001). AI summaries had high accuracy (median [interquartile range]: 94.6% [86.5%-97.3%]) with a low hallucination rate. Use of the AI summaries led to a 60% reduction in triage time (adjusted mean triage time of 37.2 seconds for AI files vs 94.2 seconds for original files, P < 0.001). DISCUSSION: The use of an LLM led to significantly reduced document length, maintained an appropriate level of accuracy, and led to a significant decrease in clinician time to review the patient record. Future steps involve creating a fully automated workflow that is integrated into the electronic health record for widespread use.
4 Apr 2026
Read appraisal →Current opinion in anaesthesiology
Data integration and systems interoperability: the prerequisite for artificial intelligence in anesthesiology
PURPOSE OF REVIEW: Anesthesiology generates large volumes of heterogeneous perioperative data, including high-resolution physiological signals, clinical documentation, and device-generated information. Despite this richness, the clinical deployment of artificial intelligence systems remains limited. This review examines how limitations in data integration and systems interoperability constrain the translation of artificial intelligence into routine practice. RECENT FINDINGS: Recent literature indicates that the principal barrier to artificial intelligence adoption in anesthesiology is not algorithmic performance but inadequate data integration. Data are distributed across anesthesia information management systems, electronic health records, and multiple medical devices, with variable data models, inconsistent semantic representations, and limited temporal synchronization. These systems were primarily designed for documentation, billing, and medicolegal purposes, rather than real-time analytics or secondary data use. In contrast, intensive care medicine has benefited from early investments in shared databases, which have facilitated reproducible artificial intelligence research and validation. Although emerging standards such as HL7 Fast Healthcare Interoperability Resources and common data models provide technical frameworks for interoperability, their implementation in anesthesiology remains partial. SUMMARY: The effective deployment of artificial intelligence in anesthesiology depends on the development of interoperable, high-quality data infrastructures. Establishing standardized data models, semantic harmonization, temporal alignment, and robust data governance is a prerequisite for scalable, trustworthy artificial intelligence-enabled perioperative care.
4 Apr 2026
Read appraisal →Respiratory medicine
A review of artificial intelligence-based research on chronic obstructive pulmonary disease.
In recent years, with the rapid development of artificial intelligence (AI), Chronic Obstructive Pulmonary Disease (COPD), one of the world's three major chronic diseases, has achieved remarkable progress in diagnosis, grading, and prognosis, which is of great significance for promoting the clinical transformation of respiratory diseases. To deeply explore the application of AI in the diagnosis and management of COPD, this paper reviews recent studies based on machine learning and deep learning, covering screening and diagnosis, disease grading and assessment, disease management and monitoring, and treatment. First, the technical basis of COPD-related research is analyzed from five perspectives: traditional research methods, supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Then, the commonly used datasets and model evaluation metrics are summarized. Finally, the application scenarios of AI in COPD research are elaborated, focusing on three aspects: early screening and diagnosis, disease monitoring and risk prediction, and disease classification and risk stratification. This paper summarizes the main research hotspots at home and abroad over the past five years with representative models, and analyzes and compares the advantages and limitations of each type of model in specific COPD tasks through comparative experiments. The study also outlines prospects for the future development of this field, aiming to provide theoretical references and insights for subsequent research.
4 Apr 2026
Read appraisal →Journal of medical Internet research
Artificial Intelligence, Connected Care, and Enabling Digital Health Technologies in Rare Diseases With a Focus on Lysosomal Storage Disorders: Scoping Review
BACKGROUND: Rare diseases affect more than 300 million people globally, and only about 5% have approved therapies. Lysosomal storage disorders (LSDs) exemplify the diagnostic and long-term care complexity typical of rare diseases, and digital health technologies (DHTs), especially artificial intelligence (AI) and connected care (CC), are emerging tools to support LSD management. OBJECTIVE: We aimed to map and synthesize peer-reviewed and gray literature from the past decade on DHTs relevant for LSD care, with a primary analytic focus on AI-enabled and CC solutions and a contextual mapping of other enabling DHTs. Evidence distribution was charted by population, care-journey phase, and outcome domains to identify gaps, methodological limitations, and timely priorities relevant for research, clinical practice implementation, and policies. METHODS: We conducted a scoping review guided by a population, concept, context framework and operationalized through a Population, Intervention, Comparison, and Outcome (PICO)-informed data-charting structure to map study characteristics and reported outcomes, without causal or effectiveness assumptions and without risk-of-bias assessment. We searched PubMed, Google Scholar, and ClinicalTrials.gov for studies published between October 2015 and September 2024, complemented by AI-assisted discovery tools for citation extension. Reproducibility logs (search strings, run dates, filters, and stepwise counts) were maintained. Of 1751 records retrieved, 245 were included. Evidence was charted by LSD population, intervention class (AI, CC, and other enabling DHTs), outcome domains (patient, health care, and societal), and phase of the care journey. RESULTS: Among 245 included records, 92.2% (226/245) were peer-reviewed, and 7.8% (19/245) were gray literature; no completed and published randomized controlled trials or LSD-specific systematic reviews were identified, with evidence dominated by small, single-center observational studies. Overall, 40 peer-reviewed records reported AI-driven DHTs, 89 reported CC DHTs, and 144 reported other enabling DHTs (some multilabeled). Evidence was concentrated mostly in Gaucher and Fabry diseases. Nearly half of the mapped literature focused on screening and diagnosis, with fewer records addressing treatment intensification, rehabilitation, and end-of-life care. Outcomes were predominantly health care delivery performance measures, with fewer patient and societal outcomes. AI applications mainly supported diagnostic decision support, phenotyping, monitoring, tracking, and risk stratification; CC commonly involved telemedicine, remote monitoring, and patient-engagement platforms; enabling DHTs included interoperable data systems, registries, and digital infrastructures. CONCLUSIONS: The evidence base is appreciable for a niche field and reflects growing interest in AI and CC for LSD care, but heterogeneity and methodological limitations preclude inferences on effectiveness or routine implementation. This evidence map highlights relatively stronger areas and gaps, providing a structured foundation to inform timely expert consensus-building and research prioritization. Key priorities include interoperable data infrastructures and data availability, prospective multicenter evaluations, transparent reporting of algorithms and workflows, and implementation-relevant outcomes to support safe, equitable, and scalable adoption aligned with evolving European Union and global rare-disease priorities.
4 Apr 2026
Read appraisal →JMIR research protocols
Development and Validation of Machine Learning Models for Predicting Early Cognitive Decline Using Home Sensor-Derived Behavioral Data: Sensors in-Home for Elder Wellbeing (SINEW) Cohort Study
BACKGROUND: As the global population continues to age, the prevalence of geriatric conditions, including dementia and frailty, is also increasing. Early identification of individuals at an elevated risk of these conditions, such as those presenting with mild cognitive impairment (MCI) or prefrailty, can provide a critical window for prompt intervention aimed at preventing or reversing disease progression. To promote such early identification, there is a burgeoning interest in the use of digital sensor technology and predictive modeling. OBJECTIVE: This study aimed to use a continuous, home-based monitoring sensor system for older adults to distinguish those exhibiting normal aging from those with MCI, early dementia, prefrailty, or frailty, and to predict their transition from normal aging to one of these conditions. METHODS: This longitudinal cohort study will recruit 200 community-dwelling adults aged ≥65 years with normal cognition or MCI at baseline. A multi-sensor system will be installed in participants' homes, including passive infrared motion sensors, door contact sensors, bed sensors, medication box sensors, wearable activity bands, and Bluetooth proximity beacons. These devices will continuously capture spatiotemporal activity patterns, mobility indicators, sleep behaviors, and medication-taking routines. Annual assessments will include standardized cognitive tests (eg, Montreal Cognitive Assessment, Mini-Mental State Examination, Rey Auditory-Verbal Learning Test, digit span, Color Trails Test, semantic fluency, Stroop), frailty measures (modified Fried phenotype, gait speed, grip strength), mental health scales, sleep quality, and psychosocial indicators. Sensor-derived features-such as gait variability, activity regularity, sleep fragmentation, and medication adherence patterns-will be integrated with clinical data to develop supervised machine learning models. Planned approaches include logistic regression, random forests, gradient boosting, and deep learning. Model performance will be evaluated using cross-validation and independent test sets. Primary metrics will include area under the receiver operating characteristic curve, sensitivity, specificity, precision, recall, and F1-score. Models will be benchmarked against gold-standard clinical diagnoses and validated using temporal subsets of the dataset. RESULTS: Enrollment for this study started in November 2019 and will continue until March 2030. As of June 2025, we have enrolled 138 participants. Full data analysis has yet to begin. CONCLUSIONS: We aim to develop a reliable and effective sensor system for in-home use that will facilitate the early detection of cognitive and physical decline. In so doing, it will add to our current understanding of digital biomarkers. It is common for older adults to seek clinical intervention only when their cognitive impairment has already reached an advanced stage. The implementation of readily deployable sensor systems within community settings presents us with opportunities for prompt intervention, which holds the potential for delaying or reversing disease progression and allowing for a greater number of functional and meaningful years.
4 Apr 2026
Read appraisal →Angiology
Artificial Intelligence Techniques for Prognostic and Diagnostic Assessments in Peripheral Artery Disease: A Scoping Review
Peripheral artery disease (PAD) is a major public health concern worldwide, associated with high risk of mortality and morbidity related to cardiovascular and adverse limb events. Despite significant advances in both medical and interventional therapies, PAD often remains under-diagnosed, and the prognosis of patients can be difficult to predict. Artificial intelligence (AI) has brought a wide range of opportunities to improve the management of cardiovascular diseases, from advanced imaging analysis to machine-learning (ML)-based predictive models, and medical data management using natural language processing (NLP). The aim of this review is to summarize and discuss current techniques based on AI that have been proposed for the diagnosis and the evaluation of the prognosis in patients with PAD. The review focused on clinical studies that proposed AI-methods for the detection and the classification of PAD as well as studies that used AI-models to predict outcomes of patients. Through evaluation of study design, we discuss model choices including variability in dataset inputs, model complexity, interpretability, and challenges linked to performance metrics used. In the light of the results, we discuss potential interest for clinical decision support and highlight future directions for research and clinical practice.
4 Apr 2026
Read appraisal →Clinical & experimental optometry
Advancements in artificial intelligence for meibography: clinical utility and contributions to meibomian gland dysfunction management
Recent advances in artificial intelligence (AI) have enhanced the capabilities of meibography by enabling objective and quantitative assessment of meibomian gland structure. This review explores the clinical utility of AI-based meibography in the diagnosis and management of Meibomian Gland Dysfunction (MGD), with a focus on segmentation, morphological analysis, and disease staging. Developments in deep learning have enabled more precise gland feature extraction, including gland dropout, density, and tortuosity, supporting efforts towards standardised and reproducible clinical evaluation. Although not the focus of this review, insights from traditional image processing techniques are referenced to highlight potential areas of improvement in current AI models. Key issues such as limited modelling of regional gland variation, restricted dataset diversity, and lack of standardised image quality control are discussed. Although significant progress has been made, further work is needed to ensure AI-driven meibography tools are generalisable, interpretable, and suitable for broad clinical implementation.
4 Apr 2026
Read appraisal →BMJ open
Use of ambient AI scribe in physicians' clinical documentation: a protocol for a systematic review on effectiveness, efficiency, and satisfaction
INTRODUCTION: Clinical documentation is a significant driver of burnout among physicians. Ambient artificial intelligence (AI) scribes, which leverage generative large language models to automate the creation of clinical notes from patient-physician conversations, are rapidly emerging as a potential solution. While these tools promise to enhance efficiency and reduce administrative tasks, concerns about the quality, accuracy and potential biases persist. There is now a need for a systematic synthesis of evidence to evaluate the impact of these technologies in clinical practice. To assess the effects of ambient AI scribes on physicians' clinical documentation, the specific objectives are to: (1) evaluate the effectiveness of these tools on documentation, including accuracy and completeness; (2) synthesise evidence on the impact on physician efficiency after adoption, including time spent on documentation and (3) examine physicians' satisfaction with these tools, including physicians' perceived burden. METHODS AND ANALYSIS: A systematic review of quantitative or mixed-method studies as well as preprints will be conducted. We will perform a comprehensive search of four electronic databases (PubMed, IEEE Xplore, APA PsycInfo and Web of Science, along with medRix and ClinicalTrials.gov for preprints) for empirical studies published between January 2023 and March 2026. The review will synthesise studies comparing physicians' use of ambient AI scribes with traditional documentation approaches. Given the anticipated heterogeneity of the studies, a narrative synthesis will be employed to summarise the findings. Where common quantitative outcomes exist, effect sizes will be calculated using Hedges' g, mean differences or risk ratios/odds ratios as appropriate. The overall quality of evidence will be assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. ETHICS AND DISSEMINATION: As no patient data are involved in the data collection, no ethical approval is acquired. Results will be disseminated in a peer-reviewed, open-access journal, and presented at relevant academic conferences. PROSPERO REGISTRATION NUMBER: CRD420251149086.
4 Apr 2026
Read appraisal →Abdominal radiology (New York)
Artificial intelligence in gastric cancer: a systematic review of machine learning and deep learning applications
BACKGROUND: Gastric cancer (GC) remains a major global health concern, ranking as the fifth most prevalent malignancy and the fourth leading cause of cancer-related mortality worldwide. Although early detection can increase the 5-year survival rate of early gastric cancer (EGC) to over 90%, more than 80% of cases are diagnosed at advanced stages due to subtle clinical symptoms and diagnostic challenges. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has shown great promise in addressing these limitations. OBJECTIVES: This systematic review aims to evaluate the performance, applications, and limitations of ML and DL models in GC management, with a focus on their use in detection, diagnosis, treatment planning, and prognosis prediction across diverse clinical imaging and data modalities. METHODS: Following the PRISMA 2020 guidelines, a comprehensive literature search was conducted in MEDLINE, Web of Science, and Scopus for studies published between 2004 and May 2025. Eligible studies applied ML or DL algorithms for diagnostic or prognostic tasks in GC using data from endoscopy, computed tomography (CT), pathology, or multi-modal sources. Two reviewers independently performed study selection, data extraction, and risk of bias assessment. RESULTS: A total of 59 studies met the inclusion criteria. DL models, particularly convolutional neural networks (CNNs), demonstrated strong performance in EGC detection, with reported sensitivities up to 95.3% and Area Under the Curve (AUCs) as high as 0.981, often exceeding expert endoscopists. CT-based radiomics and DL models achieved AUCs ranging from 0.825 to 0.972 for tumor staging and metastasis prediction. Pathology-based models reported accuracies up to 100% for EGC detection and AUCs up to 0.92 for predicting treatment response. Cross-modality approaches combining radiomics and pathomics achieved AUCs up to 0.951. Key challenges included algorithmic bias, limited dataset diversity, interpretability issues, and barriers to clinical integration. CONCLUSION: ML and DL models have demonstrated substantial potential to improve early detection, diagnostic accuracy, and individualized treatment in GC. To advance clinical adoption, future research should prioritize the development of large, diverse datasets, implement explainable AI frameworks, and conduct prospective clinical trials. These efforts will be essential for integrating AI into precision oncology and addressing the increasing global burden of gastric cancer.
4 Apr 2026
Read appraisal →Advanced science (Weinheim, Baden-Wurttemberg, Germany)
A Wearable Brain-Computer Interface for Mitigating Car Sickness via Attention Shifting
Car sickness, an enormous vehicular travel challenge, affects a significant proportion of the population. Pharmacological interventions are limited by adverse side effects, and effective nonpharmacological alternatives remain to be identified. Here, we introduce a novel attention-shifting method based on a closed-loop, artificial intelligence (AI)-driven, wearable mindfulness brain-computer interface (BCI) to alleviate car sickness. As the user performs an attentional task, i.e., focusing on breathing as in mindfulness, with a wearable headband, the BCI collects and analyzes electroencephalography (EEG) data via a convolutional neural network to assess the user's mindfulness state and provide real-time audiovisual feedback. This approach might sustainedly shift the user's attention from physiological discomfort toward the BCI-based mindfulness practices, thereby mitigating car sickness symptoms. The efficacy of the proposed method was rigorously evaluated in two real-world experiments, namely, short and long car rides, with a large cohort of more than 100 participants susceptible to car sickness. Remarkably, over 83% of the participants rated the BCI-based attention shifting as effective, with significant reductions in car sickness severity, particularly in individuals with severe symptoms. Furthermore, EEG data analysis revealed a neurobiological signature of car sickness, which provided mechanistic insights into the efficacy of the BCI-based attention shifting for alleviating car sickness. This study proposes a wearable, nonpharmacological intervention for car sickness, validated in a relatively large-scale study involving over 100 participants in real-world car riding. These findings, derived from a between-cohort comparison, support the potential of this approach to improve the travel experience for car sickness sufferers and represent a novel practical application of BCI technology.
4 Apr 2026
Read appraisal →Patient education and counseling
Patient and clinician engagement with generative artificial intelligence (GenAI): A scoping review of implications for patient-centered communication
OBJECTIVE: To examine the influence of the emerging use of generative artificial intelligence (GenAI) within electronic health records and among the public on the patient-centeredness of communication in healthcare. METHOD: In this scoping review, we conducted a systematic search for peer-reviewed studies in PubMed and PsycInfo that empirically examined GenAI involvement in clinical communication. We then mapped study findings onto a well-established framework for patient-centered communication. RESULTS: Our search yielded 67 studies for analysis. Results suggest that integration of GenAI into healthcare communication has the potential to increase clinician efficiency in interacting with patients, to expand channels for patients to obtain information about their healthcare, and to enhance empathy in clinical communication. However, findings also indicate variability in the quality of information produced by GenAI, the potential for GenAI to recast the clinician as a technical supervisor rather than a humanistic care provider, and several issues of equity and privacy raised by engagement with GenAI. CONCLUSION: As GenAI becomes more prevalent in healthcare, rigorous examination of GenAI is needed to ensure that its development and implementation aids rather than hinders patient-centered communication. We conclude with an agenda for further research on GenAI grounded in the PCC framework underlying our review. PRACTICE IMPLICATIONS: Findings from this review highlight the current potential benefits and limitations of GenAI as a third party to clinical communication. Continued efforts toward developing and applying GenAI for effective healthcare communication should focus on protecting patients from potential drawbacks and maximizing nascent benefits for patient-centered communication.
4 Apr 2026
Read appraisal →IEEE transactions on medical imaging
EviVLM: When Evidential Learning Meets Vision Language Model for Medical Image Segmentation
The disparity between image and text representations, often referred to as the modality gap, remains a significant obstacle for Vision Language Models (VLMs) in medical image segmentation. This gap complicates multi-modal fusion, thereby restricting segmentation performance. To address this challenge, we propose Evidence-driven Vision Language Model (EviVLM)-a novel paradigm that integrates Evidential Learning (EL) into VLMs to systematically measure and mitigate the modality gap for enhanced multi-modal fusion. To drive this paradigm, an Evidence Affinity Map Generator (EAMG) is proposed to collect complementary cross-modal evidences by learning a global cross-modal affinity map, thus refining modality-specific evidence embedding. An Evidence Differential Similarity Learning (EDSL) is further proposed to collect consistent cross-modal evidences by performing Bias-Variance Decomposition on differential matrix derived from bidirectional similarity matrices between image and text evidence embeddings. Finally, the subjective logic is used for mapping the collected evidences to opinions, and the Dempster-Shafer's theory based combination rule is introduced for opinion aggregation, thereby quantifying the modality gap and facilitating effective multi-modal integration. Experimental results on three public medical image segmentation datasets validate that the proposed EviVLM can achieve state-of-the-art performance. Code is available at: https://github.com/QingtaoPan/EviVLM.
4 Apr 2026
Read appraisal →Journal of telemedicine and telecare
Telemedicine and digital health for chronic conditions in pediatrics: A systematic review
BackgroundThe prevalence of chronic diseases in children and adolescents has significantly increased. The COVID-19 pandemic accelerated telemedicine adoption, leading to improved healthcare access and outcomes. Despite its benefits, telemedicine is still not fully integrated into standard care. Previous reviews have been limited in scope. This systematic review aims to provide a comprehensive overview of recent trials on web or app-based telemedicine for chronically ill children and adolescents, identifying interventions and outcomes for future healthcare strategies.MethodsA comprehensive search was conducted on PubMed on November 21, 2023, and updated on January 9, 2025. Results were filtered to include prospective intervention studies involving children and adolescents with chronic conditions and limited to publications from 2017 onwards. A total of 8699 results were retrieved, and 1506 titles were screened for eligibility, resulting in 116 studies included in the review.ResultsThese studies covered 45 different conditions, with the highest numbers for diabetes, asthma, and obesity. Telemedicine interventions varied, including gamification, video meetings, integrated devices, psychological components, symptom tracking, and educational content. Most studies reported high feasibility and patient satisfaction. Clinical outcomes improved in some cases, and none were worse than standard care. The heterogeneity limited direct comparisons and meta-analysis, and small sample sizes in many studies affected the generalizability and statistical significance of the findings.ConclusionsOur study highlights the potential of telemedicine in managing chronic illnesses among children, emphasizing the need for standardized methodologies, larger sample sizes, and continued investment in infrastructure and equitable policies to fully realize its benefits in improving accessibility, convenience, cost savings, and overall health outcomes.
4 Apr 2026
Read appraisal →Journal of biomedical informatics
Natural language generation in healthcare: A review of methods and applications
OBJECTIVE: This study presents a systematic review of natural language generation (NLG) methods and applications in the medical domain, providing quantitative and qualitative analyses to answer four key research questions regarding methods, evaluation, applications, and challenges of NLG in healthcare. METHODS: We searched PubMed, ACM Digital Library, Web of Science, Science Direct, Scopus, Embase, and ACL Anthology for NLG-related studies in healthcare from 2018 to 2024. Out of 3,988 research articles, 113 met the inclusion criteria and were analyzed across data modality, model architecture, evaluation metrics, and application domain. RESULTS AND CONCLUSION: NLG in healthcare has grown substantially, with annual publications increasing from 2 in 2018 to 40 in 2024. Of the 113 included studies, text-to-text generation was the most common data modality (65.5%), followed by image-to-text (19.5%) and multimodal-to-text (15.0%). Transformer-based architectures were dominant, especially encoder-decoder models (61.6%). Automatic evaluation metrics such as ROUGE (81.4%) and BLEU (57.5%) were widely used. Human evaluation metrics, such as Likert scales (31.9%), were increasingly adopted. The four most prevalent application domains include summarization (e.g., discharge summaries, radiology reports), clinical documentation, medical dialogue, and data augmentation. The transformer-based large language models (LLMs) and the accumulation of large-scale multimodal clinical datasets have remarkably advanced NLG in healthcare. However, challenges remain in factual consistency, explainability, evaluation robustness, and AI safety. Addressing these challenges is essential for the adoption of NLG in various healthcare applications.
4 Apr 2026
Read appraisal →Computer methods and programs in biomedicine
Application of artificial intelligence in colonoscopy imaging for polyp analysis-A systematic review
The colon is a major component of the digestive system, so early detection of colorectal polyps is essential in preventing colorectal cancer, which is a leading cause of cancer-related death worldwide. While colonoscopy remains the gold standard for polyp detection, its diagnostic accuracy is highly operator-dependent. Recent advances in Deep Learning (DL), a branch of Artificial Intelligence (AI), have shown substantial potential to improve colonoscopy image analysis by enhancing the accuracy, consistency, and objectivity of polyp detection, segmentation, and classification. Artificial intelligence-based systems have significantly reduced inter-observer variability and increased diagnostic efficiency, ultimately transforming the landscape of colorectal lesion assessment. This survey provides a comprehensive and critical analysis of the current status of deep learning applications in colorectal polyp analysis. We systematically review state-of-the-art methodologies across various DL architectures-including Convolutional Neural Networks (CNNs), transformer-based models, and hybrid approaches-and examine their performance on publicly available benchmark datasets. Additionally, we highlight the strengths and limitations of existing techniques, explore the clinical relevance of AI-assisted tools, and identify prevailing challenges such as data imbalance, real-time deployment, and generalizability across diverse populations and colonoscopy devices. By consolidating key advances and outlining future research directions, this review aims to serve as a valuable resource for researchers, clinicians, and developers seeking to leverage deep learning to enhance colorectal polyp detection, diagnosis, and clinical decision-making.
4 Apr 2026
Read appraisal →The Journal of pathology
Integrating artificial intelligence (AI) into colorectal cancer reporting
Artificial intelligence (AI) and deep learning (DL) are transforming cancer research and clinical care, with histopathology playing a central role in this transformation. In colorectal cancer (CRC), the second leading cause of cancer mortality world-wide, multimodal and vision-language models (VLMs) hold particular promise for enhancing the standardisation of histopathology reporting, the understanding of disease biology, and the discovery of novel prognostic indicators. Despite the availability of guidelines and reporting templates for essential prognostic indicators, variability remains in how key features such as TNM staging or tumour deposits are assessed and reported in routine clinical practice. AI-based tools have the potential to support refined extraction of established and extended features directly from whole-slide images. In parallel, recent studies have shown that DL models applied to pathology slides and associated AI-based biomarkers can outperform traditional histopathological prognostic indicators and uncover novel parameters, including tumour-adipocyte interactions, tumour-stroma ratio, and immune cell patterns at the invasive margin. Here, we review recent advances in both domains: AI-assisted standardisation of CRC pathology reporting and AI-driven identification of novel prognostic biomarkers. We highlight the need to refine and standardise CRC reporting practices and propose that a harmonised approach combining established pathology features with AI-derived prognostic indicators could refine risk assessment and improve outcomes for CRC patients. © 2026 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
4 Apr 2026
Read appraisal →Physiological reviews
An umbrella review of systematic reviews of the impact of wrist-worn wearables on health outcomes
We conducted an umbrella review to synthesize the evidence on the effectiveness of interventions incorporating wrist-worn wearables' feedback on diverse health outcomes including health promotion (i.e., health behaviors and disease risk perception) morbidity, mortality, functioning, and other health-related metrics in humans. We searched in MEDLINE, Web of Science, Embase, and Cochrane Library until March 18, 2025, for eligible systematic reviews. After screening 9,487 citations, we identified 39 systematic reviews, which included 98 original studies (1 observational study, 95 randomized controlled trials, and 1 pre-post study). The reviews primarily focused on adult populations, individuals with cardiometabolic conditions, and cancer survivors. The original interventional studies mainly included Fitbit (40.2%), Polar (12.4%), and ActiGraph (10.3%) devices. Over 80% of the clinical trials involved complex behavioral interventions with wearable-based feedback, and the control groups varied. Most systematic reviews were rated as low confidence, with common flaws including inadequate consideration for risk-of-bias and heterogeneity. Interventions incorporating wrist-worn activity trackers increased physical activity in diverse populations. The effect of interventions incorporating wrist-wearables' feedback on cardiometabolic risk markers, quality of life, depression/anxiety, and pain was limited and remained inconsistent. Our findings rely on existing systematic reviews, which may vary in quality, review methodologies, and comprehensiveness. There is also potential for missing more recent evidence not yet captured in these reviews. These limitations should be considered when interpreting our results. Acknowledging these caveats, wrist-worn wearables seem to increase physical activity and may have additional benefits that require further study.
4 Apr 2026
Read appraisal →Stroke
Balancing Innovation and Responsibility: Ethical and Privacy Challenges in Stroke Digital Health
Digital health solutions are being increasingly used to support care delivery across various medical fields. In stroke care, technology has contributed to enhancing prehospital workflows, facilitating communication among health care professionals, and enabling fast-track systems for hyperacute stroke management. Furthermore, digital tools are used to improve etiological diagnosis and allow for more tailored long-term follow-up of stroke patient survivors. Digital technologies have become an integral part of stroke management, and the rapid evolution of artificial intelligence continues to accelerate and drive this transformation. However, ethical and privacy considerations are central and represent a major challenge for the design and implementation of digital solutions in health care. Health care professionals involved in stroke care require a clear understanding of the digital tools they use, including their potential, limitations, and biases, and must be aware of the responsibilities inherent in relying on such systems for treatment decisions and outcome prediction. This topical review aims to examine current digital innovations in stroke care, with a particular focus on the key ethical and privacy challenges they raise.
4 Apr 2026
Read appraisal →Clinical nutrition ESPEN
Artificial intelligence in clinical nutrition. A narrative review.
The rapid integration of Artificial Intelligence (AI) into healthcare, particularly clinical nutrition, holds transformative potential for enhanced diagnostics, risk prediction, and personalized therapeutic support. However, many clinicians lack sufficient understanding of AI principles, capabilities, and limitations, which may hinder adoption, lead to inappropriate use, or result in missed opportunities to enhance patient care. This narrative review aims to provide an accessible overview of foundational AI concepts, such as machine learning, deep learning, and large language models, and their practical applications in clinical nutrition. While AI offers immense promise in advancing nutritional care, its successful implementation requires clinicians to be adequately prepared to engage with these technologies. Education programs tailored to healthcare professionals, interdisciplinary collaboration between AI experts and clinicians, and robust ethical oversight are critical to ensure responsible and effective integration. By equipping clinicians with the necessary knowledge and tools, AI can serve as a powerful ally in delivering personalized and patient-centered nutritional care while maintaining human expertise at the forefront of decision-making.
2 Apr 2026
Read appraisal →International journal of nursing studies
The use of large language models in clinical documentation: A scoping review
BACKGROUND: Clinical documentation is essential for safe, high-quality care but has become increasingly complex, contributing to clinician burnout. Large language models offer potential to ease documentation by generating summaries, structuring data, and ensuring compliance. However, concerns remain regarding accuracy, bias, privacy, and regulatory risks. OBJECTIVE: To map current literature on large language models applications in clinical documentation, evaluating their benefits, limitations, and ethical considerations. INFORMATION SOURCES: Five electronic databases (i.e., PubMed, Scopus, CINAHL, Cochrane Library, and IEEE Xplore) covering peer-reviewed literature published in English between January 2009 and August 2025. METHODS: This scoping review followed Arksey and OMalleys framework and was reported in accordance with PRISMA-ScR guidelines. Screening, data extraction, and quality appraisal were conducted independently by multiple reviewers using Joanna Briggs Institute tools. Findings were synthesized using descriptive and narrative approaches. RESULTS: Forty-one studies met inclusion criteria, most originating from the United States. Large language models were primarily applied to clinical note generation, discharge summaries, and provider-patient encounter documentation. Key evaluation metrics included content accuracy, linguistic quality, and summarization performance. Large language models demonstrated potential to improve documentation efficiency and readability, with some studies reporting up to 40 % time savings. However, concerns about factual inaccuracies, hallucinations, and reduced performance in complex cases were common. Clinician perceptions were mixed. Some found notes generated by large language models helpful and well-structured, while others raised concerns about reliability, liability, and loss of clinical nuance. Ethical challenges included data privacy, security, and algorithmic bias, with varying levels of compliance across settings. CONCLUSIONS: Large language models hold significant promise for enhancing clinical documentation by improving efficiency, standardization, and clarity. However, their safe and effective use requires rigorous attention to accuracy, ethical safeguards, and clinician trust. Integration must support, rather than supplant, clinical reasoning and patient-centered care. Co-design with clinicians, real-world evaluation, and artificial intelligence literacy are essential to ensure that these technologies augment, not erode, professional judgment and care quality. REGISTRATION: Open Science Framework Registries (https://osf.io/m4h3q).
2 Apr 2026
Read appraisal →Expert review of medical devices
Modalities and algorithms for generalized motor seizure detection and prediction: a scoping review
INTRODUCTION: Generalized tonic - clonic seizures (GTCS) is a major cause of sudden unexpected death in epilepsy (SUDEP); thus, continuous monitoring is essential. Electroencephalography (EEG) is the diagnostic gold standard; however, non-EEG wearables have emerged as promising, user-friendly alternatives enhancing comfort, mobility, and social acceptance. AREAS COVERED: This scoping review compared sensing modalities and computational methods used for seizure detection and prediction, identified effective sensor combinations, and highlighted current research gaps. Following PRISMA-ScR guidelines, Scopus, IEEE Xplore, and PubMed databases were searched up to 22 April 2025. Twenty-nine studies met inclusion criteria. For seizure detection, combining electrodermal activity (EDA) with motion sensors - accelerometers (ACC) and gyroscopes (GYR) - and surface electromyography (sEMG) achieved high reliability, 98.6% precision, while using ACC and EDA alone yielded superior sensitivities up to 97.2% and a lower false alarm rate (FAR) of 0.53/24 h. Regarding seizure prediction, combining EDA, blood volume pulse (BVP), ACC, and temperature showed the highest sensitivity of 75.6%. EXPERT OPINION: Wearable multimodal non-EEG seizure detection and prediction systems offer personalized care but face validation, hardware, and privacy hurdles. Future success depends on efficient AI, IoT integration, patient-centric design, and clear regulations ensuring accessible and trustworthy clinical tools. People with epilepsy experience sudden episodes of abnormal brain activity called seizures, or fits. One serious type, the generalized tonic – clonic seizure (GTCS), causes loss of consciousness, muscle stiffening, and body jerking, which can lead to injury or even sudden unexpected death in epilepsy (SUDEP).Traditionally, seizures are monitored in hospitals using electroencephalography (EEG), which records brain signals. However, EEG systems are uncomfortable for daily use and limit continuous monitoring. Recent innovations in wearable sensor technology – such as motion sensors (accelerometers), skin conductance sensors (electrodermal activity), and heart or blood flow sensors (photoplethysmography) – offer a more practical, noninvasive alternative. These devices can be worn as smartwatches or bracelets, fitting naturally into everyday life.Artificial intelligence (AI) and machine learning play an increasingly important role in these systems. By recognizing complex patterns in motion and physiological signals, AI algorithms can automatically detect seizures and, in early research, may even predict when a seizure is likely to occur. While results so far are promising, more clinical studies and larger datasets are needed to confirm accuracy and reliability across different patient groups.Advances in wearable sensors and artificial intelligence could revolutionize epilepsy management by making continuous seizure monitoring and prediction possible in daily life. Although more evidence is required before widespread clinical adoption, these technologies hold strong potential to improve safety, autonomy, and quality of life for people living with epilepsy.
2 Apr 2026
Read appraisal →Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
Multicenter Clinical Validation of an Artificial Intelligence Diagnostic Classification Model for Laryngoscopy Images
OBJECTIVE: To develop and externally validate a computer-aided diagnosis (CADx) model using artificial intelligence (AI) for classifying laryngeal lesions from laryngoscopy images into high-risk (HR), low-risk (LR). STUDY DESIGN: Retrospective multicenter development of a CADx model and external validation on independent cohorts. SETTING: Multicenter tertiary referral hospitals (Italy, India, China, Greece, and Spain). METHODS: Over 20,000 images derived from laryngoscopic examinations were retrieved. Images were annotated based on histopathology or expert consensus. A deep learning model was trained using an internal dataset and evaluated on 2 external datasets to assess generalizability. The CADx model classifies only images containing visible lesions, discriminating between LR and HR categories. Diagnostic performance was measured using standard metrics, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Model performance was compared with physicians of varying expertise and ChatGPT-4o. RESULTS: The computer-aided diagnosis model achieved a similar performance across internal and external datasets in distinguishing HR from LR lesions, with accuracy/AUC of 0.90/0.89 internally, 0.85/0.85 on the Greek dataset, and 0.88/0.88 on the Spanish dataset. The model's accuracy was statistically noninferior to that of otolaryngologists and expert laryngologists, and superior to general practitioners and ChatGPT-4o. CONCLUSION: This is a large multicenter clinical validation of a CADx model for laryngeal endoscopy, demonstrating generalizability and performance comparable to clinicians in discriminating between LR and HR lesions. The model's success supports its potential role in augmenting diagnostic capabilities, especially in resource-limited settings. A prospective multicenter clinical trial is underway to assess real-world clinical implementation.
2 Apr 2026
Read appraisal →J Med Internet Res
Use of Health and Welfare Technology in Palliative Care: State-of-the-Art Review
BACKGROUND: As more individuals live longer with complex conditions, the need for effective palliative care (PC) grows. It has been stated that access to PC should be integrated early and delivered in a timely manner to patients with life-threatening illnesses. Health and welfare technologies (HWTs) offer tools to enhance care delivery, particularly in home and rural settings. Although there is a profound lack of evidence regarding the impact when used in PC, it is necessary to critically assess the current state of knowledge regarding impacts and consequences of technologies, ensuring that their integration considers broader implications for patients, caregivers, and health care systems in PC. OBJECTIVE: This review explores health and welfare technology used in PC, aiming to inform practice and improve care quality. METHODS: This state-of-the-art review included empirical studies describing the use of HWT in PC for adult patients. We used a thematic synthesis approach to compare studies and provide a synthesis of the key points. RESULTS: Based on the inclusion criteria, 94 studies were included. PC is both a clinical specialty and an overall approach to care that focuses on improving quality of life and relieving suffering for patients and families facing serious illness, based on needs and not prognosis. HWT shows potential to increase access and continuity of care, for symptom management to support patients to remain at home and prevent frequent emergency visits. It can have the potential to build and remain relationships between patients, their families, and the health care team, as well as for interprofessional collaboration and support. However, there are challenges to overcome that might affect the quality of care when using technology. CONCLUSIONS: HWT shows potential as a complement to usual PC. Our findings point toward the importance of caution in choosing when to use HWT in PC, and for which patients.
1 Mar 2026
Read appraisal →Transl Psychiatry
Application of artificial intelligence in schizophrenia rehabilitation management: a systematic scoping review
Artificial intelligence (AI) is increasingly used in mental health, yet its rehabilitation-oriented applications in schizophrenia have not been systematically mapped. We conducted a systematic scoping review of PubMed, Web of Science, IEEE Xplore and the ACM Digital Library (January 1, 2012-October 31, 2025; two search rounds), applying operationalized rehabilitation boundaries and excluding diagnostics-only case-control studies. We extracted data on data sources, feature engineering, model families, validation, calibration, interpretability, application domains, outcomes and implementation readiness. Eighty-three studies met inclusion criteria (median sample size 160; 55% longitudinal). Applications focused on symptom monitoring (48/83), medication management (19/83) and risk management (16/83), whereas functional training (1/83) and psychosocial support (3/83) were rarely targeted. Supervised learning predominated (53/83, 63.9%) over representation learning (20/83, 24%), most commonly using speech/text, electronic health records and smartphone sensing. Across classification tasks, the median AUC was 0.79 (IQR 0.71-0.86); relapse early-warning models showed a median sensitivity of 31.5% at 88.0% specificity. Only four studies reported external validation and three described closed-loop deployment, including one randomized trial that improved adherence. Proxy endpoints were more common than clinical endpoints, and reporting of calibration/uncertainty and fairness auditing was sparse. Overall, AI shows promise for monitoring, adherence support and relapse risk stratification, but routine-care deployment will require externally validated and calibrated human-in-the-loop decision support, privacy-preserving multimodal pipelines and pragmatic trials targeting functional outcomes and participation.
1 Mar 2026
Read appraisal →Aesthetic Plast Surg
The Rise of Intelligent Plastic Surgery: A 10-Year Bibliometric Journey Through AI Applications, Challenges, and Transformative Potential
BACKGROUND: Driven by advancements in deep learning, surgical robots, and predictive modeling technologies, the integration of artificial intelligence (AI) and plastic surgery has expanded rapidly. Although AI shows the potential to enhance precision and efficiency, its clinical integration faces challenges, including ethical concerns and interdisciplinary complexity, which require a systematic analysis of research trends. METHODS: The CiteSpace and VOSviewer software were used to conduct a quantitative analysis of 235 documents in the core collection of Web of Science from 2016 to 2024. Co-citation networks, keyword co-occurrence, burst detection, and cluster analysis were employed to map the research trajectories. The inclusion criteria gave priority to studies that explicitly incorporated artificial intelligence into surgical designs or outcomes. The contributions of countries, institutions, and authors were evaluated through centrality indicators. RESULT: Publications related to artificial intelligence have grown exponentially, with the USA, Germany, and Canada leading research output. Harvard and Stanford Universities dominate in terms of institutional contributions, but cross-institutional collaboration remains limited. The keyword cluster highlights the innovations of artificial intelligence in breast reconstruction, facial analysis, and automated grading systems. Burst terms such as "deep learning," "risk assessment," and "attractiveness" underscore AI's role in optimizing surgical outcomes, but they also expose biases against Western-centric beauty standards. Ethical concerns, dataset diversity gaps, and overreliance on AI-driven decisions have become key obstacles. CONCLUSION: The integration of artificial intelligence in plastic surgery goes beyond the utility based on tools and into data-informed surgical engineering. The persistent gap in collaboration and dataset diversity highlights the need for global, interdisciplinary efforts to address technical and ethical challenges while advancing AI's clinical utility. Future research must prioritize transparency, inclusivity, and collaborative innovation to realize AI's transformative potential while mitigating risks. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
1 Mar 2026
Read appraisal →BMC Palliat Care
Identification and mapping of mHealth interventions applied in palliative care in Sub Saharan Africa: a scoping review
BACKGROUND: Although palliative care is very essential for improving quality of life (QOL) by providing relief from pain and other distressing symptoms, its coverage in sub Saharan Africa remains remarkably low. Utilization of mHealth could be a fundamental healthcare system strengthening approach to enhance and promote palliative care in the region. There is paucity of evidence regarding evidence on the use of these interventions in SSA because it is a relatively new concept in the region. Therefore, there was need to identify and map the available/ applied mHealth interventions for palliative care in SSA basing on published and grey literature. AIM: To examine the nature and use of mHealth interventions for palliative care and factors influencing their use in sub Saharan Africa (SSA). METHODS: The review was based on the JBI methodology for scoping review. PUBMED, CINAHL, Embase and Google Scholar databases were searched for published articles with no time restrictions. Unpublished and grey literature was also searched for. Data was extracted from the retrieved sources and synthesized. The findings were reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) in a comprehensive and transparent way. RESULTS: A total of 224 articles were identified and 35 of these met the inclusion criterion and were considered for the review. The results show use of mHealth interventions for palliative care in 12 countries of the SSA. Only 12 (35.3%) were app-based, 9(26.5%) were SMS-text-based, 4(11.8%) were telemedicine-based,5(14.7%) were phone call-based, 3(8.8%) were mixed while 1(2.9%) used wearable-sensor-based mHealth interventions. Key uses of mHealth interventions in palliative care included health strengthening, psychosocial support to patients and families, enhancement of compliance to appointments for patients, promotion of adherence to medication, health education, monitoring, evaluation and research and capacity building for palliative care health professionals. A number of influencing factors were identified and they included wide-spread telephone ownership and access, great robustness of telephone and internet connectivity, high degree of perceived usefulness and ease of use, feasibility and acceptability of mHealth. Also, vital barriers such as low levels of technological advancement/expertise, irregularity and paucity of electric power supply, high likelihood to compromise patients' privacy and confidentiality and high illiteracy rates among the population were articulated by the articles. CONCLUSION: About a quarter of SSA countries have incorporated mHealth interventions for palliative care, underscoring the need for further research and development of the technologies. These approaches are quite beneficial to SSA, where there is a disproportionate disease burden and an inadequate healthcare system.
1 Feb 2026
Read appraisal →Curr Opin Neurol
Neuro-vestibular rehab: new developments.
PURPOSE OF REVIEW: This review highlights recent advances in neuro-vestibular rehabilitation, with emphasis on vestibular adaptation and emerging mobile technologies. It summarizes developments in promoting vestibular plasticity and discusses novel tools such as virtual reality, wearable sensors, and telehealth platforms that enhance access, engagement, and outcomes. The scope is broad, focusing on general principles rather than specific populations. RECENT FINDINGS: New methods to enhance vestibulo-ocular reflex (VOR) adaptation include incremental adaptation devices and gamified exercises. Inducing VOR gain-down adaptation temporarily increases postural sway, which normalizes via sensory reweighting, demonstrating central compensation. Portable tools like StableEyes show promise in boosting VOR gain with brief sessions. Concurrently, technology-driven approaches are gaining traction. Gamified mobile applications and wearable sensors allow home-based rehabilitation with remote supervision and monitoring, showing promising results in conditions like multiple sclerosis. Virtual reality interventions and telehealth models accelerated during the COVID-19 era, expanding therapy delivery to underserved populations. Adjunctive methods such as vibrotactile feedback and galvanic vestibular stimulation are emerging as complementary therapies. SUMMARY: Recent developments are advancing vestibular rehabilitation by refining adaptive training techniques and leveraging digital tools to overcome barriers in access and adherence. These innovations point to a more personalized, technology-enabled approach to optimizing neuro-vestibular recovery.
1 Feb 2026
Read appraisal →Int J Surg
Artificial Intelligence in melanoma research: a bibliometric analysis
BACKGROUND: As one of the most lethal skin cancers, melanoma has encountered many obstacles in diagnosis and therapy. Artificial intelligence (AI) can help improve early diagnosis, prognosis, and treatment of melanoma. However, there is a lack of detailed and accurate bibliometric analysis of the field. METHODS: All publications were extracted from Web of Science Core Collection based on AI and melanoma terms. Bibliometric analysis was conducted on 1476 articles/reviews by using VOSviewer, CiteSpace, and bibliometrix for co-authorship, citation, keyword, and journal analysis. RESULTS: There was a sudden increase in publications each year after 2017 and reached 260 in 2024. The United States (337 publications) and China (292 publications) ranked top in publication productivity. Germany was the top institution and author country. IEEE Access (57 publications) and Diagnostics (54 publications) were core journals. The two most prominent research hotspots were AI-assisted diagnosis and AI-integrated immunotherapy according to the keyword analysis. CONCLUSION: AI in melanoma research has exploded since 2017. It is recommended that future research focus on various datasets, explainable AI, and cross-disciplinary cooperation to promote the transformation of achievements into clinical practice.
1 Feb 2026
Read appraisal →Ageing Res Rev
AI-driven aging digital twins: A roadmap for clinical translation in precision geriatrics
Aging and related diseases account for ⁓70 % of global mortality with declining physiological homeostasis and cognitive function increasing susceptibility to neurodegenerative disorders, cardiovascular diseases and cancer. Treatment is hampered by imprecise diagnostics and limited therapeutic options. Precision medicine involving artificial intelligence (AI)-powered Aging Digital Twin (ADT) technology which integrates multi-source heterogeneous data via advanced AI and deep learning has great potential. Virtual doppelgangers enable real-time health monitoring, predictive analytics and personalized interventions to facilitate early disease detection and optimize risk assessment and treatment strategies. Challenges remain and include age-stratified biases in training datasets and variations in data quality for older populations which may limit model generalizability. Critical concerns regarding data privacy, algorithmic transparency and clinical validation of ADT models remain unresolved. The current review examines the transformative potential of AI-enabled ADTs in precision medicine for aging and highlights technical and ethical limitations.
1 Jan 2026
Read appraisal →Clin Otolaryngol
Artificial Intelligence in Facial Plastic Procedures
OBJECTIVES: We reviewed the prospects of Artificial Intelligence (AI) and its usage in facial plastic surgery. METHODS: Literature search was performed using PubMed, Medline, Google and Google Scholar search engines between 2000 and 2025 years. RESULTS: AI is poised to impact the threefold missions of patient care, education and research. It plays a role in aiding clinician decision-making, serving as virtual conversational agents for patient engagement, and forecasting surgical outcomes. Primary areas of AI exploration in the medical field include machine learning (ML), neural networks (NN) and natural language processing (NLP). ML models have demonstrated the ability to detect facial features such as larger nasofrontal and nasolabial angles, correlating with increased attractiveness post-cosmetic rhinoplasty. Efficient resource allocation can be achieved through ML models guiding the analysis of post-operative free flap viability in facial skin cancer reconstruction. During online patient consultations, AI virtual assistants (AIVA) utilising NLP comprehend human speech intent, responding through dialogue. Unsupervised ML in facial analysis software is currently employed for correctly diagnosing rare diseases by recognising dysmorphic craniofacial features in two-dimensional photographs. AI also finds application in assessing post-operative outcomes, as well as in surgical training and research. CONCLUSION: The use of AI in healthcare is on the rise, contributing to advancements in pre-operative assessments, predicting prognoses, managing patients and monitoring postoperative progress. Surgeons are encouraged to explore collaboration with data scientists to shape the evolution of AI, aiming to enhance surgical efficiency, improve patient outcomes and potentially alleviate the workload associated with repetitive administrative duties.
1 Jan 2026
Read appraisal →Ann Med
Advancements in artificial intelligence for atopic dermatitis: diagnosis, treatment, and patient management
Atopic dermatitis (AD) is a common and complex skin disease that significantly affects the quality of life of patients. The latest advances in artificial intelligence (AI) technology have introduced new methods for diagnosing, treating, and managing AD. AI has various innovative applications in the diagnosis and treatment of atopic dermatitis, with particular emphasis on its significant benefits in medical diagnosis, treatment monitoring, and patient care. AI algorithms, especially those that use deep learning techniques, demonstrate strong performance in recognizing skin images and effectively distinguishing different types of skin lesions, including common AD manifestations. In addition, artificial intelligence has also shown promise in creating personalized treatment plans, simplifying drug development processes, and managing clinical trials. Despite challenges in data privacy and model transparency, the potential of artificial intelligence in advancing AD care is enormous, bringing the future to precision medicine and improving patient outcomes. This manuscript provides a comprehensive review of the application of AI in the process of AD disease for the first time, aiming to play a key role in the advancement of AI in skin health care and further enhance the clinical diagnosis and treatment of AD.
1 Dec 2025
Read appraisal →Ann Med
Artificial intelligence-assisted diagnosis of early gastric cancer: present practice and future prospects
Gastric cancer (GC) occupies the first few places in the world among tumors in terms of incidence and mortality, causing serious harm to human health, and at the same time, its treatment greatly consumes the health care resources of all countries in the world. The diagnosis of GC is usually based on histopathologic examination, and it is very important to be able to detect and identify cancerous lesions at an early stage, but some endoscopists' lack of diagnostic experience and fatigue at work lead to a certain rate of under diagnosis. The rapid and striking development of Artificial intelligence (AI) has helped to enhance the ability to extract abnormal information from endoscopic images to some extent, and more and more researchers are applying AI technology to the diagnosis of GC. This initiative has not only improved the detection rate of early gastric cancer (EGC), but also significantly improved the survival rate of patients after treatment. This article reviews the results of various AI-assisted diagnoses of EGC in recent years, including the identification of EGC, the determination of differentiation type and invasion depth, and the identification of borders. Although AI has a better application prospect in the early diagnosis of ECG, there are still major challenges, and the prospects and limitations of AI application need to be further discussed.
1 Dec 2025
Read appraisal →JAMA
An AI-Powered Lifestyle Intervention vs Human Coaching in the Diabetes Prevention Program: A Randomized Clinical Trial
IMPORTANCE: Prediabetes is common, yet evidence-based lifestyle interventions are underutilized. OBJECTIVE: To determine whether referral to an exclusively artificial intelligence (AI)-led lifestyle intervention based on the Diabetes Prevention Program (DPP) is noninferior to referral to a human-led DPP in achieving recommended thresholds for weight loss, hemoglobin A1c (HbA1c) reduction, and weekly physical activity among adults with prediabetes and overweight or obesity. DESIGN, SETTING, AND PARTICIPANTS: This phase 3, parallel-group, pragmatic, noninferiority randomized clinical trial was conducted from October 11, 2021, to December 16, 2024 (last follow-up) at 2 US clinical sites in Baltimore, Maryland, and Reading, Pennsylvania. Adults 18 years or older with prediabetes and overweight or obesity were enrolled. INTERVENTIONS: Participants were randomized in a 1:1 ratio to receive either a referral to an AI-powered DPP lifestyle intervention delivered via a mobile app and Bluetooth-enabled digital scale or a referral to a human coach-led DPP lifestyle intervention delivered remotely. Both interventions were delivered independently of the study team over a 12-month period. MAIN OUTCOMES AND MEASURES: The primary outcome was a composite of maintaining an HbA1c less than 6.5% throughout the study and achievement of at least 5% weight loss, at least 4% weight loss plus at least 150 minutes of weekly physical activity (assessed with actigraphy), or an absolute reduction in HbA1c of at least 0.2 percentage points at 12 months. Noninferiority of referral to the AI-led DPP compared with referral to the human-led DPP was prespecified to be determined if the 1-sided 95% CI lower boundary of the risk difference did not cross -15%. RESULTS: A total of 368 participants were included (median [IQR] age, 58 [50-65] years; 71% were female, 27% were Black, 6% were Hispanic, and 61% were White; median [IQR] BMI, 32.3 [28.5-37.1]). After referral, 171 of 183 participants (93.4%) initiated the AI-led DPP and 153 of 185 (82.7%) initiated the human-led DPP. The primary outcome was achieved by 58 of 183 participants (31.7%) in the AI-led DPP group and 59 of 185 (31.9%) in the human-led DPP group (risk difference, -0.2% [1-sided 95% CI, -8.2%]), meeting the criterion for noninferiority. Findings were consistent across individual components of the composite end point and in sensitivity analyses. CONCLUSIONS AND RELEVANCE: Among adults with prediabetes and overweight or obesity, referral to a fully automated AI-led DPP was noninferior to referral to a human-led DPP in achieving a composite outcome based on weight reduction, physical activity, and HbA1c. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05056376.
1 Dec 2025
Read appraisal →BMC Geriatr
Smart aging: integrating AI into elderly healthcare
Artificial Intelligence (AI) is transforming geriatric healthcare by improving early disease detection, optimizing patient management, and enhancing clinical decision-making. AI-driven tools, including machine learning (ML) algorithms, predictive analytics, and assistive robotics, are increasingly utilized to address challenges associated with aging, such as frailty, multimorbidity, polypharmacy, and fall prevention. These technologies can facilitate personalized medicine, improve diagnostic accuracy, and support healthcare administration by streamlining workflows and resource allocation. Despite its promising applications, the integration of AI in geriatric care presents several challenges, including issues related to data privacy, algorithmic bias, ethical considerations, and the digital literacy of older adults. Ensuring the inclusivity of AI models by incorporating diverse and representative datasets is essential to avoid disparities in healthcare delivery. Additionally, the role of AI must be carefully regulated to complement, rather than replace, human clinical expertise. This paper provides a comprehensive overview of AI applications in geriatric medicine, discussing its benefits, limitations, and future directions. By leveraging AI responsibly, healthcare systems can improve patient outcomes, reduce hospital readmissions, and promote aging in place. However, addressing existing challenges through interdisciplinary collaboration, policy development, and continued research is crucial to fully realizing AI's potential in elderly care.
1 Dec 2025
Read appraisal →Gerontologist
The AI-aging-enterprise: a political economy of aging and artificial intelligence
The current discourse on artificial intelligence (AI) in gerontology remains mostly on an interventionist level and focused on solving problems faced by individuals, leaving the wider social conditions that shape the relationship between aging and AI out of view. The considerable accumulation of power, particularly for technology development companies, in the development and implementation of AI, however, calls for a deeper and more complex analysis of the relationships between AI, older adults and the "aging enterprise." Building on the classical political economy of aging, and expanding it with concepts from material gerontology, we propose a "political economy of aging and AI" as a conceptual tool to analyze the relationships between (older) individuals, social and political structures, and (technological) materialities. We exemplify how such a political economy perspective enables a more complex consideration of the current discourse on (a) AI solutions, (b) AI ethics, and (c) AI policies. We close by summarizing our understanding of a political economy of aging and AI that centers materialities as the critical-gerontological focus of analysis and by outlining calls for action toward strengthening the power of older adults in the emerging AI-aging-enterprise.
1 Nov 2025
Read appraisal →Am Surg
Enhancing Surgical Education Through Artificial Intelligence in the Era of Digital Surgery
Artificial Intelligence (AI) is transforming surgical education, playing a pivotal role in the era of digital surgery. This review, conducted from January 2023 to December 2024, examines the integration of AI into surgical training, focusing on its benefits and limitations. Thirteen studies were included, with emphasis on AI's impact in General Surgery training. Digital surgery leverages AI-powered tools such as virtual reality (VR) and augmented reality (AR) to create immersive, simulated environments where residents can practice surgical techniques safely and effectively. These platforms improve technical skills, hand-eye coordination, and error reduction, while personalized AI-driven feedback systems enhance learning and skill assessment. Digital surgery also bridges the gap between theoretical knowledge and clinical application, fostering innovative training methodologies. Despite challenges such as financial constraints and ethical concerns, AI within digital surgery emerges as a transformative force, redefining surgical education and equipping trainees with the expertise needed for modern health care.
1 Nov 2025
Read appraisal →J Clin Oncol
Foundation Model for Predicting Prognosis and Adjuvant Therapy Benefit From Digital Pathology in GI Cancers
PURPOSE: Artificial intelligence (AI) holds significant promise for improving cancer diagnosis and treatment. Here, we present a foundation AI model for prognosis prediction on the basis of standard hematoxylin and eosin-stained histopathology slides. METHODS: In this multinational cohort study, we developed AI models to predict prognosis from histopathology images of patients with GI cancers. First, we trained a foundation model using over 130 million patches from 104,876 whole-slide images on the basis of self-supervised learning. Second, we fine-tuned deep learning models for predicting survival outcomes and validated them across seven cohorts, including 1,619 patients with gastric and esophageal cancers and 2,594 patients with colorectal cancer. We further assessed the model for predicting survival benefit from adjuvant chemotherapy. RESULTS: The AI models predicted disease-free survival and disease-specific survival with a concordance index of 0.726-0.797 for gastric cancer and 0.714-0.757 for colorectal cancer in the validation cohorts. The models stratified patients into high-risk and low-risk groups, with 5-year survival rates of 49%-52% versus 76%-92% in gastric cancer and 43%-72% versus 81%-98% in colorectal cancer. In multivariable analysis, the AI risk scores remained an independent prognostic factor after adjusting for clinicopathologic variables. Compared with stage alone, an integrated model consisting of stage and image information improved prognosis prediction across all validation cohorts. Finally, adjuvant chemotherapy was associated with improved survival in the high-risk group but not in the low-risk group (treatment-model interaction P = .01 and .006) for stage II/III gastric and colorectal cancer, respectively. CONCLUSION: The pathology foundation model can accurately predict survival outcomes and complement clinicopathologic factors in GI cancers. Pending prospective validation, it may be used to improve risk stratification and inform personalized adjuvant therapy.
1 Nov 2025
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