Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 24 appraisals
Artificial intelligence in medicine
Artificial intelligence language models for medical text analysis: A systematic review
Medical text records serve as essential repositories of patient information, providing a foundation for informed clinical decision-making, accurate diagnosis, reliable prognosis, and effective treatment planning. Recent advancements in Artificial Intelligence (AI), particularly in Natural Language Processing (NLP) and Machine Learning (ML), have positioned AI-driven language models as powerful tools for analyzing, classifying, and generating medical textual data. In this systematic literature review, an initial search retrieved 548 records published between 1 January 2000 and 1 July 2024. After rigorous screening based on predefined inclusion and exclusion criteria, 22 original research articles were included. The review highlights substantial progress in applying advanced architectures such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT) to medical text processing tasks. These models consistently outperform conventional NLP and ML approaches, achieving superior results in disease classification, automated clinical documentation, and predictive analytics. However, critical challenges persist, including the limited availability of clinically validated datasets, variability in data preprocessing protocols, insufficient external validation, and the lack of interpretable AI frameworks, all of which collectively hinder clinical trust and large-scale adoption. Future research should prioritize the development of hybrid AI systems that integrate multimodal data sources (text, imaging, and structured records), incorporate explainable AI mechanisms, and adhere to standardized reporting frameworks. Addressing these methodological gaps will be pivotal in enhancing the reliability, clinical applicability, and impact of AI language models, thereby advancing evidence-based medicine, personalized treatment strategies, and overall patient care.
2 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 →BMJ health & care informatics
Benchmarking large language models for de-identification of electronic health record notes
OBJECTIVES: The rapid evolution of large language models (LLMs) and their growing application in clinical text processing have created an urgent need for reliable de-identification mechanisms. While LLMs show promise in identifying sensitive health information (SHI), their capabilities require rigorous evaluation. This study aims to conduct a comprehensive benchmarking analysis of various LLM-based, traditional rule-based and hybrid de-identification methods. METHODS: Our benchmark analysis used five datasets (i2b2-2006, MIMIC-2008, i2b2-2014, i2b2-2016 and OpenDeID v1) from different countries. We developed three baseline and eight LLM-based models. The experimental setup encompassed nine different settings using various combinations of training and testing sets to assess model robustness and cross-dataset performance. RESULTS: In the baseline models, the approach trained on the combined corpus of all five datasets (setting 3) significantly outperformed the other settings, achieving a strict F1 micro-average score of 0.8172. Regarding LLM-based models, the supervised fine-tuning approach using the same combined configuration (setting 9) achieved the highest performance with a strict F1 score of 0.9447. DISCUSSION: The harmonisation of corpora ensured standardised data formatting and SHI management across five diverse datasets, highlighting the necessity for uniform categorisation to enhance the reliability of de-identification results. CONCLUSIONS: Our findings indicate that while fine-tuned LLMs offer superior accuracy, the observed performance variability across heterogeneous electronic health record sources poses significant technical challenges. Real-world implementation must address these inconsistencies to overcome the ethical and technical hurdles associated with deploying LLMs for handling sensitive health data.
23 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 →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 →The pharmacogenomics journal
Development and validation of an open data model for pharmacogenetics to enable semantic interoperability in clinical practice
Pharmacogenetics uses genetic testing to improve the safety and effectiveness of prescribed medicines, yet implementation at scale remains limited due to the absence of interoperable health IT solutions that integrate results into prescribing workflows. This study aimed to develop and validate open data standards for pharmacogenetic results to enable interoperability across healthcare systems. A baseline data model was constructed using the open standard openEHR by synthesising literature, genomic sequencing outputs, and international data specifications, and refined through iterative workshops with the Global Alliance for Genomics and Health. The model underwent two rounds of structured peer review involving 24 experts from 10 countries. Mapping to HL7 FHIR was evaluated using both manual and automated approaches, including the FHIR-Connect tool. The resulting standardised pharmacogenetic data model separates test results from therapeutic implications and incorporates recognised terminologies such as SNOMED CT and HGNC. It achieved international consensus and is published on the openEHR Clinical Knowledge Manager platform. Mapping to HL7 FHIR demonstrated bidirectional information flow within healthcare systems, with automated mapping enabling scalable and reusable transformations. This work provides a framework for storing and exchanging pharmacogenetic test results, supporting semantic harmonisation, interoperability, and integration with clinical decision support systems. Open data standards for pharmacogenetic test results therefore offer a foundation for scalable implementation of pharmacogenetics in routine clinical practice.
14 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 →JMIR medical informatics
Multimodal Prediction of Renal Tumor Malignancy From Radiology Reports and Structured Electronic Health Records: Retrospective Cohort Study
BACKGROUND: Accurate preoperative prediction of renal tumor malignancy is critical for guiding decisions and reducing overtreatment, as a substantial proportion of renal masses prove benign. Although radiology assessments and structured electronic health record (EHR) data are routinely used, many tumor-specific descriptors remain embedded in free-text radiology reports and are underused due to extraction challenges. OBJECTIVE: This study aimed to develop and evaluate a multimodal pipeline that integrates structured EHR variables with natural language processing features from computed tomography (CT) radiology reports, including large language model (LLM)-extracted abnormality characteristics and transformer-based report embeddings, to improve malignancy prediction. METHODS: We conducted a retrospective cohort study using University of Florida Health Integrated Data Repository Observational Medical Outcomes Partnership-mapped EHR data from December 2011 to August 2024. Adults with renal tumors were included if they had longitudinal diagnostic documentation consistent with a renal mass and at least 1 preoperative renal CT report; final benign or malignant status served as the outcome. Structured features included demographics, comorbidities, medications, vital signs, and laboratory measurements. From the recent preindex CT report, an on-premises LLM isolated kidney-specific findings and extracted abnormality characteristics. Four locally deployed LLMs were evaluated against manual annotations of 500 reports. Kidney-specific text was encoded using pretrained biomedical transformer models, including radiology Bidirectional Encoder Representations from Transformers (BERT) variants. We evaluated unimodal baselines and multimodal early, middle, and late fusion strategies. Model development used 5-fold cross-validation within the 80% training partition; each fold-specific model was evaluated on the same independent 20% held-out test set, with performance reported as mean and SD across the 5 held-out test evaluations. The primary metric was area under the receiver operating characteristic curve (AUC). RESULTS: The final cohort included 967 patients (n=712, 73.6% malignant). In extraction evaluation, Qwen2.5-32B achieved 88.3% overall accuracy with a 100% extraction success rate and was selected for downstream feature generation. Among unimodal models, the structured clinical variable model achieved an AUC of 0.758 (SD 0.012), kidney-specific text with radiology BERT achieved an AUC of 0.746 (SD 0.058), and abnormality characteristics alone achieved an AUC of 0.716 (SD 0.015). Multimodal fusion models achieved higher descriptive performance than unimodal models. Early fusion achieved the highest AUC (mean 0.813, SD 0.008), and F1-score (mean 0.809, SD 0.030), while late fusion achieved an AUC of 0.805 (SD 0.016). Ablation and interpretability analyses suggested complementary predictive information from structured clinical variables and kidney-specific text embeddings. CONCLUSIONS: Integrating unstructured radiology report text with structured EHR variables achieved higher mean predictive performance than unimodal approaches in descriptive comparisons. Multimodal fusion, particularly early fusion incorporating radiology BERT-derived kidney-specific text embeddings, achieved the strongest discrimination, suggesting potential value of natural language processing-enabled multimodal EHR pipelines for informing preoperative risk stratification.
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 →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
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 →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
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
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 →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 →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 →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 →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 →PloS one
Development and evaluation of an ontology for non-invasive respiratory support in acute care
Managing patients with respiratory failure increasingly involves non-invasive respiratory support (NIRS) strategies to support respiration, often preventing the need for invasive mechanical ventilation. However, despite the rapidly expanding use of NIRS, there remains a significant challenge to its optimal use across all medical circumstances. It lacks a unified ontological structure, complicating guidance on NIRS modalities across healthcare systems. In this study, we introduced NIRS ontology to support knowledge representation in acute care settings by providing a unified framework that enhances data clarity and interoperability, laying the groundwork for future clinical decision-making research. We developed NIRS ontology using the Web Ontology Language (OWL) and Protégé to organize clinical concepts and relationships. To enable rule-based clinical reasoning beyond hierarchical structures, we added Semantic Web Rule Language (SWRL) rules. We evaluated logical reasoning using a sample of 6 patient scenarios and used SPARQL queries to retrieve and test targeted inferences. The ontology has 145 classes, 11 object properties, and 18 data properties across 949 axioms that establish concept relationships. To standardize clinical concepts, we added 392 annotations, including descriptive definitions based on controlled vocabularies. SPARQL queries successfully validated all test cases (rules) by retrieving appropriate patients' outcomes: for instance, a patient treated with HFNC (high-flow nasal cannula) for 2 hours due to acute respiratory failure may avoid endotracheal intubation. This NIRS ontology formally represents domain-specific concepts, including ventilation modalities, patient characteristics, therapy parameters, and outcomes. SPARQL query evaluations across clinical scenarios confirmed the ontology's ability to support rule‑based reasoning and therapy recommendations, providing a foundation for consistent documentation practices, integration into clinical data models, and advanced analysis of NIRS outcomes. In conclusion, this proof-of-concept NIRS ontology demonstrates how clinical concepts can be formally represented and queried, offering a foundation for future validation, EHR integration, and evidence-based refinement of NIRS practices.
11 May 2026
Read appraisal →Artificial intelligence in medicine
Size doesn't matter: Assessing the trustworthiness of large language models in medical contexts: A focus on epidural information retrieval.
BACKGROUND: Since the release of ChatGPT, numerous LLMs have emerged, providing easy access to information without the need for technical expertise. However, relying on these systems can influence important life decisions, such as the choice to use epidural analgesia during childbirth. Epidural analgesia is widely regarded as the "gold standard" for pain relief during childbirth. However, limited access to anaesthesiologists and gaps in knowledge may prompt individuals to seek information from unverified sources, including AI systems. Misinformation in this area can discourage the use of effective analgesia, highlighting the need to assess the accuracy of LLM-generated content. OBJECTIVE: To evaluate the reliability of LLM-generated information regarding epidural analgesia. METHODS: We posed 10 standardized questions about epidural analgesia to 12 LLMs, each question reformulated 10 times in both Spanish and English, resulting in 2400 responses. Two anaesthesiologists were involved in the assessment process. One expert performed the initial ratings, while the second independently verified the evaluations assessing the outputs using an extended SERVQUAL framework. RESULTS: ChatGPT performed best, followed by Gemini 2. Medium-sized models, such as Phi-3 and OpenChat, outperformed several larger models like Llama-2 or Llama-3, challenging the notion that "bigger is better" and offering potential advantages in low-resource settings (e.g., Phi-3 outperformed Llama-2 with an average increase of 81% across all metrics). Specialized models did not show superior performance. Except for ChatGPT, English responses were generally more reliable than Spanish, with some Spanish outputs incoherent. ChatGPT also exhibited the least variability between responses. CONCLUSIONS: Despite promising performance, LLMs display limitations in medical contexts. Collaboration between national and international medical societies is crucial to develop evidence-based resources to guide LLM training and improve information trustworthiness.
10 May 2026
Read appraisal →Journal of the American Medical Informatics Association : JAMIA
Evaluating the potential of fast healthcare interoperability resources for clinical registry data submission.
OBJECTIVE: We conducted the Clinical Registry Extraction and Data Submission (CREDS) project to evaluate the readiness of HL7 Fast Healthcare Interoperability Resources (FHIR) for provisioning data from health information systems for the American College of Cardiology Cardiac Catheterization Percutaneous Coronary Intervention (CathPCI) Registry. MATERIALS AND METHODS: The CREDS project had 3 workstreams: (1) evaluation of the readiness of clinical documentation for data transforms, (2) modeling of a FHIR-based clinical workflow for registry data submission, and (3) development and demonstration of a CREDS FHIR implementation for registry data submission. RESULTS: Of the 344 data concepts comprising the CathPCI Registry, only 111 (32%) were sufficiently discrete to be listed in the CathPCI Data Dictionary with a terminology mapping. Cardiologist informaticians identified an additional 42 concepts suitable for provisioning via a FHIR payload. The resulting notional workflow combined FHIR-based data assembly with manual chart abstraction of compound, summative, and complex clinical concepts. A CathPCI FHIR StructureDefinition artifact was authored, incorporated into a CREDS FHIR Implementation Guide, and balloted to Standard for Trial Use status. DISCUSSION: CREDS demonstrated both potential and limitations for using FHIR for registry data submission. The largest technical impediment was the volume of code (>11 000 lines) for the FHIR StructureDefinition. Lack of regularized clinical vocabularies, reliance of registries on complex clinical concepts, and absence of FHIR infrastructure must be overcome before CREDS can be used at scale. CONCLUSION: CREDS demonstrated proof-of-concept FHIR-based provisioning of clinical data for registry submission. All artifacts are open source to inform others with similar interests.
9 May 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 →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
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