Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 20 appraisals
The Tohoku journal of experimental medicine
At-Home Deaths and Regional Factors in Japan
In Japan, at-home deaths are not always popular, yet many people hope to spend their end-of-life at home, which makes this a socio-medical issue. Regional factors are associated with at-home deaths, but their relative importance has not been known. The present study investigated the relationship between at-home deaths and regional factors and ranked their importance at the municipal level in Japan. We used national open data on at-home deaths, population, medical resources, causes of death, economic factors and geographic factors as well as built the prediction model for at-home deaths using Extreme Gradient Boosting (XGBoost) regression. The Shapley Additive Explanations (SHAP) value was applied to rank the importance of regional factors and explain the XGBoost model. As a result, the median prevalence of at-home deaths across municipalities was 12.7%. A higher prevalence of at-home deaths was observed in large metropolitan areas such as Tokyo, Osaka, and Nagoya. SHAP values revealed that latitude was the most important factor in rank, followed by the number of clinics providing end-of-life care, longitude, population density, and population change rate. In conclusion, geographic and population-related factors were ranked highly among regional factors associated with at-home deaths. These findings may be informative for community planning to realize at-home deaths nationwide.
7 July 2026
Read appraisal →Brain research
BDNF relates to prefrontal cortex activity in the context of physical exercise
Exercise has been shown to support brain health, cognitive function, and increase levels of brain-derived neurotrophic factor (BDNF). While BDNF is known to support the central nervous system through improved brain metabolism, vasculature, neurotransmission and synaptic plasticity, the association between exercise-induced changes in BDNF concentrations and exercise-related cognitive improvements is still unclear. This study investigated the relationship between exercise-induced changes in plasma BDNF (pBDNF) and serum BDNF (sBDNF), and haemodynamic indicators of prefrontal cortex function in sedentary adults. Participants (n = 23, female = 7) were randomized into intervention (12-week cycling programme) and control groups (no intervention). Participants completed V̇O2max tests to assess changes in fitness. pBDNF and sBDNF were measured pre- and post-V̇O2max testing, and a battery of executive function and memory tasks were also conducted, alongside functional near-infrared spectroscopy (fNIRS) to assess haemodynamic changes in the prefrontal cortex activity. Changes were assessed using the correlation-based signal improvement (CBSI) method. Results indicated that participants in the exercise intervention group did not show increased levels of resting-state s/pBDNF levels at the end of the intervention; however, they did exhibit a significant exercise-induced increase in sBDNF at week 12. This increase was correlated with changes in V̇O2max. Higher pBDNF levels and exercise-induced sBDNF were associated with a decrease in CBSI values in the frontopolar, dorsolateral and orbitofrontal prefrontal cortex during attention and inhibition tasks, but not during the memory task. These results suggest that increasing physical fitness can enhance BDNF transcription in response to acute bouts of exercise. This might, in turn, play a part in the modulation of neural function during executive tasks after acute exercise.
15 June 2026
Read appraisal →Physics in medicine and biology
Scan-wise generalized PET denoising with contrastive adversarial learning
Objective.Deep learning has significantly advanced low-count positron emission tomography (PET) denoising. However, models trained on specific distributions often yield biased outputs when applied to scans with different activity distributions caused by anatomical and physiological variations (distribution shifts). Existing methods fail to generalize well across these scan-wise variations. Our goal is to formulate PET denoising as a scan-wise domain generalization (DG) problem to mitigate these variations and achieve robust, unbiased denoising for unseen scans.Approach.We propose a contrastive adversarial DG (CADG) framework to learn scan-invariant features efficiently. We leverage the property that multiple low-count noise realizations can be generated from a single raw list-mode PET scan to form a scan-wise domain distribution. Unlike conventional adversarial training with cross-entropy (CE) loss, we propose a contrastive adversarial framework that minimizes the mutual information between feature and scan-wise domains. Considering that each subject can have multiple scans in longitudinal studies, we further propose an ordered and memory-queued contrastive adversarial framework. This method efficiently includes realizations from the same scan as positive pairs, different scans of the same subject as pseudo-positive pairs, and different subjects as negative pairs in a memory batch. We explicitly exploit their ordered relationship as prior knowledge using a novel noisy-robust multi-positive ordinal contrastive loss.Main results.We systematically validated the effectiveness of our methods using 1920 noise realizations derived from 80 subjects with 192 longitudinal scans of MK-6240 tau PET data. The proposed CADG approach demonstrated superior denoising performance compared to CE-based adversarial methods and standard baselines. The ordered contrastive loss successfully improved the peak signal-to-noise ratio and structural similarity index consistently while reducing bias and standard deviation in Alzheimer-related regions and the whole brain.Significance.To our knowledge, this is the first attempt to alleviate the performance degradation in cross-scan denoising from the perspective of DG. Our study is also a pioneering work in utilizing longitudinal scans as pseudo-positives within an ordered contrastive learning scheme to exploit fine-grained relationships for robust clinical PET imaging applications.
9 June 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 →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 →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 →Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Effects of Semaglutide on Cognitive Function in People With HIV: A Randomized, Controlled Trial
BACKGROUND: People with human immunodeficiency virus (HIV, PWH) are at higher risk for visceral adiposity, enhanced inflammation, and cognitive decline than controls who do not have HIV. We previously demonstrated that PWH with lipohypertrophy had a decrease in weight, visceral adipose tissue, and several inflammatory markers after receiving semaglutide, a glucagon-like peptide-1 receptor agonist. Our aim was to investigate the effect of semaglutide on cognitive function in PWH and the possible mediation of this effect by changes in adiposity or inflammation. METHODS: In this randomized, double-blind, placebo-controlled phase 2b clinical trial, PWH on antiretroviral therapy were randomized 1:1 to receive 32 weeks of subcutaneous semaglutide or placebo. The primary outcome was the change in cognitive function at 32 weeks. Secondary measures included changes in body composition and inflammatory markers. Causal mediation analysis assessed semaglutide's direct and indirect effects on Cognivue scores through changes in adiposity and inflammation. RESULTS: 108 participants were included (54 per arm); 65% were non-White, 40% were female, and median age was 53 years. Compared with placebo, PWH on semaglutide significantly increased visuospatial, naming/language, and delayed recall scores at 32 weeks (P = .01, .05, and .04, respectively). After adjusting for sex and absolute CD4 count, only visuospatial score remained statistically significant (P = .05). Semaglutide's total natural direct effect maintained a positive effect on the visuospatial score while accounting for potential changes in high-sensitivity C-reactive protein and soluble CD163 levels (P = .04). CONCLUSIONS: Semaglutide may have a beneficial impact on visuospatial cognitive function in PWH through its effect on inflammation. Clinical Trials Registration . NCT04019197.
22 May 2026
Read appraisal →Neurosurgical review
Short-and long-term responsiveness of deep brain stimulation on motor and cognitive outcomes in GBA vs. Non-GBA parkinson's disease: a systematic review and meta-analysis of observational studies
Deep brain stimulation (DBS) is an established therapy for motor complications in Parkinson's disease (PD). Patients carrying glucocerebrosidase (GBA) mutations exhibit distinct disease trajectories, raising questions regarding potential differences in clinical outcomes following DBS compared with non-carriers. To evaluate short- and long-term motor, medication, and cognitive outcomes following DBS in patients with GBA-PD compared with non-GBA PD. We conducted a systematic review and meta-analysis of studies reporting clinical outcomes in PD patients with and without GBA mutations who underwent DBS and had a minimum follow-up of one year. Random-effects inverse variance models were applied, with subgroup analyses according to GBA status. DBS was associated with significant improvements in motor function in the off-medication state and sustained reductions in levodopa equivalent daily dose in both GBA carriers and non-carriers, with no significant between-group differences. Cognitive performance declined over long-term follow-up in both groups. At five years, greater cognitive decline, assessed using the Mattis Dementia Rating Scale, was observed among GBA-PD mutation carriers compared with non-carriers. Motor improvement and medication reduction following DBS were comparable between PD patients with and without GBA mutations. Over long-term follow-up, greater cognitive decline was observed among GBA-PD carriers.
21 May 2026
Read appraisal →The British journal of general practice : the journal of the Royal College of General Practitioners
Are non-injectable anticipatory medications a viable end-of-life intervention in the community? A systematic review and narrative synthesis.
BACKGROUND: The anticipatory prescribing of injectable medications for symptom management at the end-of-life is an established practice in the UK. Transmucosal and inhaled routes of administration offer alternatives to injections, when patients are no longer able to take medications orally. Their evidence base in the community setting has not been established. AIM: To systematically review and synthesise the published, peer-reviewed evidence regarding the clinical practice and effectiveness, including patients', family caregivers' and healthcare professionals' views, of transmucosal and inhaled anticipatory medications for symptom management at the end-of-life in the community. METHOD: Ten literature databases were searched from inception to February 2025, using terms for non-injectable anticipatory medications, or kits, combined with end-of-life terms. Two journals were hand-searched from 2015 to 2025. References and citations of included papers were searched. Gough's Weight of Evidence Framework was used to appraise the included studies and inform the narrative synthesis. RESULTS: Fifteen papers were included, reporting the use of transmucosal routes, of which nine used a standardised medication kit. The limited evidence of clinical effectiveness, and the views of patients, family caregivers, and healthcare professionals, suggested that transmucosal medications were effective at relieving patients' symptoms and enabling patients to die at home. CONCLUSION: The evidence regarding transmucosal anticipatory medications in the community suggests that they are acceptable, clinically effective, and contribute to reducing unplanned hospital attendances, but is limited and should be treated with caution. Further research is needed to assess whether they offer viable alternatives to injectable anticipatory medications in the community.
16 May 2026
Read appraisal →Annals of human biology
Machine learning combined with WGCNA reveals potential calcium uptake-associated biomarkers in Parkinson's disease
BACKGROUND: Parkinson's disease (PD) is the second most common neurodegenerative disease, characterised by progressive loss of dopaminergic neurons in the substantia nigra pars compacta. Although previous studies have suggested a correlation between calcium signal disruption and the development of PD, the role of genes associated with store-operated calcium entry (SOCE) in PD remains unclear. AIM: To combine machine learning methods with Weighted Gene Co-expression Network Analysis (WGCNA) to systematically screen and identify SOCE-associated candidate genes and evaluate their potential for assisting in discrimination. SUBJECTS AND METHODS: This study is based on the GSE6613 dataset, using ssGSEA and Weighted Gene Co-expression Network Analysis (WGCNA) to screen for co-expression modules related to SOCE, and combining LASSO, SVM-RFE, and random forest algorithm to identify feature genes. Subsequently, validation was conducted in GSE20163 and GSE22491, and in vitro experiments were carried out in 6-OHDA and MPP+ models of dopaminergic neurons derived from LUHMES. RESULTS: A total of 5 SOCE-associated feature genes (LPCAT3, CLCNKB, TXLNA, etc.) were identified, and their combined model showed moderate discriminative ability in external validation. Immune analysis showed immune dysfunction in PD patients, and in vitro experiments also observed consistent expression trends with patient samples. CONCLUSIONS: In summary, this study identified potential PD feature genes associated with SOCE, providing clues for further exploration of the molecular mechanisms of SOCE-associated calcium signalling in PD.
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 →Journal of ethnopharmacology
Advances in the pharmacology and mechanism of anti-skin aging in Tonifying Traditional Chinese Medicines
ETHNOPHARMACOLOGICAL RELEVANCE: According to the Traditional Chinese Medicine (TCM) tenet that "internal imbalances manifest externally," skin aging reflects deficiencies in qi, blood, and organ systems-particularly the liver and kidneys. Tonifying Traditional Chinese Medicines (TTCM) address these root causes, nourishing the skin and delaying aging through internal regulation, as documented in classical texts including the Shennong Ben Cao Jing and the Compendium of Materia Medica. AIM OF THE STUDY: This article systematically reviews the anti-skin-aging mechanisms of TTCM by integrating traditional applications with modern pharmacological evidence. It explores their multi-component, multi-target, and multi-pathway actions, evaluates current experimental models, and investigates the emerging role of artificial intelligence (AI) in advancing TTCM research. MATERIALS AND METHODS: We systematically searched PubMed, China National Knowledge Infrastructure (CNKI), Web of Science and Scopus for articles published from January 2015 to August 2025 with keywords such as "skin aging", "anti-aging" and the Latin names of TTCM (e.g. Panax ginseng, Astragalus membranaceus). Cellular, animal and clinical studies were analyzed for active constituents, molecular targets, ageing biomarkers and related signalling pathways. RESULTS: TTCM demonstrates anti-skin-aging effects through systemic regulation of organ function and qi-blood-yin-yang balance. At the molecular level, TTCM attenuates senescence through antioxidative, anti-inflammatory, extracellular matrix (ECM) stabilizing mechanisms, mediated by modulation of multiple pathways, such as Mitogen-Activated Protein Kinase (MAPK), Tumor Protein p53 (p53) and Nuclear factor erythroid 2-related factor 2 (Nrf2), exemplifying a "holistic regulation-targeted intervention" therapeutic paradigm. CONCLUSIONS: TTCM exerts anti-skin-aging effects through multi-component, multi-target and multi-pathway synergism. We strongly recommend the further integration of TTCM with modern medicine and in-depth mechanistic studies to promote translational applications.
12 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 aging
Machine Learning-Derived Cardiovascular Aging Phenotypes From Cardiac Function and Stroke Risk in the UK Biobank: Cohort Study
BACKGROUND: Cardiovascular magnetic resonance (CMR) is widely used across various cardiac conditions and systematically assesses cardiac anatomical structures and functional dynamics. Machine learning (ML) can accurately predict outcomes and understand the inherent features of clinical data. OBJECTIVE: This study aimed to derive CMR phenotypes related to cardiovascular aging, investigate the relationship between these phenotypes and stroke risk, and relearn these phenotypes using supervised ML. METHODS: We enrolled 36,467 participants without stroke and extracted CMR parameters from the UK Biobank, with follow-up data extending until September 30, 2023. Using the generative topographic mapping technique, we identified latent grid nodes among participants and then derived phenotypes through agglomerative hierarchical clustering. We used supervised ML models to predict cardiac function phenotypes and used Cox proportional hazards models to assess the association between these phenotypes and long-term stroke risk. RESULTS: We enrolled 36,467 participants in the study. The mean age was 54.9 (SD 7.5) years, with 17,442 (47.8%) male participants. During a mean follow-up time of 14.7 (SD 1.1) years, 500 (1.4%) participants developed stroke and 664 (1.8%) participants died, respectively. After generative topographic mapping modeling, we identified 2 distinct phenotypes: phenotype 1, characterized by adverse cardiac function and an accumulation of cardiovascular risk factors, reflecting cardiovascular aging; and phenotype 2, associated with a lower risk of stroke (hazard ratio 0.695, 95% CI 0.559-0.864; P=.001), which remained significant after accounting for competing mortality (hazard ratio 0.578, 95% CI 0.484-0.691; P<.001). We selected the random forest model as the optimal model for the phenotypes, demonstrating high accuracy (area under the curve 0.914, 95% CI 0.911-0.918 for training and 0.867, 95% CI 0.858-0.876 for validation) and calibration ability (Brier score 0.111, 95% CI 0.109-0.113 for training and 0.132, 95% CI 0.127-0.137 for validation). CONCLUSIONS: By integrating unsupervised and supervised ML methods, we identified cardiovascular aging-related phenotypes that demonstrate robust predictive ability for incident stroke, which may have the potential to improve preventive and therapeutic strategies for high-risk populations.
2 May 2026
Read appraisal →BMJ open
Cost-effectiveness of osteoporotic fracture risk assessment in people with intellectual disabilities: a UK NHS modelling study
OBJECTIVES: We compared the cost-effectiveness of alternative fracture risk assessment strategies for people with intellectual disabilities (ID) aged ≥40 years from a UK National Health Services perspective over a lifetime horizon. DESIGN: Cost-effectiveness analysis using a lifetime decision-analytical model. SETTING: UK primary care, with data from literature and national databases. PARTICIPANTS: People with ID. INTERVENTIONS: Three strategies were assessed: (S1) Risk assessment using the UK QFracture score; (S2) use of IDFracture (a fracture risk prediction tool specifically developed for adults with ID); and (S3) conducting a one-time dual-energy X-ray absorptiometry (DXA) scan in all. S1 and S2 were followed by DXA scan for those at risk. At-risk individuals received treatment according to UK practice (bisphosphonates plus vitamin D and calcium for osteoporosis, and vitamin D and calcium alone for osteopenia). PRIMARY OUTCOME MEASURES: Direct healthcare costs and quality-adjusted life years (QALYs), and incremental cost-effectiveness ratio (ICER). RESULTS: In the base case, S2 (ICER: -£2568/QALY) was dominant (ie, less costly and more effective) and S3 (ICER: £1678/QALY) was cost-effective relative to S1 for major osteoporotic fracture (MOF). For hip fracture, S2 (ICER: £32 116/QALY) and S3 (ICER: £49 536/QALY) were not cost-effective relative to S1 under the National Institute for Health and Care Excellence-recommended cost-effectiveness thresholds. Findings from the sensitivity analyses were predominantly consistent with the base-case results. Subgroup analyses showed that age-specific and gender-specific strategies could be used. CONCLUSION: For people with ID aged ≥40 years, a proactive approach to risk assessment for MOF is not only clinically beneficial, but also cost-effective.
20 Apr 2026
Read appraisal →Psychonomic bulletin & review
Speaker effects in language comprehension: An integrative model of language and speaker processing
The identity of a speaker influences language comprehension through modulating perception and expectation. This review explores speaker effects and proposes an integrative model of language and speaker processing that integrates distinct mechanistic perspectives. We argue that speaker effects arise from the interplay between bottom-up perception-based processes, driven by acoustic-episodic memory, and top-down expectation-based processes, driven by a speaker model. We show that language and speaker processing are functionally integrated through multi-level probabilistic processing: prior beliefs about a speaker modulate language processing at the phonetic, lexical, and semantic levels, while the unfolding speech and message continuously update the speaker model, refining broad demographic priors into precise individualized representations. Within this framework, we distinguish between speaker-idiosyncrasy effects arising from familiarity with an individual and speaker-demographics effects arising from social group expectations. We discuss how speaker effects serve as indices for assessing language development and social cognition, and we encourage future research to extend these findings to the emerging domain of artificial intelligence (AI) speakers, as AI agents represent a new class of social interlocutors that are transforming the way we engage in communication.
17 Apr 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 →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 →Cardiovasc Diabetol
Association of estimated glucose disposition rate with aging acceleration and mortality risk in individuals with cardiovascular-kidney-metabolic syndrome: evidence from two large national population-based studies
OBJECTIVE: This study investigated the relationship between estimated glucose disposal rate (eGDR), aging acceleration (AgeAccel), and mortality in adults diagnosed with cardiovascular-kidney-metabolic (CKM) stages 1 to 4. METHODS: The study utilized data from 4,826 adults with CKM syndrome stages 1 to 4, collected from the National Health and Nutrition Examination Survey (NHANES) conducted during the 2005-2010 survey cycles. The assessment of AgeAccel was performed using two complementary measures: phenotypic AgeAccel (PhenoAgeAccel) and biological AgeAccel (BioAgeAccel). Survey-weighted logistic regression and Cox proportional hazards models were used to assess the associations of eGDR with AgeAccel and mortality risk, respectively. To assess the prognostic value of eGDR for mortality risk, we implemented a suite of nine distinct machine learning models. Additionally, a nomogram was developed to enhance the clinical applicability of our findings. Furthermore, we performed causal mediation analysis to quantify the proportion of the total effect of eGDR on mortality that was mediated through AgeAccel. To ensure the robustness of the results, we replicated our primary analyses using data from the nationally representative China Health and Retirement Longitudinal Study (CHARLS) cohort. RESULTS: Our analysis included 4,826 NHANES participants, among whom we documented 831 all-cause mortality events and 208 cardiovascular disease (CVD)-specific deaths during follow-up. In multivariable-adjusted Cox regression models, each unit increase in eGDR was significantly associated with a 10% reduction in all-cause mortality risk (Hazard ratio [HR] = 0.90, 95% Confidence interval [CI] 0.86-0.93) and a 13% decrease in CVD mortality risk (HR = 0.87, 95% CI 0.81-0.93). Additionally, eGDR showed a negative association with AgeAccel, including both BioAgeAccel (odds ratio [OR] = 0.85, 95% CI 0.82-0.87) and PhenoAgeAccel (OR = 0.78, 95% CI 0.75-0.80). For predicting all-cause mortality from eGDR, the K-Nearest Neighbors (KNN) showed superior discrimination (Area Under the Curve [AUC]: 0.926), exceeding the performance of other machine learning algorithms in a comparative evaluation. Mediation analysis revealed that the protective effect of higher eGDR was partially explained by slower PhenoAgeAccel, with mediation effects accounting for 23.53% and 15.73% of the total impact on all-cause and CVD mortality, respectively. CONCLUSIONS: In the CKM population, lower eGDR levels may be associated with both AgeAccel and an increased risk of mortality, with AgeAccel potentially mediating the relationship between eGDR and mortality. These findings suggested that eGDR could serve as a potential predictor and intervention target for delaying aging and reducing mortality risk.
1 Oct 2025
Read appraisal →