ArticleJournal of the American Geriatrics Society2026
Use of Conventional Artificial Intelligence Methods in the Identification of Frailty: A Scoping Review.
Article in Journal of the American Geriatrics Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- From frailty-driven to frailty-informed care in the age of wearable AI.Communications medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
21 authors.
Funding
Abstract
backgroundEarly identification and management of frailty are crucial, yet its detection in early stages remains difficult for clinicians. Artificial intelligence (AI) has emerged as a promising tool in healthcare. However, the absence of a standard frailty definition and diversity of AI methods create a need for a comprehensive review. This study examines the clinical tools and conceptual frameworks used as reference standards in training AI algorithms for frailty identification and management, describes current AI methods, and explores the engagement of knowledge users in developing and evaluating these technologies.
methodsA scoping review was conducted following the Arksey and O'Malley framework, enhanced by Levac et al. and the Joanna Briggs Institute. Eight academic databases-Medline, Embase, PsycInfo, Cumulative Index to Nursing and Allied Health Literature, Ageline, Web of Science, Scopus, and Institute of Electrical and Electronics Engineers Xplore-and one gray literature source-ProQuest Dissertations & Theses Global-were searched. Abstracts and full-text screening and data charting were performed in duplicate. Results were summarized through text and graphical representations.
resultsThe review included 33 publications, predominantly emerging after 2020. Twenty-three different AI techniques were presented, with standard modeling approaches such as logistic regression and decision trees being most common. Among the 21 distinct reference standards used to train AI models, the Physical Frailty Phenotype was cited most frequently (n = 7). Most AI methods (n = 27) prioritized frailty identification, one addressed frailty management, and five focused on both. None of the papers engaged knowledge users in defining or validating AI tools, and only three studies explored algorithmic biases that could lead to inequities.
conclusionsLike the broader frailty literature, emerging AI tools lack a consistent definition of frailty, leading to design and implementation inconsistencies. The absence of knowledge user involvement may further limit the clinical relevance and equity of these technologies.
trial registrationOSF Registries [https://doi.org/10.17605/OSF.IO/T54G8].
Indexed as
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.