ReviewJournal of the American Geriatrics Society2026
Clin-STAR Corner: Practice Changing Advances at the Interface of Artificial Intelligence/Machine Learning and Geriatrics.
Review 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. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Artificial intelligence (AI) methods, including machine learning (ML), are transforming healthcare by enabling personalized interventions that integrate multimodal data to support rehabilitation, preventive care, and remote monitoring. Despite their broad potential, older adults remain underrepresented in model development, raising concerns about bias and limited generalizability. As AI/ML adoption expands, it is essential to critically appraise emerging tools to ensure ethical, equitable, person-centered implementation in aging populations and long-term randomized evaluations. A structured MEDLINE search identified randomized controlled trials (RCTs) published between January 2023 and December 2025 that evaluated AI/ML-based interventions in adults ≥ 65 years. Of 31 records identified, 19 underwent abstract screening, seven underwent full-text assessment, and four articles were identified as meeting all criteria for inclusion, which focused on scalability, clinical impact, and methodological rigor. Across postoperative rehabilitation and preventive care, AI/ML interventions demonstrated meaningful clinical benefits. Transformative applications of AI/ML described robotic systems quantifying weight-bearing, algorithm-guided paired rehabilitation interventions, paired exercises in geriatric hip fracture rehabilitation, smartphone platforms delivering continuously personalized exercise programs, and conversational chatbots providing tailored vaccine counseling. The included RCTs show that AI-driven interventions can enhance physical recovery, support psychosocial well-being, and improve uptake of preventive measures compared with conventional approaches. Key considerations for future implementation include digital health literacy, long-term follow-up, and potential bias arising from healthier or more motivated study populations. As evidence grows, geriatric innovation must prioritize safety, ethics, and equitable access to ensure that technological precision enhances, rather than replaces, person-centered care.
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What OpenQuestion 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 OpenQuestion graph.