ArticleThe Gerontologist2026
Multidisciplinary perspectives on artificial intelligence in aging research and education: evolving uses, ethics, and equity considerations in gerontology.
Article in The Gerontologist, 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.
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8 authors.
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Abstract
Artificial intelligence (AI) models and applications are proliferating rapidly throughout gerontological research and education. Machine learning has catapulted gerontological research in diagnosing and treating age-related health conditions. Students and educators have new tools for customized learning and innovation. Yet many of these developments come with persistent challenges, including bias, inaccuracy, and data security. As in other fields, engagement with AI models in gerontology is often siloed within disciplines. Exploring common opportunities and challenges in this space requires collaboration and conversations across disciplines. To fill this gap, the Gerontological Society of America (GSA)'s Public Policy Advisory Panel convened a multidisciplinary panel discussion of experts from the six GSA member groups and three advisory panels in November 2024 to discuss how AI is shaping various disciplines, and what ethical issues exist within or across disciplines. Several common themes emerged across disciplines: (1) human interaction remains critical to offset AI limitations in human experience, abstract reasoning, creativity, and bias; (2) AI provides opportunities for customized support across disciplines for older adults, care partners, practitioners, researchers, and students; (3) ongoing training is essential to navigate this rapidly evolving landscape; and (4) cross-disciplinary collaboration is needed to address overlapping challenges, limitations, and risks concerning AI.
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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.