Evidence map›Paper›PMID 41410517›Full record

ArticleThe Gerontologist2026

Multidisciplinary perspectives on artificial intelligence in aging research and education: evolving uses, ethics, and equity considerations in gerontology.

Angela K Perone, Peter M Abadir, Nancy Berlinger, James R Carey, M Aaron Guest, Zachary J Hass, Abigail T Stephan, Bo Xie

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Angela K PeroneSchool of Social Welfare, University of California, Berkeley, California, United States.ORCID 0000-0002-3307-9662
Peter M AbadirSchool of Medicine, Johns Hopkins University, Baltimore, Maryland, United States.ORCID 0000-0002-8186-0066
Nancy BerlingerThe Hastings Center, Garrison, New York, United States.
James R CareyDepartment of Entomology, University of California, Davis, California, United States.
M Aaron GuestEdson College of Nursing and Health Innovation, Arizona State University, Phoenix, Arizona, United States.ORCID 0000-0001-7356-3734
Zachary J HassSchool of Nursing, Edwardson School of Industrial Engineering, and Regenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, Indiana, United States.ORCID 0000-0002-0743-937X
Abigail T StephanCollege of Behavioral, Social and Health Sciences, Clemson University, Clemson, South Carolina, United States.ORCID 0000-0002-4418-6586
Bo XieSchool of Nursing, University of Texas at Austin, Austin, Texas, United States.ORCID 0000-0002-6016-6008

Funding

South Carolina Alzheimer's Disease Research Center
6 · The paper itself

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.

Indexed as

AgingArtificial IntelligenceGeriatricsAgedHumansGeriatricsInterdisciplinaryTechnologyTraining

Identifiers

PMID41410517
PMCPMC13017557

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.