Evidence map›Paper›PMID 41313148›Full record

ArticleJournal of the American Geriatrics Society2026

Stakeholders' Perceived Benefits and Concerns Regarding Artificial Intelligence in the Care of Older Adults.

Kacey Chae, Jacqueline Massare, Sato Ashida, Thomas K M Cudjoe, Peter Abadir, Alicia I Arbaje, Mathias Unberath, Phillip Phan, Nancy L Schoenborn

Abstract read
In one paragraph

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

9 authors.

Kacey ChaeDivision of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Jacqueline MassareDivision of Geriatric Medicine and Gerontology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Sato AshidaDepartment of Community and Behavioral Health, University of Iowa College of Public Health, Iowa City, Iowa, USA.
Thomas K M CudjoeDivision of Geriatric Medicine and Gerontology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.ORCID 0000-0002-2590-209X
Peter AbadirDivision of Geriatric Medicine and Gerontology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.ORCID 0000-0002-8186-0066
Alicia I ArbajeDivision of Geriatric Medicine and Gerontology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Mathias UnberathDepartment of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA.
Phillip PhanJohns Hopkins Carey Business School, Baltimore, Maryland, USA.
Nancy L SchoenbornDivision of Geriatric Medicine and Gerontology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Funding

Utilizing Technology and AI Approaches to Facilitate Independence andResilience in Older AdultsP30AG073104 · NIA · JOHNS HOPKINS UNIVERSITY · PI Peter M. Abadir · 2021 to 2026
$31.2M
Health Services and Outcomes Research for Aging PopulationsT32AG066576 · NIA · JOHNS HOPKINS UNIVERSITY · PI CYNTHIA Melinda BOYD, Jennifer L. Wolff · 2020 to 2026
$3.8M
Identifying and Addressing Social Isolation among Older Adults Living in Subsidized HousingK23AG075191 · NIA · JOHNS HOPKINS UNIVERSITY · PI Thomas Kofi Mensah Cudjoe · 2022 to 2026
$992k
AI-Driven Frailty Assessment and Molecular Correlation: A Multimodal Mentorship InitiativeK24AG088484 · NIA · JOHNS HOPKINS UNIVERSITY · PI Peter M. Abadir · 2024 to 2026
$596k
NIA NIH HHS K23 AG075191NIA NIH HHS K23AG075191NIA NIH HHS K24 AG088484NIA NIH HHS P30 AG073104NIA NIH HHS P30AG073104NIA NIH HHS T32 AG066576NIA NIH HHS T32AG066576
6 · The paper itself

Abstract

backgroundArtificial Intelligence (AI) applications in healthcare have significant potential to address the unmet needs of older adults. To successfully adopt and implement AI in the care of older adults, it is critical to understand stakeholders' perspectives. We sought to explore the perceived benefits and concerns among stakeholders about AI applications in caring for older adults.

methodsWe conducted individual semi-structured interviews with five groups of stakeholders: older adults and caregivers, clinicians, health system and health insurance plan leaders (payers), investors, and technology developers. Interviews asked about the perceived role of AI in the care of older adults, the perceived benefits and concerns regarding AI, and suggestions for mitigating the concerns. Interviews were audio recorded and transcribed verbatim. We used thematic content analysis to code the transcripts.

resultsOverall, 49 participants completed interviews: older adults/caregivers (n = 15), clinicians (n = 15), payers (n = 8), investors (n = 5), and technology developers (n = 6). We identified three themes. (1). Stakeholders reported multiple benefits of AI and identified several roles for its use in the care of older adults. (2). Stakeholders expressed concerns about AI, including worsening social isolation, high cost, propagating ageism, goal misalignment, and scams/misuse of AI; views on privacy concerns were mixed. (3). Stakeholders suggested potential solutions, such as setting appropriate guardrails, to mitigate concerns about AI.

conclusionsGiven the complexity and significant unmet needs among older adults, AI's potential benefits and harms are both heightened in this population. Appropriate guardrails are needed to leverage the benefits of AI while mitigating potential harms. Our findings have implications for technology developers to design innovations that align with the stakeholders' perceived roles for AI, for regulatory bodies to incorporate stakeholders' concerns when developing AI regulations, and for health systems and end-users of technology to critically evaluate a product regarding its affordability and impact on social isolation and ageism.

Indexed as

Artificial IntelligenceStakeholder ParticipationAgedCaregiversFemaleHumansInterviews as TopicMaleMiddle AgedQualitative Researchaging in placeartificial intelligenceassistive technologymachine learningqualitative analysisthematic analysis

Identifiers

PMID41313148
PMCPMC12750441

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.