Evidence map›Paper›PMID 39909831›Full record

ArticleThe journals of gerontology. Series A, Biological sciences and medical sciences2025

Engagement of Older Adults in the Design, Implementation, and Evaluation of Artificial Intelligence Systems for Aging: A Scoping Review.

Hannah Cho, Oonjee Oh, Nancy Greene, Larissa Gordon, Sherry Morgan, Lisa Walke, George Demiris

Abstract readScoping Review
In one paragraph

Article in The journals of gerontology. Series A, Biological sciences and medical sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. 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

7 authors.

Hannah ChoSchool of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0009-0006-7504-5934
Oonjee OhSchool of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Nancy GreeneSchool of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Larissa GordonHolman Biotech Commons, University of Pennsylvania Libraries, Philadelphia, Pennsylvania, USA.
Sherry MorganHolman Biotech Commons, University of Pennsylvania Libraries, Philadelphia, Pennsylvania, USA.
Lisa WalkePerelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
George DemirisSchool of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0002-6318-5829

Funding

Technology Identification and Training CoreP30AG073105 · NIA · UNIVERSITY OF PENNSYLVANIA · PI DEMIRIS, GEORGE, KARLAWISH, JASON H · 2021 to 2025
$21.2M
NIA NIH HHS P30 AG073105
6 · The paper itself

Abstract

Integration of artificial intelligence (AI) in health and healthcare, especially for older adults, has significantly advanced healthcare delivery. AI technologies, with capabilities such as self-learning and pattern recognition, are employed to address social isolation and monitor older adults' daily activities. However, rapid AI development often fails to consider the heterogeneous needs of older populations, which could exacerbate an existing digital divide and inequality. This scoping review examines older adults' involvement in AI system design, implementation, and evaluation of AI systems in health and healthcare literature, emphasizing the necessity of their input for beneficial AI systems. We conducted a scoping review according to PRISMA-SCR. We reviewed 17 studies, finding that half of these studies (n = 8) engaged older adults during the design phase, a small number (n = 3) during the evaluation stage, and even fewer (n = 2) involved older adults in the implementation stage. Despite AI's growing role, design processes often overlook older adults' needs. Our findings emphasize the need for inclusive, participatory design approaches to address ethical and equity challenges, enhancing user engagement and relevance. We also highlight how these approaches address the needs of older adults and improve outcomes. Specifically, we integrated evidence showing the practical benefits of these approaches for better accessibility, usability, and engagement among older adults. Although AI has potential to improve healthcare delivery, these approaches must be part of broader efforts to ensure ethical, inclusive, and equitable AI practices, especially in gerontology.

Indexed as

AgingArtificial IntelligenceAgedDelivery of Health CareHumansArtificial intelligenceMachine learningParticipatory designSuccessful aging

Identifiers

PMID39909831
PMCPMC12368510

What OpenQuestion 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 OpenQuestion graph.