Evidence map›Paper›PMID 40184066›Full record

ArticleJAMA network open2025

Perspectives on AI and Novel Technologies Among Older Adults, Clinicians, Payers, Investors, and Developers.

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

Abstract read
In one paragraph

Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Artificial Intelligence Can Direct Patients Toward a Complaint-specific Musculoskeletal Provider.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026
    Article
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
  7. Reimagining Resilience in Aging: Leveraging AI/ML, Big Data Analytics, and Systems Innovation.The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry · 2025
    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.

Nancy L SchoenbornDivision of Geriatric Medicine and Gerontology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Kacey ChaeDivision of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Jacqueline MassareDivision of Geriatric Medicine and Gerontology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Sato AshidaDepartment of Community and Behavioral Health, University of Iowa College of Public Health, Iowa City.
Peter AbadirDivision of Geriatric Medicine and Gerontology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Alicia I ArbajeDivision of Geriatric Medicine and Gerontology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Mathias UnberathDepartment of Computer Science, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland.
Phillip PhanJohns Hopkins Carey Business School, Baltimore, Maryland.
Thomas K M CudjoeDivision of Geriatric Medicine and Gerontology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.

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 K24 AG088484NIA NIH HHS P30 AG073104NIA NIH HHS T32 AG066576
6 · The paper itself

Abstract

Importance: Artificial intelligence (AI) and novel technologies, such as remote sensors, robotics, and decision support algorithms, offer the potential for improving the health and well-being of older adults, but the priorities of key partners across the technology innovation continuum are not well understood. Objective: To examine the priorities and suggested applications for AI and novel technologies for older adults among key partners. Design, Setting, and Participants: This qualitative study comprised individual interviews using grounded theory conducted from May 24, 2023, to January 24, 2024. Recruitment occurred via referrals through the Johns Hopkins Artificial Intelligence and Technology Collaboratory for Aging Research. Participants included adults aged 60 years or older or their caregivers, clinicians, leaders in health systems or insurance plans (ie, payers), investors, and technology developers. Main Outcomes and Measures: To assess priority areas, older adults, caregivers, clinicians, and payers were asked about the most important challenges faced by older adults and their caregivers, and investors and technology developers were asked about the most important opportunities associated with older adults and technology. All participants were asked for suggestions regarding AI and technology applications. Payers, investors, and technology developers were asked about end user engagement, and all groups except technology developers were asked about suggestions for technology development. Interviews were analyzed using qualitative thematic analysis. Distinct priority areas were identified, and the frequency and type of priority areas were compared by participant groups to assess the extent of overlap in priorities across groups. Results: Participants included 15 older adults or caregivers (mean age, 71.3 years [range, 65-93 years]; 4 men [26.7%]), 15 clinicians (mean age, 50.3 years [range, 33-69 years]; 8 men [53.3%]), 8 payers (mean age, 51.6 years [range, 36-65 years]; 5 men [62.5%]), 5 investors (mean age, 42.4 years [range, 31-56 years]; 5 men [100%]), and 6 technology developers (mean age, 42.0 years [range, 27-62 years]; 6 men [100%]). There were different priorities across key partners, with the most overlap between older adults or caregivers and clinicians and the least overlap between older adults or caregivers and investors and technology developers. Participants suggested novel applications, such as using reminders for motivating self-care or social engagement. There were few to no suggestions that addressed activities of daily living, which was the most frequently reported priority for older adults or caregivers. Although all participants agreed on the importance of engaging end users, engagement challenges included regulatory barriers and stronger influence of payers relative to other end users. Conclusions and Relevance: This qualitative interview study found important differences in priorities for AI and novel technologies for older adults across key partners. Public health, regulatory, and advocacy strategies are needed to raise awareness about these priorities, foster engagement, and align incentives to effectively use AI to improve the health of older adults.

Indexed as

Artificial IntelligenceAgedAged, 80 and overCaregiversFemaleHumansInvestmentsMaleMiddle AgedQualitative Research

Identifiers

PMID40184066
PMCPMC11971670

What Socratic 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 Socratic graph.