Review of artificial intelligence technologies for older adult health and wellbeing in NIA AITC Program

Artificial intelligence (AI) technologies are increasingly being applied to healthcare delivery, including efforts to support aging populations. This study presents a descriptive portfolio analysis of 100 pilot projects funded by the National Institute on Aging’s Artificial Intelligence and Technology Collaboratories (AITC) program between 2021–2024. Using a mixed-methods document analysis, we examine the clinical focus, user groups, care settings, and AI methodologies employed. Most pilots targeted dementia risk or management, targeted home-based settings, with machine learning-based classification as the dominant approach. While early detection and remote monitoring were common applications, opportunities to address broader clinical and social needs, caregiver burden, access, and real-world usability have emerged. Few projects explicitly targeted underserved populations or involved end-users in design. Our findings highlight areas of promise within the AITC‑funded portfolio while also pointing to opportunities for greater emphasis on human‑centered and inclusive design. This analysis provides insight into informing future funding strategies and underscores the potential value of deeper stakeholder engagement to better align AI technologies with older adults’ needs and priorities.

Authors

Institutions

Publication Details

Journal
npj Aging
Published
2026-11-02
DOI
https://doi.org/10.1038/s41514-026-00549-5
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Review of artificial intelligence technologies for older adult health and wellbeing in NIA AITC Program

Alicia I. Arbaje, Phillip Phan, Quincy Miles Samus, Thomas Cudjoe et al.
1 links
npj Aging
Artificial Intelligence in Healthcare and Education
article

Review of artificial intelligence technologies for older adult health and wellbeing in NIA AITC Program

Alicia I. Arbaje, Phillip Phan, Quincy Miles Samus, Thomas Cudjoe, Peter M. Abadir, Mounya Elhilali, Christopher G. Chute, Alexis J. Battle, Jennifer A. Schrack, Nancy L. Schoenborn, Mathias Unberath, Avantika S Shah, Najim Dehak, Esther S Oh, Ann Wiker, Rama Chellappa
article en

Abstract

Artificial intelligence (AI) technologies are increasingly being applied to healthcare delivery, including efforts to support aging populations. This study presents a descriptive portfolio analysis of 100 pilot projects funded by the National Institute on Aging’s Artificial Intelligence and Technology Collaboratories (AITC) program between 2021–2024. Using a mixed-methods document analysis, we examine the clinical focus, user groups, care settings, and AI methodologies employed. Most pilots targeted dementia risk or management, targeted home-based settings, with machine learning-based classification as the dominant approach. While early detection and remote monitoring were common applications, opportunities to address broader clinical and social needs, caregiver burden, access, and real-world usability have emerged. Few projects explicitly targeted underserved populations or involved end-users in design. Our findings highlight areas of promise within the AITC‑funded portfolio while also pointing to opportunities for greater emphasis on human‑centered and inclusive design. This analysis provides insight into informing future funding strategies and underscores the potential value of deeper stakeholder engagement to better align AI technologies with older adults’ needs and priorities.

npj Aging
Johns Hopkins University (US), Chicago Department of Public Health (US), Johns Hopkins Medicine (US), University of Illinois Chicago (US)
National Institutes of Health
Good health and well-being
Openalex Percentile: Top 22%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon