JMIR Publications

ABSTRACT

Background:

The demographic shift towards an aging population is particularly pronounced in rural areas where access to healthcare is often limited. Older adults in rural communities face unique challenges, including geographic isolation, a shortage of healthcare providers, and limited access to specialized services. Artificial intelligence (AI) has emerged as a promising solution for improving healthcare access and delivery. However, concerns persist about equitable access to and representation in these innovations, especially for marginalized populations where technological literacy and infrastructure may present additional barriers to effective use.

Objective:

This systematic literature review aims to: (1) identify existing literature on AI for health research focused on rural older adults; and (2) critically evaluate the identified literature to highlight gaps and inform the development of inclusive AI for health research and design practices.

Methods:

On January 25, 2024, we followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 Protocol to conduct a systematic search of health and information science literature. We searched PUBMED and EBSCOhost (CINAHL Plus with Full Text and PsycINFO) databases to identify relevant research articles published between January 2013 and January 2023. We used predetermined search terms and built-in result limiters including: English language, human subjects, full text, and aged 65+. Publications were excluded if they did not include a focus on older adults’, rural populations, or artificial intelligence. The resulting data was reviewed, coded, and analyzed using thematic analysis.

Results:

Nine articles comprised the final sample. The results showed that representation of rural older adults in AI for health literature is limited. We identified three (3) salient themes: (1) Numbers over Narratives: The Quantitative Focus in AI For Health Research on Older Adults, (2) Efficacy Over Impact: Prevalence Clinical Outcomes in AI for Health Research, and (3) Deepening Disparities: Representation of Rurality Missing in AI for Health Research. These themes underscore the need for a more nuanced understanding of how AI for health research can be tailored to the specific needs of rural older populations.

Conclusions:

Our systematic analysis identified a robust body of research on AI for older adults. However, a critical gap emerged: a dearth of studies explicitly focusing on older adults in rural communities. This lack of representation raises concerns about the generalizability of findings and the potential for exacerbating existing healthcare disparities in rural areas. Future research should: (1) prioritize targeted recruitment strategies for rural participants to ensure better representation of rural older adults in AI for Health research; (2) develop community-based AI policies, practices, and products that reflect the specific needs and contexts of rural populations; and (3) explore solutions that address the limited representation of rural communities, ensuring AI interventions are equitable, accessible, and beneficial for all populations.