Shiroma Kristina, Miller Jacqueline
School of Information Studies, Louisiana State University, 223T Peabody Hall, Baton Rouge, LA, 70803, United States, 1 2255787932.
JMIR Hum Factors. 2025 Sep 15;12:e70057. doi: 10.2196/70057.
Older adults in rural communities face unique challenges, including geographic isolation, a shortage of health care providers, and limited access to specialized services. Artificial intelligence (AI) has emerged as a promising solution for improving health care access and delivery. However, concerns persist about equitable access and representation in these innovations, especially for marginalized populations where technological literacy and infrastructure may present additional barriers to effective use.
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.
Between January 2024 and March 2025, we followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 Protocol to conduct a systematic search of health and computer science literature. We searched 7 databases (PubMed, CINAHL Plus with Full Text, PsycINFO, Web of Science, IEEE Explore, ACM Digital Library, and Scopus) to identify relevant research papers published between January 2013 and December 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 were not empirical or did not include a focus on older adults, rural populations, or AI. The resulting data were reviewed, coded, and analyzed using thematic analysis.
A total of 23 papers comprised the final sample. The results showed that the representation of rural older adults in AI for health literature is limited. We identified three 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.
Our systematic analysis identified a robust body of research on AI for older adults. However, a critical gap emerged with 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 health care disparities in rural areas. Future research should: (1) prioritize targeted recruitment strategies for rural older adult participants to ensure better representation 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 that AI interventions are equitable, accessible, and beneficial for all.
农村社区的老年人面临着独特的挑战，包括地理隔离、医疗保健提供者短缺以及获得专业服务的机会有限。人工智能（AI）已成为改善医疗保健可及性和服务提供的一个有前景的解决方案。然而，对于这些创新中的公平获取和代表性问题，尤其是对于技术素养和基础设施可能给有效使用带来额外障碍的边缘化人群，人们仍然存在担忧。
本系统文献综述旨在：（1）识别关于针对农村老年人的健康研究的人工智能的现有文献，以及（2）批判性地评估所识别的文献，以突出差距并为包容性的健康研究人工智能和设计实践的发展提供信息。
在2024年1月至2025年3月期间，我们遵循PRISMA（系统评价和元分析的首选报告项目）2020协议对健康和计算机科学文献进行系统检索。我们搜索了7个数据库（PubMed、CINAHL Plus with Full Text、PsycINFO、Web of Science、IEEE Explore、ACM数字图书馆和Scopus），以识别2013年1月至2023年12月期间发表的相关研究论文。我们使用预先确定的搜索词和内置的结果限制器，包括英语、人类受试者、全文以及65岁以上。如果出版物不是实证性的或没有专注于老年人、农村人口或人工智能，则将其排除。使用主题分析对所得数据进行审查、编码和分析。
最终样本共有23篇论文。结果表明，针对农村老年人的健康文献中人工智能的代表性有限。我们确定了三个突出主题：（1）数字高于叙事：人工智能在老年人健康研究中的定量关注，（2）功效高于影响：人工智能在健康研究中的临床结果普遍性，以及（3）差距加深：人工智能在健康研究中农村地区代表性缺失。这些主题强调需要更细致入微地理解如何使健康研究人工智能适应农村老年人群体的特定需求。
我们的系统分析确定了大量关于老年人人工智能的研究。然而，出现了一个关键差距，即明确关注农村社区老年人的研究匮乏。这种代表性的缺乏引发了对研究结果可推广性以及加剧农村地区现有医疗保健差距可能性的担忧。未来的研究应该：（1）优先为农村老年参与者制定有针对性的招募策略，以确保在健康研究人工智能中有更好的代表性；（2）制定基于社区的人工智能政策、实践和产品，以反映农村人口的特定需求和背景；以及（3）探索解决农村社区代表性有限问题的方案，确保人工智能干预对所有人都是公平、可及且有益的。
