Lit review identifies a ‘glaring gap’ involving older Americans and healthcare AI

Conventional wisdom has it that older adults willingly lag behind younger subpopulations when it comes to reaping the benefits of emerging healthcare technologies, including AI.

A new literature review shows this may be a tone-deaf diagnosis. What’s actually lacking, the review’s authors surmise from their findings, is research aimed at understanding the community healthcare needs of particular seniors. 

“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,” write Kristina Shiroma, PhD, and Jacqueline Miller, MLIS, in a paper published Sept. 15 in JMIR Human Factors. “This lack of representation raises concerns about the generalizability of findings and the potential for exacerbating existing healthcare disparities in rural areas.”

The researchers, both with the School of Information Studies at Louisiana State University, identify three themes that emerged from their study, which reviewed some 23 published studies. 

1. Numbers over narratives.

AI for health research predominantly emphasizes quantitative findings, focusing on measurable results, such as diagnostic accuracy, predictive modeling and algorithmic efficacy. 

“This trend reflects broader scientific priorities placed on numerical data and technical performance, often at the expense of deeper, qualitative insights into the lived experiences of older adults,” Shiroma and Miller remark. 

‘Without [a] qualitative dimension, the development of AI technologies will likely continue to miss crucial elements that could optimize their utility in everyday healthcare settings, particularly in rural and underserved populations.’ 

2. Efficacy over impact.

A more inclusive, participatory approach may ensure that AI tools are better aligned with the challenges and needs of rural older adults.

“[W]hile AI-driven tools demonstrate potential in improving diagnostic precision and personalizing care for older adults, the understanding of their real-world application in rural areas remains underexplored,” the authors note. 

‘[W]ithout a community-centered approach to AI for health research—one that includes understanding the barriers and experiences of rural older adults—the focus may remain on clinical outcomes rather than the broader community health outcomes that truly affect these populations.’ 

3. Deepening disparities.

The findings from this systematic review of the literature highlight a glaring gap in the representation in AI for health research of the perspectives of older adults from rural areas.

“AI tools for healthcare and health decision support are developed using large datasets that, if not inclusive, will continue to marginalize communities like rural older adults,” the authors comment. “Moreover, most of the studies we reviewed were heavily urban-centric, emphasizing data from metropolitan populations while overlooking the distinct challenges and health contexts of rural communities.”

‘AI models developed in these contexts may fail to reflect the lived experiences or healthcare needs of rural older adults, further widening existing disparities.’ 

Shiroma and Miller suggest future research into rural older adults vis-à-vis healthcare AI should:

• Prioritize targeted recruitment strategies for rural older adult participants to ensure better representation in AI for health research;
• Develop community-based AI policies, practices and products that reflect the specific needs and contexts of rural populations; and 
• Explore solutions that address the limited representation of rural communities, ensuring that AI interventions are equitable, accessible and beneficial for all.

The study is posted in full for free. 

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