Gaps in artificial intelligence research for rural health in the United States: a scoping review

TL;DR

This paper reviews AI research in rural US healthcare and finds limited studies on model deployment and validation.

Contribution

The study identifies gaps in AI research for rural health, particularly in deployment and validation of AI tools.

Findings

Only 26 studies focused on AI in rural US healthcare between 2010 and 2025.

Most studies focused on predictive models for resource allocation, with few addressing deployment or impact.

Half of the studies noted data and analytic resource limitations, and none involved generative AI in rural settings.

Abstract

Artificial intelligence (AI) has impacted healthcare at urban and academic medical centers in the US. There are concerns, however, that the promise of AI may not be realized in rural communities. This scoping review aims to determine the extent of AI research in the rural US.

We conducted a scoping review following the PRISMA guidelines. We included peer-reviewed, original research studies indexed in PubMed, Embase, and WebOfScience after January 1, 2010 and through April 29, 2025. Studies were required to discuss the development, implementation, or evaluation of AI tools in rural US healthcare, including frameworks that help facilitate AI development (eg, data warehouses).

Our search strategy found 26 studies meeting inclusion criteria after full text screening with 14 papers discussing predictive AI models and 12 papers discussing data or research infrastructure. AI models most often targeted resource allocation and distribution. Few studies explored model deployment and impact. Half noted the lack of data and analytic resources as a limitation. None of the studies discussed examples of generative AI being trained, evaluated, or deployed in a rural setting.

Practical limitations may be influencing and limiting the types of AI models evaluated in the rural US. Validation of tools in the rural US was underwhelming.

With few studies moving beyond AI model design and development stages, there are clear gaps in our understanding of how to reliably validate, deploy, and sustain AI models in rural settings to advance health in all communities.

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