A Growing Divide in AI-Enabled Care

Artificial intelligence promises to revolutionize clinical diagnostics and operational efficiency in healthcare. Yet its deployment remains concentrated in metropolitan academic centers, leaving rural communities behind. According to the Centers for Medicare and Medicaid Services, nearly 56 million Americans, 18 percent of the national population, reside in areas designated as rural where provider shortages and hospital closures already threaten access to essential services.

Insights from a Scoping Review

A recent scoping review following PRISMA guidelines examined peer-reviewed studies from January 2010 through April 2025 that focused on the development, implementation or evaluation of AI tools in rural U.S. healthcare settings. Twenty-six studies met inclusion criteria, of which fourteen evaluated predictive models, predominantly random forests and gradient-boosting machines, while twelve addressed data infrastructure systems such as rural health data warehouses and collaborative research networks .

Unmet Needs and Technical Barriers

Despite enthusiasm for generative AI in medicine, none of the reviewed studies reported on deployment of large language models or related generative methods in rural contexts, a gap that threatens to widen existing digital disparities. Half of the papers cited insufficient data volume and analytic expertise as barriers to both model development and local validation. Only a minority of projects progressed beyond algorithm design to real-world implementation or impact assessment, leaving questions about long-term sustainability unanswered .

Impacts on Acute and Preventive Services

Predictive applications most often supported resource allocation tasks such as forecasting COVID-19 testing demand. Few studies targeted acute clinical scenarios, such as stroke routing or trauma triage, despite substantially higher rural mortality rates in these time-sensitive conditions. The absence of AI-driven decision support in emergency or specialty care underscores a critical unmet need, given that rural patients frequently require transfer to urban centers for definitive treatment .

Recommendations for Equitable Deployment

Multi-stakeholder partnerships must bridge technical and resource gaps to ensure rural inclusion in AI innovation. Academic medical centers can share de-identified datasets and provide consultative support, while federated learning frameworks can enable collaborative model training without compromising patient privacy. Investments in rural broadband infrastructure, data interoperability standards and on-site analytic capacity will be essential to support both predictive and generative AI solutions.

A concerted focus on deployment studies in high-impact clinical domains, such as emergency care, chronic disease management and preventive screenings, alongside transparent reporting and rigorous external validation, will help transform promising prototypes into sustainable, life-saving tools for rural populations.