The Inverse Care Law in the Age of AI - Geographic Disparities in Health Care Technology Access

More than 50 years after Hart proposed the inverse care law, artificial intelligence (AI) in health care risks repeating the same pattern: Those who could benefit most have the least access. Using publicly available U.S. data, we show that rural areas face greater health burdens but have fewer health care resources and lower capacity to implement AI solutions. These disparities most likely extend beyond the United States. Age-adjusted mortality rates, chronic disease prevalence, and socioeconomic challenges increase with rurality, while the health care workforce and infrastructure decline. This mismatch creates contexts where AI could be most beneficial. However, AI implementation capacity declines from metropolitan to rural areas across key indicators, including interoperability infrastructure, AI adoption, and large language model readiness. In addition, clinical AI systems trained predominantly on urban populations raise concerns about distribution shift and transportability when applied to rural populations. Without deliberate intervention, AI risks amplifying rather than addressing existing disparities. Addressing this misalignment requires coordinated policy, research, and regulatory efforts that explicitly account for geography and equity. Policy should support foundational infrastructure in underserved systems, research should evaluate AI against current care alternatives rather than ideal standards, and regulation should address disparities in access and diffusion to ensure AI benefits reach areas of greatest clinical need.