Miles Away from Care, One Step Closer With AI

According to the American Hospital Association (AHA), since January 2005, 195 rural hospitals have closed or converted in the United States, including 19 rural hospital closures in 2020 alone – representing the highest annual closure rate in the past decade. For millions of Americans in rural communities, accessing healthcare has long meant an impossible choice; endure hours of travel for routine care, or postpone treatment until a manageable condition becomes a crisis. This is not merely a healthcare delivery failure; it is a governance and public policy crisis.

The scope of rural health disparities represents a systemic policy challenge. Around 46 million people - just over 15% of the U.S. population - reside in rural areas, covering 72% of the country's landmass. Weeks et al. (2023) found that "… rural counties overwhelmingly had worse measures of Social Determinant of Health (SDOH) at the county level. With few exceptions, the measures we examined were getting worse between 2015 and 2019 in all counties, relatively more so in rural counties, resulting in the widening of rural-urban disparities in these measures". Median travel distance to the nearest inpatient or emergency department increased significantly after a hospital closure. Miller et al. (2020) confirmed that "rural hospital closures increased mean Emergency Medical Services (EMS) transport and total activation times in the subsequent year after a closure". Arredondo et al. (2023) state that "access to primary healthcare plays a crucial role in overall health and wellbeing, physician shortages have broad impacts on rural" communities.

Artificial Intelligence (AI), which in clinical settings is referred to as Intelligence Augmentation (IA), having a human in the loop, offers a promising policy mechanism for expanding the capacity of health service delivery to underserved communities. Nwankwo et al. (2024) argue that "telemedicine and AI presents a promising solution to enhance healthcare access and quality in rural … integrating telemedicine and AI into rural healthcare settings”. These systems use wearable or implantable devices, digital health records, and patient-reported information to monitor health and alert healthcare providers of abnormal changes for timely intervention. Ghadi et al. (2025) highlight that "responsive deployment of AI-enhanced wearable systems for remote patient monitoring. … shows potential in improving access to healthcare in rural and underserved areas of the country". Diagnostic AI is reaching communities previously left behind. Perez et al. (2025) found that researchers can "explore the use of AI-driven diagnostic tools and telemedicine … able to diagnose patients with diseases in rural areas where" specialists are scarce. AI algorithms can analyze medical images in remote areas. For example, CT, MRI, and X-ray scans give healthcare professionals credible automated diagnostic findings.

In New Mexico, Presbyterian Healthcare Services has implemented AI-powered clinical decision support tools integrated into its electronic health record system. Their pilot program uses AI copilots, created by RhythmX AI, embedded within their existing electronic health record (EHR) system called Epic. It helps reduces administrative burden and ensure clinicians have the “information needed to address key patient health concerns efficiently at each patient visit”.

Yet, there are significant notable barriers to it. Telehealth utilization rates are lower among rural communities due to factors such as poor broadband access, poverty, education. Drake et al. (2019) found that "broadband infrastructure in rural areas prevents telemedicine from mitigating the barriers to care … and may explain the low rates of telemedicine use among rural Medicare enrollees". Cortelyou-Ward et al. (2020) identify broadband service availability and quality as a key structural barrier to telehealth utilization in rural America. The World Health Organization (2021) urges health-care systems to prioritize "avoiding AI that encodes biases that are detrimental to equitable provision of and access to healthcare". The AITC program, funded by the NIA has earmarked $40 million from 2021 to 2026, to fund promising Ai-driven AgeTech projects that seek to improve care healthcare outcomes for older adults.

For policymakers, the evidence suggests three priorities. First, treat rural broadband expansion as healthcare infrastructure, not merely communications policy. Second, develop regulatory frameworks that ensure AI diagnostic safety while enabling rapid deployment and integration in underserved areas. Third, integrate digital literacy training into existing community health programs.

AI is assisting with earlier problem detection and improved care in areas that were previously ignored. But realizing this potential requires policy innovation, administrative capacity, and sustained public investment. We may still be miles away from the nearest hospital, but with sound policy and strategic AI deployment, we are one step closer to equitable healthcare for all.