AI in Rural Healthcare: Bridging the Gap Between Technology and Underserved Communities

By Sowmy Thuppal, MD, PhD — Vice-chair, Department of Population Science and Policy; Research and Development, Ed Curtis Laboratory for Innovation in Medical Education (ECLIME), Southern Illinois University School of Medicine

The rural healthcare crisis

Rural Americans face healthcare disparities, with over 60 million people living in rural areas[1] — and the gap is growing.

Key challenges include:

Provider shortage: Rural areas have approximately 30 physicians/100,000 people, versus 263 in urban areas[2].

Geographic barriers: Rural residents travel an average of 17.8 miles for medical care, more than twice the distance of urban residents[3].

Delayed diagnosis: Limited specialist access, leading to late diagnosis and poor health outcomes.

Chronic disease burden and life expectancy: Rural communities face higher rates of heart disease, diabetes, obesity, and cancer mortality. Rural counties have a lower life expectancy and a higher rate of premature deaths[4].

Mental health: Many rural counties are designated as Mental Health Professional Shortage Areas[5,6].

Limited broadband: Unreliable internet hampers even basic telehealth adoption.

What is artificial intelligence (AI)?

AI refers to computer systems that perform tasks typically requiring human intelligence—analyzing data, recognizing patterns, and making recommendations. In healthcare, three AI types are most relevant:

Machine Learning (ML): Learns from data to predict outcomes, such as flagging a patient's elevated diabetes risk.

Natural Language Processing (NLP): Reads and understands text, enabling AI to mine electronic health records (EHRs) for critical patterns.

Computer Vision: Analyzes medical images to detect disease with specialist-level accuracy.[7]

Examples of high-impact, feasible AI applications

1. AI diagnostic imaging for delayed diagnosis

Delayed diagnosis is one of the most common reasons for increased morbidity and mortality. Many rural hospitals lack radiologists on staff, forcing images to be shipped to distant specialists with wait times of days or weeks. AI-powered diagnostic imaging tools can analyze scans in seconds with accuracy.[8] A rural clinic using an FDA-cleared AI retinal scanner, for example, can screen a diabetic patient for retinopathy on-site without a need for an ophthalmologist. The AI flags abnormal results for urgent follow-up. This dramatically reduces the diagnosis timeline from weeks to minutes and improves outcomes.

2. AI-powered telehealth triage for provider shortages

The U.S. could face a shortage of up to 86,000 physicians by 2036.[2] AI-driven triage tools integrated into telemedicine can fill the gap by handling symptom assessment, medication refill requests, and routine follow-ups without requiring physician time.[9] Patients interact with an AI assistant via phone or app and the system determines urgency, routes non-critical cases to nursing staff, and escalates emergencies immediately. This extends the capacity of every rural physician, allowing them to focus on complex cases while AI manages the high volume of routine interactions.

3. Geographic barriers and telemedicine concerns

Distance to healthcare facility limits access to care in rural or underserved areas, Mobile health clinics and community health workers bring essential services directly to patients, however, remote monitoring through wearable devices, enhanced with AI-driven alerting systems, allows providers to track patients' health in real time and intervene when issues arise enabling patients to receive timely guidance and virtual care from their homes.

Trust in artificial intelligence

AI adoption in rural healthcare is not purely a technical challenge, but a human one. Minoritized communities often approach AI with heightened skepticism, in part due to historical inequities and distrust of outside institutions.[10] Patients are significantly more likely to trust AI when a clinician is present or when governance mechanisms including FDA approvals and/or national certification are available.[11] Patients consistently prefer AI as an augmenting tool, not a replacement for human providers.[12] For AI to succeed in rural settings, transparency is essential: communities must understand how tools make decisions and where they can fail. Pilot programs that involve community voices in design and implementation consistently see higher adoption and better outcomes.

Conclusion

A systematic review of 26 studies found that AI tools were consistently associated with improved diagnostic accuracy, reduced turnaround times, and enhanced access to care in rural and low-resource settings.[13] AI works within existing infrastructure, extending reach and improving speed of care with minimal investment. Thoughtful, community-centered deployment of AI in rural health is one of the inevitable investments American healthcare can make today.

References

1. U.S. Government Accountability Office. Why Health Care Is Harder to Access in Rural America. gao.gov/blog/why-health-care-harder-access-rural-america
2. Tanzeem S, Ayyappan V. Addressing Healthcare Disparities Between Rural and Urban Communities. Healthcare Administration Leadership & Management Journal. (2025);3(4)211/214. doi.org/10.55834/halmj.3701318410
3. Rural Health Information Hub. Transportation to Support Rural Healthcare. ruralhealthinfo.org/topics/transportation
4. U.S. Department of Health and Human Services (ASPE). Health Disparities in Rural America (2024). aspe.hhs.gov
5. Health Resources & Services Administration (HRSA). Health Professional Shortage Areas Data. data.hrsa.gov
6. Rural Health Information Hub. Mental Health in Rural Communities Toolkit. ruralhealthinfo.org/toolkits/mental-health
7. Wang et al. (2024). Artificial Intelligence in Diagnostic Imaging. doi.org/10.1016/j.cmpbup.2024.100146
8. NIH / PubMed Central. AI-Driven Diagnostic Tools in Rural Healthcare (2025). pmc.ncbi.nlm.nih.gov/articles/PMC12892150
9. Ethics and governance of artificial intelligence for health: WHO guidance. Geneva: World Health Organization; 2021. Licence: CC BY-NC-SA 3.0 IGO.
10. Nature npj Health Systems (2025). Trust in AI-Assisted Health Systems. doi.org/10.1038/s44401-025-00016-5
11. JAMA Network Open (2026). Patient Trust in Medical AI. pmc.ncbi.nlm.nih.gov/articles/PMC12964161
12. NIH / PMC (2025). Patient Perceptions of AI in Dermatology. pmc.ncbi.nlm.nih.gov/articles/PMC12867938
13. NIH / PMC (2025). AI-Driven Diagnostic Tools Review. pmc.ncbi.nlm.nih.gov/articles/PMC12892150

Originally published in the Spring 2026 Illinois Rural Health Association newsletter. Reprinted with the author's permission.

— The ECLIME team