In Hot Springs, South Dakota, prominent health officials are optimistic about the role of artificial intelligence (AI) in addressing healthcare challenges faced by rural America. Health Secretary Robert F. Kennedy Jr. emphasized to a panel of U.S. senators that AI-driven nursing solutions can deliver personalized care to rural patients. Similarly, Mehmet Oz, the head of the Centers for Medicare & Medicaid Services, noted that AI avatars could effectively connect rural residents with essential mental health resources.
State health leaders appear to echo this sentiment, actively leveraging funds from the recently established $50 billion federal Rural Health Transformation Program to integrate AI technologies into rural healthcare organizations.
AI refers to computational technologies that replicate human intelligence in tasks by identifying patterns and generating responses. Its potential in healthcare includes streamlining administrative tasks and pinpointing patients at risk, though various studies suggest limited evidence on AI's effectiveness in improving access to care and health outcomes in rural areas. There remains uncertainty about how states will track and share the results of their investments in this technology.
In Hot Springs, a community of approximately 3,400 residents, some locals express skepticism about AI's role in personal healthcare. Tara Haffner conveyed concerns about AI making critical errors and emphasized the importance of maintaining a direct relationship between patients and doctors.
Conversely, Phillip Mues, who manages technology at Cherry County Hospital and Clinic in rural Valentine, Nebraska, shared a more positive outlook. He noted that AI is already aiding healthcare professionals by reducing workloads, alleviating burnout, and allowing more time for patient interactions. However, he cautioned that AI cannot singularly resolve all challenges, especially for rural hospitals grappling with potential closures or service terminations.
The Rural Health Transformation Program was introduced last summer by congressional Republicans as part of a broader legislative package championed by President Donald Trump. This funding aims to mitigate concerns regarding anticipated Medicaid cutbacks that could disproportionately affect rural communities.
Hot Springs, which hosts a small independent hospital and a Department of Veterans Affairs facility, is known for its historical sandstone architecture and natural hot springs. Residents often travel at least an hour to access more specialized medical care. In interviews, residents indicated that the primary challenges in rural healthcare revolve around high costs and lengthy wait times stemming from workforce shortages.
Doug Nikkila, a heavy equipment operator, recognized that while AI and similar technologies carry both benefits and risks, improper implementation can lead to more issues than solutions. He suggested that AI could assist nursing homes by sending reminders for necessary resident care.
Although the healthcare sector is rapidly integrating AI, recent reports, including one by ARISE—a group affiliated with Stanford and Harvard—highlight concerns about the lack of comprehensive evaluations related to these tools. While AI has shown promise in controlled environments, evidence supporting its success in real-world applications remains scant, particularly in rural settings. An academic study indicated that merely 26 peer-reviewed papers analyzing AI in rural healthcare were published between 2010 and early 2025, with few focusing on implementation or outcomes.
Some states are embarking on innovative AI initiatives, such as exploring AI’s ability to suggest diagnoses or recommend treatments. Utah has expressed interest in funding an AI-based system for managing prescription refills, despite the controversies surrounding this approach.
Experts are cautious, noting that tools validated in urban centers may not translate well to rural contexts, where factors like transportation barriers can vastly differ. The discrepancy in testing settings also raises questions about the effectiveness of AI in rural health scenarios.
State plans for the Rural Health Transformation Program indicate a strong interest in utilizing AI for less visible, time-intensive tasks, including medical charting and processing referrals. Specific applications range from predictive algorithms guiding emergency responders in Mississippi to wearables that monitor patient health conditions.
Rural health facilities may confront further hurdles in tech implementation, including inadequate infrastructure and a lack of trained IT personnel. Many healthcare workers already juggle multiple roles, making it difficult to dedicate time for AI training. Additionally, connectivity issues may hinder patient access to AI tools.
Trust plays a critical role in rural communities. Residents such as Roy Ehlers expressed skepticism about AI's reliability in healthcare. However, Mues noted that while some patients may initially resist AI, many have accepted its use at the Valentine clinic.
Despite uncertainties around implementation, there is a growing push among rural and urban health systems to integrate AI solutions. Experts warn that rural healthcare organizations could face significant risks if they rush into contracts without adequate vetting of AI technologies.
To mitigate this, some states are using their rural health funding to form collaborative groups aimed at assisting healthcare facilities with the selection and monitoring of AI tools while also providing necessary training. The Centers for Medicare & Medicaid Services is working to develop a reporting process for states to track progress and outcomes, although there are currently no AI-specific requirements.
Experts advocate for measuring the efficacy of AI beyond mere usage rates, suggesting evaluations of its impact on healthcare metrics such as fall rates and hospital admissions. While some states have indicated plans to track outcomes linked to AI adoption, others focus solely on the technology's implementation.
Collectively, health officials and researchers agree that results from AI initiatives need to be shared across states and healthcare organizations to maximize learning and innovation. The goal is to avoid wasting resources on ineffective tools, particularly in rural healthcare settings where resources are already limited.
