AI-Assisted Diagnostics for Rural and Underserved Communities: Bridging Healthcare Gaps

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Abstract

The delivery of quality health care in rural and other hard-to-reach areas in the United States is still a problem due to poor infrastructural development, lack of enough health workers, and little or no funds. These barriers lead to late diagnosis, worse health and a large disparity in health care. This research aims to identify the process of developing and implementing cost-effective diagnostic AI systems that are specific to identifying chronic and critical diseases such as diabetes, skin cancer, and influenza in these areas. The tools applied are machine learning algorithms, portable diagnosis devices, and cloud-based analytics. They showed high diagnostic accuracy with sensitivity of up to 94% for diabetes diagnosis and 91% for skin cancer diagnosis. Another important improvement was the cost efficiency, which was noted as the fact that the AIbased methods were significantly cheaper, on average 45% cheaper than conventional methods. Moreover, the use of AI-supported tools enhanced early detection by a large margin, especially in Appalachia; early diabetes identification rose from 40% in 2019 to 78% in 2023. Nevertheless, some of the issues that were highlighted include restricted internet connection, legal restraints, and first rejection from the medical fraternity. Solving these problems will require infrastructure development, changes in the law, and trust in new technologies. This paper focuses on the role of AI Diagnostics in filling gaps in healthcare for special populations in the United States. In this paper, AI technologies are argued to be a scalable solution to address the equity issue and enhance healthcare for the rural population through access cost reduction and better diagnostic capabilities. Telemedicine tools for self-monitori

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