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

Abstract

The delivery of quality healthcare in rural and other hard-to-reach areas in the United States remains a challenge due to inadequate infrastructure, a shortage of healthcare workers, and limited funding. 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 specifically designed to identify chronic and critical diseases, such as diabetes, skin cancer, and influenza, in these areas. The tools employed include machine learning algorithms, portable diagnostic 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 AI-based methods were, 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 highlighted include restricted internet connections, legal restraints, and initial 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 rural populations through reduced access costs and improved diagnostic capabilities. Telemedicine tools for self-monitoring should be developed for other conditions and incorporated into other telemedicine solutions.

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