The provision of quality and refined disease diagnosis in remote and underserved areas has been one of the greatest challenges because most of the time healthcare infrastructure is minimal or none. Artificial intelligence (AI) has brought revolutionary changes in terms of scalable and real-time diagnostic systems that can fill vital healthcare gaps. This paper will examine how the AI-driven diagnostic platforms to be created could be implemented in low-resource environments and help identify diseases in real time. By combining recent developments in telemedicine, machine learning, as well as mobile health (mHealth), we assess how such platforms work, what is their diagnostic quality, and whether they can be successfully deployed in the field. We also look at the case studies that provide successful examples of the implementation of AI tools in communitybased health programs. The study indicates the conclusion that AI-enhanced diagnostic systems can help enhance early disease detection and response time as well as foster healthcare equity. Nonetheless, concerns over information privacy, algorithm discrimination, and localized training datasets are some of the factors that have been major impediments to mass usage. The study notes that the emerging intelligent diagnostic systems may be the key contributions towards the global health approaches, especially in those contexts where the features of progressive healthcare products have never been reached.
Ejaz et al. (Wed,) studied this question.