AI-Powered Decision Support Systems in Traditional Medicine

Abstract

Ayurveda is part of the traditional system of medicine, along with Traditional Chinese Medicine and Unani among others that continues to exist in the major part of the world today and serve many billions of people. Nevertheless, its application in contemporary clinical practice is a problem because of such issues as the absence of standardized approaches to diagnosis, the inability to determine the connections between symptoms and their treatment, as well as the infeasibility of digitalization of centuries-old knowledge. Decision Support Systems (DSS) based on Artificial Intelligence (AI) is an interesting way to overcome this scenario as it merges modern computational intelligence with the accrued experiences of traditional medicine. With the use of machine learning, natural language processing, knowledge graphs, and computer vision, AI-enhanced DSS can engage with large, heterogeneous data--including historical manuscripts and real-time patient data--to deliver evidence-informed diagnostic and therapeutic suggestions. [1,2] This paper addresses the architecture, possibilities, and practical utilization of the AI-powered DSS designed to be used with traditional medicine, their potential in terms of increasing diagnostic accuracy, enhancing the regularity of treatment, or supporting integrative health care strategy. Moreover, the paper addresses implementation issues, ethical implications, and the necessity of using culturally responsive AI frameworks to establish trust among the practitioners and acceptance of the patients. With the connection between long-standing knowledge and the capabilities of powerful computers, an AI-driven DSS would open the possibility of a more affordable, efficient, and universally standardized healthcare system. [3, 4]