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]