Prakash Muthuperumal; Dhivya Karmegam; Bagavandas Mappillairaju
Abstract
The traditional medicine system is gaining more importance and has provided a lot of very important drugs for modern medicine. However, traditional medicines lack strong scientific ...
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The traditional medicine system is gaining more importance and has provided a lot of very important drugs for modern medicine. However, traditional medicines lack strong scientific evidence by modern pharmacology standards, and the safety and efficacy of such interventions have not been established. Machine learning (ML) and artificial intelligence (AI) can play a significant role in various aspects of the Ayush (Ayurveda, Yoga, Naturopathy, Unani, Siddha, and Homeopathy) system to provide scientific evidence. Therefore, this systematic review aims to understand the gaps in ML and AI applications in Ayush systems and provide research directions for future researchers in these fields. We have conducted literature searches using databases such as Scopus, PubMed, IEEE Xplore, and Science Direct to retrieve published and unpublished research articles related to the use of -ML and AI in Ayush systems. We have included various study designs, such as case studies, case series, cohort studies, case-control studies, RCTs, and non-randomized controlled trials. We have included the articles that were written in English. Our systematic review identifies gaps in the use of ML and AI in these fields, providing research directions. Indian traditional medicine systems have shown some usage of DL and ML, with yoga and Ayurveda being the most commonly used. However, other areas such as diagnosis, prognosis, biomarker identification, and pharmacokinetics hold potential for the adoption of ML and DL. Evidence-based medicine and the proper use of ML and DL techniques in all areas of Ayush research are crucial for precision medicine.
