Background: Ayurveda, an ancient Indian medical science with over 5,000 years of practice, is gaining attention for its potential contributions to individualised and preventative treatment. However, its widespread acceptance in global scientific and clinical contexts is hampered by a lack of uniformity and empirical confirmation. The advent of Artificial Intelligence (AI) presents transformative opportunities to reinterpret, validate, and scale Ayurvedic concepts within a scientific framework. Aim: To investigate how AI technology can be used to develop an evidence-based model of Ayurveda by digitising, decoding, and assessing traditional knowledge using computational techniques. Methods: This paper examines current multidisciplinary research efforts using AI technologies such as machine learning, natural language processing (NLP), data mining, and bioinformatics on Ayurvedic datasets. Case studies include artificial intelligence-assisted prakriti (constitution) analysis, predictive modelling in tridosha-based diagnostics, automated parsing of traditional Sanskrit Ayurvedic texts, and clinical data integration for individualised therapy insights. Results and Discussion: Preliminary AI applications have shown potential in improving diagnostic accuracy, standardising herbal formulations, and developing dynamic patient-specific therapeutic routes. NLP algorithms can comprehend and structure traditional Ayurvedic scriptures, allowing for semantic search, disease-symptom correlation, and data harmonisation. Furthermore, AI-driven clinical analytics provides methods for evaluating therapy outcomes using real-world evidence. Conclusion: The incorporation of AI into Ayurvedic research represents a paradigm change from traditional experiential wisdom to technology-driven, evidence-based therapy. This convergence ensures Ayurveda's worldwide relevance while also advancing tailored healthcare systems. Continued collaboration among Vedic academics, physicians, data scientists, and technologists is required to ethically and successfully utilise this potential.
Viveka et al. (Thu,) studied this question.