Artificial Intelligence in Ayurveda: A Comprehensive Review of Trends and Applications

Abstract

Ayurveda, the traditional Indian system of medicine, emphasizes holistic health management through personalized diagnosis and treatment based on individual constitution (Prakriti) and disease characteristics (Vikriti). The integration of Artificial Intelligence (AI) in Ayurveda has emerged as a promising approach to enhance diagnostic accuracy, optimize treatment strategies, and facilitate predictive healthcare. AI techniques, including machine learning, natural language processing, and expert systems, are being utilized to analyse complex Ayurvedic texts, patient data, and clinical outcomes. These technologies can identify patterns and correlations that are often challenging to discern through conventional methods, enabling more precise recommendations for herbal formulations, lifestyle modifications, and Panchakarma therapies. Additionally, AI-driven platforms support decision-making for practitioners by providing real-time insights, reducing human error, and enabling remote healthcare delivery. The application of AI in Ayurveda also extends to drug discovery, disease prediction, and validation of traditional therapies through evidence-based research. Despite these advancements, challenges such as data standardization, limited digitalization of Ayurvedic records, and integration of AI tools with traditional practices remain significant. Ethical considerations, patient privacy, and interpretability of AI models are also critical concerns that must be addressed. This review summarizes current research trends, applications, and limitations of AI in Ayurveda, highlighting its potential to revolutionize personalized healthcare and bridge the gap between traditional wisdom and modern technology. Future research should focus on developing robust AI frameworks tailored to Ayurvedic principles, promoting digitization of classical texts, and validating AI-assisted interventions through clinical trials to ensure safe and effective integration into mainstream healthcare.