Ayurveda and Artificial Intelligence: A Review of Applications in Diagnosis, Prakriti Analysis, and Personalized Therapeutics

Abstract

Background: Ayurveda, the Indian system of traditional personalized medicine, focuses on constitution (Prakriti) and imbalance-based (Vikriti) diagnosis and treatment. Artificial Intelligence (AI) capabilities in pattern recognition, multimodal data fusion, and prediction can uniquely contribute to digitizing and scaling Ayurvedic modalities. Objective: This review integrates evidence regarding AI applications for Ayurvedic diagnosis, Prakriti assessment, and individualized therapeutics, with opportunities, challenges, and future research directions. Methods: A narrative review was conducted by searching PubMed, Scopus, Web of Science, and AYUSH-specific journals (2010–2025) using term Ayurveda, Artificial Intelligence, machine learning, digital diagnosis, and Prakriti. Peer-reviewed studies, technical reports, and conceptual models were included. Grey literature (e.g., apps, websites) was excluded unless directly relevant to clinical practice. Results: Applications of AI in Ayurveda are - Diagnosis: Digital Nadi Pariksha with pulse sensors and Machine Learning (ML)-based methods (up to 85% accuracy), tongue imaging with Convolutional Neural Network (CNN)-based methods for disease categorization, and hybrid Artificial Intelligence (AI) models incorporating Darshana (inspection/visual examination), Sparshana (palpation/tactile examination), and Prashna (interrogation/patient history taking). Prakriti Analysis: Automated questionnaires, ML-based classification, and Ayurgenomics integration enable scalable, objective constitution typing. Personalized Therapeutics: AI-powered treatment suggestion systems, predictive Panchakarma protocols, and NLP-based selection models of drugs boost individualized care. Limitations: Challenges include lack of standardized datasets, the interpretability of AI models, epistemological mismatch, and unresolved ethical/regulatory frameworks. Conclusion: AI can make Ayurveda into an evidence-based, scalable, and worldwide applicable system of personalized medicine. Success involves data standardization, interdisciplinary teamwork, and culturally appropriate regulation to provide assurance of safety, trustworthiness, and clinical translation.