Artificial intelligence in personalized Ayurveda: Transforming Prakriti-based health care through digital health innovation

Ayurveda, one of the oldest medical systems in the world, is based on the Tridosha (~three regulatory functional factors of the body) theory comprising Vata (~ Dosha responsible for movement and cognition), Pitta (~ Dosha responsible for regulating body temperature and metabolic activities), and Kapha (~ Dosha responsible for regulating body fluids and keeping the body constituents cohesive), while Prakriti (~somatic constitution) forms the foundation of personalized medicine in Ayurveda, as each one of us can be individualized based on our Prakritis . However, the conventional approaches for assessing Prakriti are frequently subjective and reliant on the practitioner’s expertise. This may lead to variations during categorizing the individuals and ultimately alter the therapeutic impacts. Artificial Intelligence (AI) and Machine Learning (ML) can enhance the objectivity, reliability, and data-driven nature of these assessments. Recent studies have explored ML-based approaches for Prakriti classification using questionnaire-derived phenotypes and image-based assessments. Exploratory Ayurgenomics studies have further examined associations between Prakriti types and biological or genetic features. Emerging AI-driven approaches are being explored to support diagnostic standardization, assist in individualized therapeutic planning, and enable data-driven analysis in areas such as formulation research and drug discovery within Ayurveda. Ayurvedic data are increasingly being integrated with contemporary medical information through national digital health initiatives such as the NAMASTE portal, which provides standardized Ayush terminology aligned with the International Classification of Diseases (ICD) frameworks, and the Ayush Grid, which supports digital interoperability and data integration across Ayush healthcare systems. Even with these advancements showing promise, there are still issues, including small datasets, lack of standards, and moral dilemmas about data privacy, cultural integrity, and most importantly, comparability of available datasets for their integration into AI and ML systems. Transparent algorithms, extensive cooperative research, and interaction with digital health systems are therefore necessary for future advancements. Together, AI and Ayurvedic frameworks may support more consistent Prakriti assessment, enable scalable phenotype–biological correlations, and strengthen preventive and personalized care models, while also requiring careful attention to data quality, standardization, and validation challenges for their meaningful clinical adoption.