AI-Assisted Ayurvedic Pattern Recognition for Personalized Assessment of Prakriti and Therapeutic Response Using Multimodal Health Data

Abstract

Ayurvedic Prakriti represents an individualized constitutional framework with potential relevance to contemporary precision medicine, yet its conventional assessment remains constrained by subjectivity, inter-observer variability, and limited standardization. This review examines the emerging role of artificial intelligence (AI) and multimodal health data in improving Prakriti recognition and predicting personalized therapeutic response. Evidence across clinical characteristics, questionnaires, physiological signals, wearable sensors, imaging, voice features, genomics, metabolomics, proteomics, and microbiome profiles indicates opportunities to construct multidimensional representations of constitutional phenotypes. Machine learning, deep learning, computer vision, natural language processing, and multimodal fusion can integrate these heterogeneous data streams, while explainable AI may improve interpretability and clinical acceptance. Importantly, AI-enabled Ayurveda should progress beyond automated constitutional classification toward prediction of individualized responses to dietary, lifestyle, herbal, yoga, and behavioral interventions. Longitudinal digital phenotyping and causal machine learning may further support dynamic monitoring and estimation of heterogeneous treatment effects. However, clinical translation requires standardized Prakriti definitions, high-quality multicenter datasets, external and cross-population validation, transparent reporting, privacy protection, algorithmic fairness, and regulatory oversight. Future research should prioritize prospective studies demonstrating whether multimodal AI-derived phenotypes provide incremental clinical value, improve therapeutic selection, and enhance patient outcomes within evidence-based personalized healthcare across diverse populations and clinical settings globally.