Prakriti Analysis Using AI: A Convergence of Ayurveda and Modern Technology

Abstract

The intersection of Ayurveda and artificial intelligence (AI) is gaining scholarly attention, particularly in Prakriti (body constitution) analysis. Ayurveda, an ancient medical system, classifies individuals into Vata, Pitta, and Kapha constitutions, influencing their health and disease susceptibility. While, traditional Prakriti assessments rely on qualitative observations, but AI-driven technologies offer opportunities to enhance diagnostic precision and standardization. This review synthesizes existing literature on Prakriti assessment methodologies and their integration with computational approaches. While previous studies have explored questionnaire-based assessments and limited biometric parameters, challenges remain in standardization, reproducibility, and empirical validation. The lack of large-scale datasets and AI models trained on Ayurvedic principles hinders alignment with contemporary biomedical frameworks. Additionally, few studies integrate physiological and machine learning-based approaches for a more comprehensive understanding of Prakriti. This paper identifies these research gaps and examines emerging AI methodologies, such as machine learning, neural networks, and predictive modeling, hold potential for advancing Prakriti analysis. This review also highlights ethical and methodological considerations, emphasizing the need for interdisciplinary collaboration and regulatory frameworks. AI-driven approaches can provide evidence-based validation of Ayurveda, enhancing its acceptance in modern healthcare. The findings underscore the need for a standardized, data-driven framework for Prakriti analysis, leveraging AI to bridge the gap between traditional knowledge and scientific rigor. This convergence not only enhances the credibility of Ayurveda but also paves the way for personalized, preventive, and precision medicine in global healthcare.