Artificial Intelligence and Digital Technologies in Prakriti Assessment: Toward Standardized Evidence-Based Ayurvedic Practice

Prakriti, the Ayurvedic concept of individual somatic constitution, forms the foundation for personalized preventive and therapeutic strategies by classifying individuals based on distinctive physical, physiological, and psychological attributes. Traditional methods of Prakriti assessment, such as physician-administered questionnaires, clinical examinations, and observational techniques, though deeply rooted in Ayurvedic principles, are often constrained by subjectivity, limited reproducibility, and a lack of standardization. Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), and digital health technologies offer new opportunities to modernize Prakriti assessment and enhance its reliability. This narrative review systematically located, selected, and synthesized relevant literature from scientific databases and institutional repositories to identify digital, AI, and ML tools applicable to the parameters outlined in the Central Council for Research in Ayurvedic Sciences (CCRAS) Prakriti Assessment Manual. Retrieved data were categorized according to their relevance to physical, physiological, psychological, and behavioral domains, emphasizing technologies that enable objective measurement and computational analysis. The review highlights the potential of integrating physiological sensors, image analysis systems, and computational algorithms with classical Ayurvedic approaches to improve the accuracy and clinical relevance of Prakriti analysis. The convergence of Ayurveda and digital health technologies holds transformative potential to generate evidence, enhance clinical applicability, and foster interdisciplinary research in personalized medicine. However, challenges related to data quality, interoperability, algorithmic interpretability, and domain-specific validation must be addressed to ensure credibility and wider adoption. Overall, digital innovations can significantly enhance the rigor, reach, and impact of Prakriti assessment in integrative healthcare.

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Publication Details

Journal
Journal of Ayurveda and Integrative Medicine
Published
2026-08-03
DOI
https://doi.org/10.1016/j.jaim.2026.101372
Primary Topic
Traditional Chinese Medicine Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Artificial Intelligence and Digital Technologies in Prakriti Assessment: Toward Standardized Evidence-Based Ayurvedic Practice

Sankari Subbiah, Ramakrishna Allam, Swagata Sarkar, B. Kothainayagi et al.
Journal of Ayurveda and Integrative Medicine
Traditional Chinese Medicine Studies
article

Artificial Intelligence and Digital Technologies in Prakriti Assessment: Toward Standardized Evidence-Based Ayurvedic Practice

Sankari Subbiah, Ramakrishna Allam, Swagata Sarkar, B. Kothainayagi, Sivgaamasundari R
article en

Abstract

Prakriti, the Ayurvedic concept of individual somatic constitution, forms the foundation for personalized preventive and therapeutic strategies by classifying individuals based on distinctive physical, physiological, and psychological attributes. Traditional methods of Prakriti assessment, such as physician-administered questionnaires, clinical examinations, and observational techniques, though deeply rooted in Ayurvedic principles, are often constrained by subjectivity, limited reproducibility, and a lack of standardization. Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), and digital health technologies offer new opportunities to modernize Prakriti assessment and enhance its reliability. This narrative review systematically located, selected, and synthesized relevant literature from scientific databases and institutional repositories to identify digital, AI, and ML tools applicable to the parameters outlined in the Central Council for Research in Ayurvedic Sciences (CCRAS) Prakriti Assessment Manual. Retrieved data were categorized according to their relevance to physical, physiological, psychological, and behavioral domains, emphasizing technologies that enable objective measurement and computational analysis. The review highlights the potential of integrating physiological sensors, image analysis systems, and computational algorithms with classical Ayurvedic approaches to improve the accuracy and clinical relevance of Prakriti analysis. The convergence of Ayurveda and digital health technologies holds transformative potential to generate evidence, enhance clinical applicability, and foster interdisciplinary research in personalized medicine. However, challenges related to data quality, interoperability, algorithmic interpretability, and domain-specific validation must be addressed to ensure credibility and wider adoption. Overall, digital innovations can significantly enhance the rigor, reach, and impact of Prakriti assessment in integrative healthcare.

Journal of Ayurveda and Integrative MedicineVol. 17(5)
Tamil Nadu Dr. M.G.R. Medical University (IN), Sri Sri University (IN)
Ministry of Ayurveda, Yoga and Naturopathy, Unani, Siddha and Homoeopathy
Zero hunger
Openalex Percentile: Top 5%
Traditional Chinese Medicine Studies
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