Artificial Intelligence in Ayurveda: Crafting personalized care for delivering Ayurvedic obesity management strategies

Tara, B (2025) Artificial Intelligence in Ayurveda: Crafting personalized care for delivering Ayurvedic obesity management strategies. World Journal of Advanced Research and Reviews, 26 (2). pp. 1066-1070. ISSN 2581-9615

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Abstract

Background: Artificial intelligence (AI) has emerged as a powerful tool in health care, and its integration with Ayurveda in managing obesity not only ensures promising results but also enhances accessibility while maintaining a holistic approach. Aims and Objective: This review aims to explore the advantages of incorporating AI in management of ‘Sthoulya’ roga through personalised diet and treatment plans and real time monitoring of health parameters. Literature Review: A comprehensive literature search was conducted to identify relevant studies and articles on the integration of AI and Ayurveda in management of Sthoulya. The search included databases such as PubMed, Google Scholar, and relevant journals. Result: The integration of AI into Ayurvedic treatment for Sthoulya yields numerous benefits like multi-dimensional analysis of data, such as genetic, lifestyle, and environmental factors, of an individual along with his Prakriti and root imbalances causing obesity. These systems enable dynamic adjustments to dietary plans, oushadhi and lifestyle recommendations by real time monitoring of one’s Naadi, Jihwa and other components of roga rogi pareeksha ensuring treatments remain relevant and effective. Conclusion: By combining AI's analytical and predictive abilities with Ayurveda's time-tested, holistic principles, this synergy allows for highly personalized, preventive, and corrective healthcare solutions.

Item Type: Article
Official URL: https://doi.org/10.30574/wjarr.2025.26.2.1477
Uncontrolled Keywords: Sthoulya; Ayurveda; Artificial Intelligence; Obesity; Integrative Medicine; Personalised Healthcare
Depositing User: Editor WJARR
Date Deposited: 20 Aug 2025 10:46
Related URLs:
URI: https://eprint.scholarsrepository.com/id/eprint/2751