Artificial Intelligence in Ayurveda: A Simple Overview

  • Chetna Rathor Post Graduate Scholar, Department of Swasthvritta and Yoga, Pandit Khushilal Sharma Govt. Autonomous Ayurveda College & Institute, Bhopal, Madhya Pradesh, India.
  • Vijendra Singh Mandloi Lecturer, Department of Swasthvritta and Yoga, Pandit Khushilal Sharma Govt. Autonomous Ayurveda College & Institute, Bhopal, Madhya Pradesh, India.
  • Ishwari Sachan Post Graduate Scholar, Department of Samhita Siddhanta, Pandit Khushilal Sharma Govt. Autonomous Ayurveda College & Institute, Bhopal, Madhya Pradesh, India.
  • Vikash Sahu Post Graduate Scholar, Department of Shalya Tantra, Gramin Ayurveda Institute, Patur, Dist. Akola, Maharashtra, India.
Keywords: Ayurveda, Artificial intelligence, Machine learning, Research

Abstract

Ayurveda is regarded as a thousand-year-old science. This system of medicine has been time-proven and beneficial not just for maintaining individual's health but also in ensuring their (holistic) well-being. Combining complementary and modern medicines can help in solving patient issues and improve treatment strategies. This study looks into the use of machine learning in Ayurveda, an age-old Indian medical practice that is becoming more and more well-known throughout the world. In order to close the gaps in the current state of knowledge, it is imperative that modern technologies like artificial intelligence and machine learning be combined with Ayurvedic sciences. We have the potential to revolutionize the field of Ayurveda by accepting and transforming this digital landscape. Researchers have combined AI with additional technological advances to enhance Ayurvedic medicines’ efficiency, availability, and reliability. The study analyses how AI influences Ayurveda.

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CITATION
DOI: 10.21760/jaims.9.11.38
Published: 2025-02-03
How to Cite
Chetna Rathor, Vijendra Singh Mandloi, Ishwari Sachan, & Vikash Sahu. (2025). Artificial Intelligence in Ayurveda: A Simple Overview. Journal of Ayurveda and Integrated Medical Sciences, 9(11), 271 - 275. https://doi.org/10.21760/jaims.9.11.38
Section
Review Article