Natural language processing in Ayurveda: Unlocking the wisdom of ancient Ayurveda texts with artificial intelligence

Abstract BACKGROUND: Ayurveda is rooted in classical texts like Brihatyari ( Charak Samhita, Sushruta Samhita , and Ashtang ), Laghutrayi ( Bhavprakash, Madhav Nidaan , and Sharangdhar ), and Ayurvedottar Samhitas , which contain invaluable knowledge of preventive and curative healthcare. However, the complexity of these texts, written in Sanskrit and unstructured formats, poses significant challenges to modern research and clinical application. OBJECTIVES: This study aimed to investigate the potential of natural language processing (NLP) techniques to digitize, translate, and structure Ayurvedic texts, thereby unlocking their valuable insights for contemporary use. MATERIAL AND METHODS: Artificial intelligence-driven NLP techniques, including text digitization, machine translation, and semantic analysis, are applied to classical Ayurvedic texts. These methods aimed to decipher linguistic complexities, extract structured data, and make the information accessible for modern research and applications such as drug discovery and clinical practices. OUTCOMES AND RESULTS: NLP successfully identifies the relationship between Ayurvedic herbs, diseases, and treatment protocols. The digitization and structuring of the text open new avenues for applying Ayurvedic knowledge in the modern healthcare system, facilitating research and improving clinical outcomes. CONCLUSION: The integration of NLP with Ayurveda has the potential to bridge the gap between the ancient system and modern medicine, enabling innovations in drug discovery, clinical research, and personalized healthcare rooted in Ayurvedic principles.