AI-driven Decision Support for Ayurveda: A Retrieval-Augmented Generation (RAG) Approach

Ayurveda, one of the world’s oldest holistic medical systems, contains a vast body of knowledge documented across classical texts, clinical guidelines, and practitioner experiences. However, much of this information remains scattered across manuscripts, books, and institutional archives, making it difficult for modern practitioners and researchers to access and utilize effectively. AI-driven decision support systems using Retrieval-Augmented Generation (RAG) offer a transformative approach to unlocking this knowledge for modern healthcare applications.

The proposed system combines large language models with domain-specific Ayurvedic knowledge repositories. Instead of relying solely on pretrained language models, the RAG architecture retrieves relevant knowledge from curated Ayurvedic datasets such as classical texts (Charaka Samhita, Sushruta Samhita), research publications, herbal pharmacology databases, and clinical records. The retrieved knowledge is then used to generate contextual, evidence-based responses.

For additional context and detailed documentation of this use case, please refer to pages 274-275 in the attached Casebook.