Medical Diagnosis with RAG-LLMs: A Hybrid Approach for AI-Driven Healthcare

Abstract

Medical diagnosis through Retrieval-Augmented Generation using Large Language Models (RAG-LLMs) enhances the reliability and accuracy of AI-facilitated healthcare support. We employ a large medical knowledge database through web scraping and structuring information from MedlinePlus to generate a database of 21,000 records within a Snowflake database to allow efficient retrieval. Once the system receives a query from the user, the system employs the method of cosine similarity to retrieve the top matching entries to generate the contextual basis upon which the response will be generated. The LLM then builds an educated response based on the retrieved information to allow strong diagnostic conclusions despite the fact that there are no direct answers within the retrieved context. The method unites quick information retrieval with the ability to generate information using the LLM to promote the availability of medical knowledge to support diagnosis.

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