Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report

Purpose: Large Language Models (LLMs) hold significant promise for medical applications. Retrieval Augmented Generation (RAG) emerges as a promising approach for customizing domain knowledge in LLMs. This case study presents the development and evaluation of an LLM-RAG pipeline tailored for healthcare, focusing specifically on preoperative medicine.
Methods: We developed an LLM-RAG model using 35 preoperative guidelines and tested it against human-generated responses, with a total of 1260 responses evaluated. The RAG process involved converting clinical documents into text using Python-based …

applications case case study cs.ai cs.cl development domain domain knowledge evaluation healthcare knowledge language language models large language large language models llm llms medical pipeline rag report retrieval retrieval augmented generation study testing