NaviPath: A Novel Knowledge Graph-Based RAG Framework for Medical QA

Large Language Models (LLMs) have shown strong abilities in language understanding and reasoning, drawing increasing attention in medical question answering (QA). While retrieval-augmented generation (RAG) methods improve factual accuracy, existing approaches still struggle to retrieve and organize relevant knowledge effectively. To overcome this, we propose NaviPath, a knowledge graph-based RAG framework for medical QA. It enhances LLM responses through a structured prompt built in three steps: (1) extended entity retrieval, (2) multiperspective reasoning path construction, and (3) natural language transformation for better comprehension. Experiments on two medical QA benchmarks show that NaviPath achieves state-of-the-art performance in diagnostic accuracy and factual consistency. The implementation is available at https://github.com/zyr319/Navipath.git.