Medical Graph RAG: Evidence-based Medical Large Language Model via Graph Retrieval-Augmented Generation
Junde Wu, Jiayuan Zhu, Yunli Qi, Jingkun Chen, Min Xu, Filippo Menolascina, Yueming Jin, Vicente Grau
Abstract
We introduce MedGraphRAG, a novel graph-based Retrieval-Augmented Generation (RAG) framework designed to enhance LLMs in generating evidence-based medical responses, improving safety and reliability with private medical data. We introduce Triple Graph Construction and U-Retrieval to enhance GraphRAG, enabling holistic insights and evidence-based response generation for medical applications. Specifically, we connect user documents to credible medical sources and integrate Top-down Precise Retrieval with Bottom-up Response Refinement for balanced context awareness and precise indexing. Validated on 9 medical Q&A benchmarks, 2 health fact-checking datasets, and a long-form generation test set, MedGraphRAG outperforms state-of-the-art models while ensuring credible sourcing. Our code is publicly available.
BibTeX
@inproceedings{wu-etal-2025-medical,
title = "Medical Graph {RAG}: Evidence-based Medical Large Language Model via Graph Retrieval-Augmented Generation",
author = "Wu, Junde and
Zhu, Jiayuan and
Qi, Yunli and
Chen, Jingkun and
Xu, Min and
Menolascina, Filippo and
Jin, Yueming and
Grau, Vicente",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1381/",
doi = "10.18653/v1/2025.acl-long.1381",
pages = "28443--28467",
ISBN = "979-8-89176-251-0"
}