Medical Graph RAG: Evidence-based Medical Large Language Model via Graph Retrieval-Augmented Generation
Wu, Junde Zhu, Jiayuan Qi, Yunli Chen, Jingkun Xu, Min Menolascina, Filippo Jin, Yueming Grau, Vicente
Wu, Junde
Zhu, Jiayuan
Qi, Yunli
Chen, Jingkun
Xu, Min
Menolascina, Filippo
Jin, Yueming
Grau, Vicente
Supervisor
Department
Computer Vision
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Type
Conference proceeding
Date
2025
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Language
English
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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.
Citation
J. Wu et al., “Medical Graph RAG: Evidence-based Medical Large Language Model via Graph Retrieval-Augmented Generation,” 2025. [Online]. Available: https://aclanthology.org/2025.acl-long.1381/
Source
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics
Conference
63rd Annual Meeting of the Association for Computational Linguistics, 2025
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Source
63rd Annual Meeting of the Association for Computational Linguistics, 2025
Publisher
Association for Computational Linguistics