Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation
<|reference_start|>Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation: We introduce a novel graph-based Retrieval-Augmented Generation (RAG) framework specifically designed for the medical domain, called \textbf{MedGraphRAG}, aimed at enhancing Large Language Model (LLM) capabilities and generating evidence-based results, thereby improving safety and reliability when handling private medical data. Our comprehensive pipeline begins with a hybrid static-semantic approach to document chunking, significantly improving context capture over traditional methods. Extracted entities are used to create a three-tier hierarchical graph structure, linking entities to foundational medical knowledge sourced from medical papers and dictionaries. These entities are then interconnected to form meta-graphs, which are merged based on semantic similarities to develop a comprehensive global graph. This structure supports precise information retrieval and response generation. The retrieval process employs a U-retrieve method to balance global awareness and indexing efficiency of the LLM. Our approach is validated through a comprehensive ablation study comparing various methods for document chunking, graph construction, and information retrieval. The results not only demonstrate that our hierarchical graph construction method consistently outperforms state-of-the-art models on multiple medical Q\&A benchmarks, but also confirms that the responses generated include source documentation, significantly enhancing the reliability of medical LLMs in practical applications. Code will be at: https://github.com/MedicineToken/Medical-Graph-RAG/tree/main<|reference_end|>
@article{wu2024medical,
    title={Medical Graph RAG: Towards Safe Medical Large Language Model via Graph
  Retrieval-Augmented Generation},
    author={Junde Wu, Jiayuan Zhu, Yunli Qi, Jingkun Chen, Min Xu, Filippo
  Menolascina, Vicente Grau},
    journal={arXiv preprint arXiv:2408.04187},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04187},
    primaryClass={cs.CV}
}