Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2408.04187v2
• Date: Tue, 15 Oct 2024 17:37:42 GMT
• Title: Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation
• Authors: Junde Wu, Jiayuan Zhu, Yunli Qi, Jingkun Chen, Min Xu, Filippo Menolascina, Vicente Grau,
• Abstract summary: We introduce a novel graph-based Retrieval-Augmented Generation framework specifically designed for the medical domain, called MedGraphRAG. Our approach is validated on 9 medical Q&A benchmarks, 2 health fact-checking benchmarks, and one collected dataset testing long-form generation.
• Score: 9.286509119104563
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 for generating evidence-based medical responses, thereby improving safety and reliability when handling private medical data. Graph-based RAG (GraphRAG) leverages LLMs to organize RAG data into graphs, showing strong potential for gaining holistic insights from long-form documents. However, its standard implementation is overly complex for general use and lacks the ability to generate evidence-based responses, limiting its effectiveness in the medical field. To extend the capabilities of GraphRAG to the medical domain, we propose unique Triple Graph Construction and U-Retrieval techniques over it. In our graph construction, we create a triple-linked structure that connects user documents to credible medical sources and controlled vocabularies. In the retrieval process, we propose U-Retrieval which combines Top-down Precise Retrieval with Bottom-up Response Refinement to balance global context awareness with precise indexing. These effort enable both source information retrieval and comprehensive response generation. Our approach is validated on 9 medical Q\&A benchmarks, 2 health fact-checking benchmarks, and one collected dataset testing long-form generation. The results show that MedGraphRAG consistently outperforms state-of-the-art models across all benchmarks, while also ensuring that responses include credible source documentation and definitions. Our code is released at: https://github.com/MedicineToken/Medical-Graph-RAG.

Related papers

• MedGemma Technical Report [75.88152277443179]
  We introduce MedGemma, a collection of medical vision-language foundation models based on Gemma 3 4B and 27B.<n>MedGemma demonstrates advanced medical understanding and reasoning on images and text.<n>We additionally introduce MedSigLIP, a medically-tuned vision encoder derived from SigLIP.
  arXiv  Detail & Related papers  (2025-07-07T17:01:44Z)
• When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation [25.508719115522645]
  Graph retrieval-augmented generation (GraphRAG) has emerged as a powerful paradigm for enhancing large language models (LLMs) with external knowledge.<n>Recent studies report that GraphRAG frequently underperforms vanilla RAG on many real-world tasks.<n>This raises a critical question: Is GraphRAG really effective, and in which scenarios do graph structures provide measurable benefits for RAG systems?
  arXiv  Detail & Related papers  (2025-06-06T02:37:47Z)
• DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains [13.63225871556018]
  Knowledge graph completion (KGC) aims to predict missing triples in knowledge graphs (KGs) by leveraging existing triples and textual information.<n>DrKGC employs a flexible lightweight model training strategy to learn structural embeddings and logical rules within the KG.<n>It then leverages a novel bottom-up graph retrieval method to extract a subgraph for each query guided by the learned rules.
  arXiv  Detail & Related papers  (2025-05-31T20:56:54Z)
• Align-GRAG: Reasoning-Guided Dual Alignment for Graph Retrieval-Augmented Generation [75.9865035064794]
  Large language models (LLMs) have demonstrated remarkable capabilities, but still struggle with issues like hallucinations and outdated information.<n>Retrieval-augmented generation (RAG) addresses these issues by grounding LLM outputs in external knowledge with an Information Retrieval (IR) system.<n>We propose Align-GRAG, a novel reasoning-guided dual alignment framework in post-retrieval phrase.
  arXiv  Detail & Related papers  (2025-05-22T05:15:27Z)
• HyperGraphRAG: Retrieval-Augmented Generation via Hypergraph-Structured Knowledge Representation [21.291102413159752]
  We propose HyperGraphRAG, a novel graph-based RAG method that represents n-ary relational facts via hyperedges.<n> Experiments across medicine, agriculture, computer science, and law demonstrate that HyperGraphRAG outperforms both standard RAG and previous graph-based RAG methods in answer accuracy, retrieval efficiency, and generation quality.
  arXiv  Detail & Related papers  (2025-03-27T10:01:16Z)
• Reducing Hallucinations of Medical Multimodal Large Language Models with Visual Retrieval-Augmented Generation [15.468023420115431]
  We show how MLLMs may be enhanced to support Visual RAG, a retrieval-augmented generation framework. On the MIMIC-CXR chest X-ray report generation and Multicare medical image caption generation datasets, we show that Visual RAG improves the accuracy of entity probing.
  arXiv  Detail & Related papers  (2025-02-20T20:55:34Z)
• GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation [84.41557981816077]
  We introduce GFM-RAG, a novel graph foundation model (GFM) for retrieval augmented generation. GFM-RAG is powered by an innovative graph neural network that reasons over graph structure to capture complex query-knowledge relationships. It achieves state-of-the-art performance while maintaining efficiency and alignment with neural scaling laws.
  arXiv  Detail & Related papers  (2025-02-03T07:04:29Z)
• CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs [9.718354494802002]
  Contextualized Graph Retrieval-Augmented Generation (CG-RAG) is a novel framework that integrates sparse and dense retrieval signals within graph structures. First, we propose a contextual graph representation for citation graphs, effectively capturing both explicit and implicit connections within and across documents. Second, we introduce Lexical-Semantic Graph Retrieval (LeSeGR), which seamlessly integrates sparse and dense retrieval signals with graph encoding. Third, we present a context-aware generation strategy that utilizes the retrieved graph-structured information to generate precise and contextually enriched responses.
  arXiv  Detail & Related papers  (2025-01-25T04:18:08Z)
• Retrieval-Augmented Generation with Graphs (GraphRAG) [84.29507404866257]
  Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream task execution by retrieving additional information. Graph, by its intrinsic "nodes connected by edges" nature, encodes massive heterogeneous and relational information. Unlike conventional RAG, the uniqueness of graph-structured data, such as diverse-formatted and domain-specific relational knowledge, poses unique and significant challenges when designing GraphRAG for different domains.
  arXiv  Detail & Related papers  (2024-12-31T06:59:35Z)
• Medical Report Generation Is A Multi-label Classification Problem [38.64929236412092]
  We propose rethinking medical report generation as a multi-label classification problem. We introduce a novel report generation framework based on BLIP integrated with classified key nodes. Our experiments demonstrate that leveraging key nodes can achieve state-of-the-art (SOTA) performance, surpassing existing approaches across two benchmark datasets.
  arXiv  Detail & Related papers  (2024-08-30T20:43:35Z)
• Graph Retrieval-Augmented Generation: A Survey [28.979898837538958]
  Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. This paper provides the first comprehensive overview of GraphRAG methodologies. We formalize the GraphRAG workflow, encompassing Graph-Based Indexing, Graph-Guided Retrieval, and Graph-Enhanced Generation.
  arXiv  Detail & Related papers  (2024-08-15T12:20:24Z)
• RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models [35.60385437194243]
  Current Medical Large Vision Language Models (Med-LVLMs) frequently encounter factual issues. RAG, which utilizes external knowledge, can improve the factual accuracy of these models but introduces two major challenges. We propose RULE, which consists of two components. First, we introduce a provably effective strategy for controlling factuality risk through the selection of retrieved contexts. Second, based on samples where over-reliance on retrieved contexts led to errors, we curate a preference dataset to fine-tune the model.
  arXiv  Detail & Related papers  (2024-07-06T16:45:07Z)
• GRAG: Graph Retrieval-Augmented Generation [14.98084919101233]
  Graph Retrieval-Augmented Generation (GRAG) tackles the fundamental challenges in retrieving textual subgraphs. We propose a novel divide-and-conquer strategy that retrieves the optimal subgraph structure in linear time. Our approach significantly outperforms current state-of-the-art RAG methods.
  arXiv  Detail & Related papers  (2024-05-26T10:11:40Z)
• Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning [65.54680361074882]
  Eye-gaze Guided Multi-modal Alignment (EGMA) framework harnesses eye-gaze data for better alignment of medical visual and textual features. We conduct downstream tasks of image classification and image-text retrieval on four medical datasets.
  arXiv  Detail & Related papers  (2024-03-19T03:59:14Z)
• G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering [61.93058781222079]
  We develop a flexible question-answering framework targeting real-world textual graphs. We introduce the first retrieval-augmented generation (RAG) approach for general textual graphs. G-Retriever performs RAG over a graph by formulating this task as a Prize-Collecting Steiner Tree optimization problem.
  arXiv  Detail & Related papers  (2024-02-12T13:13:04Z)
• A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises [52.31710895034573]
  This work provides the first comprehensive review of healthcare knowledge graphs (HKGs) It summarizes the pipeline and key techniques for HKG construction, as well as the common utilization approaches. At the application level, we delve into the successful integration of HKGs across various health domains.
  arXiv  Detail & Related papers  (2023-06-07T21:51:56Z)
• Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation [92.73584302508907]
  We propose a knowledge graph with Dynamic structure and nodes to facilitate medical report generation with Contrastive Learning. In detail, the fundamental structure of our graph is pre-constructed from general knowledge. Each image feature is integrated with its very own updated graph before being fed into the decoder module for report generation.
  arXiv  Detail & Related papers  (2023-03-18T03:53:43Z)
• An Empirical Study of Retrieval-enhanced Graph Neural Networks [48.99347386689936]
  Graph Neural Networks (GNNs) are effective tools for graph representation learning. We propose a retrieval-enhanced scheme called GRAPHRETRIEVAL, which is agnostic to the choice of graph neural network models. We conduct comprehensive experiments over 13 datasets, and we observe that GRAPHRETRIEVAL is able to reach substantial improvements over existing GNNs.
  arXiv  Detail & Related papers  (2022-06-01T09:59:09Z)
• Learning Contextualized Document Representations for Healthcare Answer Retrieval [68.02029435111193]
  Contextual Discourse Vectors (CDV) is a distributed document representation for efficient answer retrieval from long documents. Our model leverages a dual encoder architecture with hierarchical LSTM layers and multi-task training to encode the position of clinical entities and aspects alongside the document discourse. We show that our generalized model significantly outperforms several state-of-the-art baselines for healthcare passage ranking.
  arXiv  Detail & Related papers  (2020-02-03T15:47:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.