MED-COPILOT: A Medical Assistant Powered by GraphRAG and Similar Patient Case Retrieval

2026-02-27

AI Summary

This work proposes an interactive clinical decision support system that addresses the challenge of integrating heterogeneous evidence—ranging from structured clinical guidelines to real-world patient records—while mitigating the hallucination risks inherent in large language models when processing lengthy, structured medical documents. The system uniquely combines community-level knowledge graph summaries, a structured knowledge graph derived from WHO/NICE guidelines, and 36,000 standardized SOAP-format patient cases. It leverages GraphRAG alongside a hybrid semantic-keyword retrieval mechanism to enable synergistic reasoning between guideline-based knowledge and analogical case evidence. Evaluated on clinical note generation and medical question answering tasks, the system significantly outperforms both parametric large language models and conventional RAG approaches, enhancing factual fidelity and clinical reasoning accuracy while offering an interpretable interface for evidence tracing.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Clinical decision-making requires synthesizing heterogeneous evidence, including patient histories, clinical guidelines, and trajectories of comparable cases. While large language models (LLMs) offer strong reasoning capabilities, they remain prone to hallucinations and struggle to integrate long, structured medical documents. We present MED-COPILOT, an interactive clinical decision-support system designed for clinicians and medical trainees, which combines guideline-grounded GraphRAG retrieval with hybrid semantic-keyword similar-patient retrieval to support transparent and evidence-aware clinical reasoning. The system builds a structured knowledge graph from WHO and NICE guidelines, applies community-level summarization for efficient retrieval, and maintains a 36,000-case similar-patient database derived from SOAP-normalized MIMIC-IV notes and Synthea-generated records. We evaluate our framework on clinical note completion and medical question answering, and demonstrate that it consistently outperforms parametric LLM baselines and standard RAG, improving both generation fidelity and clinical reasoning accuracy. The full system is available at https://huggingface.co/spaces/Cryo3978/Med_GraphRAG , enabling users to inspect retrieved evidence, visualize token-level similarity contributions, and conduct guided follow-up analysis. Our results demonstrate a practical and interpretable approach to integrating structured guideline knowledge with patient-level analogical evidence for clinical LLMs.

Problem

• clinical decision-making
• large language models
• hallucinations
• structured medical documents
• evidence integration

Innovation

• GraphRAG
• similar patient retrieval
• clinical decision support
• knowledge graph
• evidence-aware reasoning