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

Shuheng Chen, Namratha Patil, Haonan Pan, Angel Hsing-Chi Hwang, Yao Du, Ruishan Liu, Jieyu Zhao

TL;DR

MED-COPILOT is presented, 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.

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.

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

TL;DR

MED-COPILOT is presented, 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.

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.
Paper Structure (32 sections, 8 figures, 2 tables)

This paper contains 32 sections, 8 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Retrieval-Augmented Clinical Reasoning
  4. Similar-Patient Retrieval
  5. Methodology
  6. System Overview
  7. Graph-Augmented Guideline Retrieval
  8. Guideline Sources.
  9. Graph Construction.
  10. Indexing.
  11. Similar-Patient Retrieval
  12. Patient Data Sources.
  13. Hybrid Similarity Scoring.
  14. Unified Retrieval Workflow
  15. Evaluation and Demonstration
  16. ...and 17 more sections

Figures (8)

  • Figure 1: Overview of the proposed system, which performs dual evidence retrieval from similar patient cases and graph-structured clinical guidelines to support clinical inference.
  • Figure 2: Interactive question answering with dual evidence control.
  • Figure 3: Similar-patient panel with query-conditioned saliency highlighting. Clinical concepts are color-coded according to their semantic relevance to the input query (yellow: important; red: highly important).
  • Figure 4: Patient case input and query locking interface.
  • Figure 5: Retrieval of the most similar patient case from the curated repository.
  • ...and 3 more figures