RAG Frameworks · ImprintLab

Medical-Graph-RAG

Medical-Graph-RAG is a Python framework that combines graph-based retrieval-augmented generation (RAG) with large language models to answer medical questions using structured knowledge graphs. It organizes medical data in three layers—patient records, academic literature, and medical terminology—to provide evidence-based answers while mitigating LLM hallucination.

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Source: GitHub — github.com/ImprintLab/Medical-Graph-RAG

812

GitHub stars

143

Forks

Python

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	ImprintLab/Medical-Graph-RAG
Owner	ImprintLab
Primary language	Python
License	MIT — OSI-approved
Stars	812
Forks	143
Open issues	19
Latest release	Unknown
Last updated	2025-10-18
Source	https://github.com/ImprintLab/Medical-Graph-RAG

What Medical-Graph-RAG is

A Graph RAG system built on Neo4j that constructs multi-level knowledge graphs from clinical data (MIMIC-IV), scientific papers (S2ORC), and standardized medical ontologies (UMLS), then uses LLM-based graph traversal and retrieval to ground medical QA. Requires external LLM (OpenAI API) and Neo4j infrastructure for production deployment.

Quickstart

Get the Medical-Graph-RAG source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/ImprintLab/Medical-Graph-RAG.git2cd Medical-Graph-RAG3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Evidence-Based Clinical Decision Support

Ground LLM responses in structured medical knowledge graphs to provide clinicians with literature-backed answers to diagnostic and treatment questions, reducing hallucination risk in high-stakes settings.

Medical Research Literature Discovery

Traverse multi-level graphs linking patient cohorts, academic papers, and medical ontologies to surface relevant evidence for systematic reviews, meta-analyses, or hypothesis generation.

Patient-Facing Health Information Systems

Build accessible Q&A systems that explain medical concepts by linking patient-relevant data, textbook definitions, and peer-reviewed evidence, improving transparency and trust.

Implementation considerations

Data licensing is a major blocker: MIMIC-IV requires credentialing; medical textbooks must be purchased; UMLS requires free account & approval. Budget time and legal review upfront.
Graph construction from unstructured text (especially medium-level papers) relies on LLM-based entity/relation extraction, which is noisy; non-learning baselines are noted as faster/cheaper but noisier—trade-off must be evaluated per use case.
Requires local Neo4j deployment and maintenance; OpenAI API keys are hardcoded in examples—substitute with self-hosted LLM (Llama, etc.) to reduce operational costs and latency in production.
No official release tags; latest push was Oct 2025. Check GitHub issues (19 open) and paper references before committing; academic codebases often lack production-grade error handling and monitoring.
Example dataset (mimic_ex) is provided, but reproducing full paper results requires significant data engineering; conda environment file exists but external dependencies (Neo4j, LLM) must be separately provisioned.

When to avoid it — and what to weigh

Real-Time, Ultra-Low-Latency Requirements — Graph construction, LLM querying, and Neo4j traversal introduce non-trivial latency; unsuitable for millisecond-critical systems or high-frequency inference on resource-constrained devices.
Small or Highly Proprietary Medical Datasets — The system is optimized for large multi-level datasets (MIMIC-IV, academic corpora, UMLS); small private datasets may not justify the complexity or provide sufficient signal for graph construction.
Fully Offline or Air-Gapped Deployment — Current demo relies on PubMed web searches and OpenAI API; local deployment requires pre-ingested licensed medical literature and considerable infrastructure (Neo4j, LLM service), creating licensing and operational friction.
Strict Regulatory Compliance Without Legal Review — Medical use requires careful validation under HIPAA, FDA MDR, or equivalent; this framework alone does not constitute a validated medical device and requires substantial additional compliance work.

License & commercial use

MIT License (permissive). Allows commercial use, modification, and distribution with attribution and no warranty. However, medical data sources (MIMIC-IV, textbooks, UMLS) have separate, more restrictive licenses and credentialing requirements that may limit commercial deployment.

MIT permits commercial software use. However, deploying this system commercially requires navigating strict licensing for medical datasets (MIMIC-IV credentialing, textbook licensing, UMLS agreement), plus regulatory validation (FDA/HIPAA) before any medical claims. Requires legal and compliance review before marketing as a medical product.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	High
DEV.co fit	Good
Assessment confidence	Medium

Security considerations

Handles HIPAA-sensitive data (MIMIC-IV) and requires API keys; no discussion of PII redaction, audit logging, or encryption in transit/at rest. Environment variables in code examples are a security anti-pattern. Needs threat modeling, role-based access control on Neo4j, and compliance review before handling real patient data. No security advisories or CVE history provided.

Alternatives to consider

LangChain + LlamaIndex + Knowledge Graph RAG

Wider integration ecosystem, more mature community, better documentation for production RAG. Requires custom graph construction but greater flexibility and vendor independence.

GraphRAG (Microsoft Research)

General-purpose graph RAG with strong engineering; less medical-specific but may be easier to adapt. No licensing restrictions on medical data; may require more custom domain work.

UpToDate, DynaMed, or proprietary medical QA SaaS

Pre-built, clinically validated, and regulatory-compliant alternatives for clinical decision support. Higher cost but eliminates engineering and compliance risk; preferable if budget allows.

Software development agency

Build on Medical-Graph-RAG with DEV.co software developers

Medical-Graph-RAG offers a research-backed approach to grounding medical LLMs in evidence, but requires significant data licensing and regulatory planning. Our team can help assess fit, architect production deployments, and navigate compliance. Get a consultation today.

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Medical-Graph-RAG FAQ

Can I use this in production without OpenAI?

Yes, but requires engineering effort. Substitute OpenAI API with a self-hosted LLM (Llama, Mistral, etc.); LLM calls are abstracted but code assumes OpenAI format. Test thoroughly with your LLM.

What is the total cost to set up and run?

Data licensing (MIMIC-IV credentialing, medical textbooks, UMLS account) is free or low-cost but time-intensive. Infrastructure (Neo4j, LLM service or self-hosted server, GPU if local) varies; OpenAI API usage scales with query volume. Requires budget estimate per organization.

Is this FDA-approved or clinically validated?

No. This is a research framework published at ACL 2025. Using it for medical diagnosis or treatment requires independent validation, regulatory submissions, and clinical trials. Do not deploy as a medical device without legal and clinical review.

How do I handle the medical licensing wall?

MIMIC-IV requires credentialing at PhysioNet. Medical textbooks must be purchased separately and processed locally (raw content not released). UMLS is free but requires account. For proprietary literature, negotiate directly with publishers or use web-based APIs (PubMed, arXiv) as shown in the demo.

Software developers & web developers for hire

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If Medical-Graph-RAG is part of your rag frameworks roadmap, our team can implement, customize, migrate, and maintain it.

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