AMG-RAG
Reduces manual knowledge graph maintenance and improves QA accuracy in regulated medical domains by automating graph construction, integrating reasoning, and…
AMG-RAG (Agentic Medical Graph-RAG) is a comprehensive framework that automates the construction and continuous updating of Medical Knowledge Graphs (MKGs), integrates reasoning, and retrieves current external evidence for medical Question Answering (QA).
- Generate medical knowledge graphs automatically from clinical literature and research documents.
- Ask Claude to answer medical questions using current external evidence and graph-based reasoning.
- Automate continuous updates to medical knowledge bases as new research and clinical data emerge.
Reduces manual knowledge graph maintenance and improves QA accuracy in regulated medical domains by automating graph construction, integrating reasoning, and surfacing current evidence—critical for compliance and clinical decision support.
Healthcare software teams and clinical informatics consultants building QA systems requiring current, traceable medical evidence.
https://github.com/MrRezaeiUofT/AMG-RAG
By MrRezaeiUofT
How to Get It
claude plugins install MrRezaeiUofT/AMG-RAG
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Help me generate medical knowledge graphs automatically from clinical literature and research documents
Trust Signals Auto-scanned
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 42 GitHub stars; contributors unknown; last commit 8d ago; license no license.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
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Evaluation
Scored from trust signals (evidence-eval-v1): 42 GitHub stars; contributors unknown; last commit 8d ago; license no license.