AMG-RAG is a breakthrough framework that automatically constructs and updates medical knowledge graphs to improve LLM performance in healthcare applications.

• Creates self-updating medical knowledge bases that adapt to new research
• Enhances LLM accuracy on medical questions through specialized retrieval mechanisms
• Demonstrates significant performance improvements on medical benchmarks (MEDQA, MEDMCQA)
• Reduces the need for costly manual updates of medical knowledge resources

This innovation addresses a critical challenge in healthcare AI: keeping pace with rapidly evolving medical knowledge while maintaining factual accuracy in LLM responses. The approach enables more reliable clinical decision support systems without constant human intervention.

Adaptive Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge