Medical artificial intelligence systems require comprehensive knowledge integration to provide safe and reliable clinical decision support; however, many existing single-source RAG approaches offer limited coverage and may introduce bias. Here, we present MultiMed-RAG, a multi-agent framework that synthesizes heterogeneous medical knowledge from structured graphs, curated textual databases, web resources, and LLM internal knowledge to generate evidence-grounded clinical responses. The framework orchestrates specialized agents that decompose complex queries, dynamically select optimal knowledge sources, validate retrieved evidence against safety and accuracy criteria, and generate responses with transparent source attribution. Across nine medical benchmarks spanning diverse tasks, MultiMed-RAG achieves state-of-the-art performance, demonstrating consistent improvements of 4–7% over direct generation and 2–4% over existing RAG methods. Qualitative evaluation further indicates superior adherence to clinical safety, trustworthiness, actionability, and responsibility (STAR) principles. By addressing key challenges of knowledge heterogeneity, query complexity, and evidence transparency, MultiMed-RAG establishes a principled pathway toward trustworthy AI-assisted clinical decision support systems.
Lu et al. (Mon,) studied this question.