A Multi-Agent RAG Framework for Biomedical Literature Analysis (opens in new tab)

Background: The biomedical literature is expanding at an unprecedented rate, with over 4,000 new articles indexed on PubMed each day. Clinicians and researchers frequently lack the time to review this volume before making decisions. Retrieval-Augmented Generation (RAG) systems attempt to bridge this gap by grounding language model responses in relevant documents, but standard implementations rank all retrieved passages solely by semantic similarity, treating a case report and a meta-analysis ...