Retrieval-augmented generation in biomedicine: a survey of technologies, datasets, and clinical applications

Large language models (LLMs) in biomedicine face a fundamental conflict between static parameter knowledge and the dynamic nature of clinical evidence. Retrieval-augmented generation (RAG) addresses this by grounding generation in external data, yet it introduces new complexities in latency and architecture. This survey synthesizes the biomedical RAG landscape from 2020 to 2025 through a structured literature mapping of 178 screened records, yielding 94 representative studies classified into naive RAG (38 studies, 40.4%), advanced RAG (46 studies, 48.9%), and modular RAG (10 studies, 10.7%). Beyond a technological taxonomy, we formalize the biomedical RAG trilemma, identifying the inherent trade-offs between reasoning depth, inference latency, and data privacy that constrain current clinical deployment. We analyze how recent agentic workflows enhance diagnostic reasoning but risk prohibitive latency, and how privacy constraints dictate the choice between powerful cloud-based models and local deployment. Finally, we outline the alignment gap in multimodal RAG and propose future directions for self-correcting, verifiable clinical agents.