A survey on medical multimodal retrieval-augmented generation (RAG)
Ruipeng Wang, Jian Wang, Yan Huang, Haiyang Guo, Junjie Pang · High-Confidence Computing · 2026
Large Language Models (LLMs) have demonstrated immense potential in the medical field, yet they face inherent limitations such as hallucination and knowledge obsolescence. Although retrieval-augmented generation (RAG) effectively mitigates these issues by incorporating external knowledge, traditional approaches are primarily limited to textual data, failing to leverage the rich multimodal nature of clinical information, which potentially compromises diagnostic accuracy. Multimodal RAG extends information processing capabilities to encompass diverse data types, thus overcoming the limitations of its text-only counterpart. This paper aims to provide a comprehensive survey of medical Multimodal RAG to facilitate a thorough understanding of its current landscape and future potential. Specifically, we introduce its related technical foundations, review the various types of multimodal data in the medical field, analyze its core challenges and corresponding solutions, explore the diverse applications and representative works of medical Multimodal RAG, and conclude by outlining evaluation methods and future research directions.