This survey explores how Retrieval-Augmented Generation (RAG) is revolutionizing visual understanding by connecting AI models to external knowledge sources.

• RAG enhances vision models by providing access to reliable, up-to-date information
• Applications extend beyond text into the visual domain for more comprehensive understanding
• Significantly improves AI-generated content quality through supplementary information
• Shows particular promise in medical applications for improving diagnostic report generation and clinical decision support

For healthcare organizations, this research offers pathways to more accurate, contextually-informed visual analysis systems that can leverage existing medical knowledge bases while maintaining data reliability.

Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook