MMed-RAG addresses the critical challenge of factual hallucinations in Medical Vision Language Models by implementing a specialized retrieval-augmented generation system.

• Integrates seamlessly across multiple medical domains including radiology, ophthalmology, and pathology
• Significantly reduces hallucination rates in medical visual question answering tasks
• Employs a two-stage retrieval approach that combines knowledge from both medical images and text sources
• Demonstrates improved diagnostic accuracy without requiring extensive model fine-tuning

This research is crucial for healthcare applications where diagnostic errors can have serious consequences, potentially enabling more reliable AI-assisted medical analysis systems that doctors can trust.

MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models