Summary of Mmed-rag: Versatile Multimodal Rag System For Medical Vision Language Models, by Peng Xia et al.
MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models
by Peng Xia, Kangyu Zhu, Haoran Li, Tianze Wang, Weijia Shi, Sheng Wang, Linjun Zhang, James Zou, Huaxiu Yao
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent advance in Medical Large Vision-Language Models (Med-LVLMs) has led to the development of interactive diagnostic tools in healthcare. However, these models often suffer from factual hallucination, which can lead to incorrect diagnoses. To address this issue, researchers have proposed fine-tuning and retrieval-augmented generation (RAG) methods. The effectiveness of RAG-based approaches is limited by the availability of high-quality data and distribution shifts between training and deployment data. In response, a new approach called MMed-RAG has been developed to enhance the factuality of Med-LVLMs. This system introduces domain-aware retrieval mechanisms, adaptive retrieved contexts selection methods, and provable RAG-based preference fine-tuning strategies. Experimental results across five medical datasets demonstrate that MMed-RAG can achieve an average improvement of 43.8% in the factual accuracy of Med-LVLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial Intelligence (AI) is helping doctors diagnose diseases more accurately. A type of AI called Medical Large Vision-Language Models (Med-LVLMs) is being used to make decisions about treatment plans. However, these models sometimes get information wrong. To fix this problem, experts have developed new ways to train Med-LVLMs using retrieval-augmented generation (RAG). But there’s still a challenge: finding enough good data and making sure the AI works well in different situations. Researchers have created a new system called MMed-RAG that can solve these problems. It uses special tools to find the right information, select relevant details, and improve how the AI makes decisions. Tests on five medical datasets showed that MMed-RAG helped Med-LVLMs get more accurate diagnoses. |
Keywords
» Artificial intelligence » Fine tuning » Hallucination » Rag » Retrieval augmented generation
