richard-peng-xia/RULE
[EMNLP'24] RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models
This project helps medical professionals and researchers improve the factual accuracy of AI models that answer questions based on medical images and related text. You provide medical images (like X-rays) and questions about them, and it produces more reliable answers by ensuring the AI's responses align with established medical facts. Medical AI researchers and practitioners focused on diagnostic support or medical education would use this.
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Use this if you are developing or using AI models for medical imaging tasks and need to ensure their responses are factually accurate and trustworthy.
Not ideal if your primary goal is general image analysis outside of the medical domain, or if you don't require high-stakes factual accuracy from your vision-language models.
Stars
98
Forks
5
Language
Python
License
MIT
Category
Last pushed
Dec 13, 2024
Commits (30d)
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