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AI Research
arXiv CS.AI·May 28·6 min read
C-MIG: Multi-view Information Gain-based Retrieval-Augmented Generation for Clinical Diagnosis Reasoning
TL;DR
Clinical AI systems struggle to explain diagnostic reasoning and often retrieve irrelevant medical information. C-MIG (Multi-view Information Gain-based Retrieval-Augmented Generation) combines multiple perspectives to intelligently select the most useful medical documents before generating diagnoses.
✦ Why It Matters
Engineers building clinical AI can adopt multi-view information filtering to improve both accuracy and explainability of medical recommendations.
Key Takeaways
- Use information gain from multiple perspectives to filter retrieved documents, not just similarity scores.
- Implement RAG with explicit document relevance ranking to reduce noise in medical knowledge bases.
- Measure both diagnostic accuracy and explanation quality when evaluating clinical AI systems.
- Apply multi-view filtering to improve interpretability alongside predictive performance in healthcare applications.
Full Summary
Clinical diagnosis requires AI systems that not only predict correctly but also explain their reasoning using relevant medical evidence. Retrieval-Augmented Generation (RAG)—a technique that retrieves relevant documents before generating answers—is promising for medicine but often pulls unhelpful or redundant information.
C-MIG addresses this by evaluating retrieved documents from multiple viewpoints (information gain perspectives) to select only the most informative ones for diagnosis reasoning. The system uses these curated documents to generate both diagnostic predictions and human-readable explanations.
By filtering documents through multiple information-gain criteria rather than simple similarity matching, C-MIG reduces noise and improves both diagnostic accuracy and explanation coherence. Results demonstrate measurable improvements in diagnostic performance and explanation quality over standard RAG baselines.