Summary of Magda: Multi-agent Guideline-driven Diagnostic Assistance, by David Bani-harouni et al.
MAGDA: Multi-agent guideline-driven diagnostic assistance
by David Bani-Harouni, Nassir Navab, Matthias Keicher
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The proposed approach combines Large Language Models (LLMs) with a contrastive vision-language model to provide zero-shot guideline-driven decision support in emergency departments, rural hospitals, or clinics. The LLM agents are augmented to follow medical guidelines and synthesize prompts for image analysis. The system provides understandable chain-of-thought reasoning for diagnoses and can refine its decisions considering inter-dependencies between diseases. This approach is adaptable to settings with rare diseases where training data is limited. The evaluation on two chest X-ray datasets, CheXpert and ChestX-ray 14 Longtail, shows performance improvement over existing zero-shot methods and generalizability to rare diseases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In emergency departments or clinics in less developed regions, clinicians often struggle without access to fast image analysis by trained radiologists. Large Language Models can provide insights that help clinicians make decisions. However, these models don’t always follow medical guidelines. This new approach uses multiple LLM agents and a contrastive vision-language model to reach patient diagnoses. The system follows simple diagnostic guidelines and provides understandable reasoning for its diagnosis. |
Keywords
» Artificial intelligence » Language model » Zero shot
