<ul data-eligibleForWebStory="false"><li>Medical diagnosis prediction is crucial for disease detection and personalized healthcare.</li><li>Machine learning models face limitations in generalizing to unseen cases due to their reliance on supervised training and the need for large labeled datasets.</li><li>A new approach called KERAP, a knowledge graph-enhanced reasoning method, addresses challenges faced by large language models in diagnosis prediction by using a multi-agent architecture.</li><li>Experimental results show that KERAP enhances diagnostic reliability and offers a scalable and interpretable solution for zero-shot medical diagnosis prediction.</li></ul>

KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs

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