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arXiv · 2610.32258

MedCaseAgent: From Multimodal Clinical Evidence to Rare Disease Case Reports

Hongkang Chu·Yifan Shen·Tianjiao Yu·Zhuoqing Zhong·Jianguo Cao·Meihuan Huang·Xu Cao
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Abstract

Although Large Language Models (LLMs) have demonstrated potential in clinical documentation, generating the rigorous, evidence-based reasoning required for unprecedented, atypical, and rare disease case reports remains a significant challenge. We present MedCaseAgent, an end-to-end agentic reasoning paradigm that bridges this gap by synthesizing publication-quality reports from unstructured clinical notes. MedCaseAgent leverages a hierarchical structure, integrating patient-level data mining with iterative, tool-integrated reasoning. By dynamically querying medical literature and knowledge bases, the agent maintains high clinical fidelity and assesses case novelty. The framework is inherently modular, supporting various backbone LLMs in a plug-and-play fashion. To facilitate this research, we release MedCase-150K, a large-scale multimodal clinical case-report corpus, and MedCase-Bench, a robust temporally held-out evaluation suite. We also introduce an open-source model post-trained from Qwen3.5-9B and optimized specifically for this task. Under both LLM-judged and rule-based evaluation, MedCaseAgent outperforms existing general-purpose and specialized medical LLMs and agents. Blinded expert reviews and ablations further support the system's clinical relevance and evidence grounding.

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Hongkang Chu, Yifan Shen, Tianjiao Yu, Zhuoqing Zhong, Jianguo Cao, Meihuan Huang, Xu Cao. 2026-10-23. MedCaseAgent: From Multimodal Clinical Evidence to Rare Disease Case Reports. https://arxiv.org/abs/2610.32258

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