心脏疾病诊断新工具，能自动推理并给出可解释的结论。
HeartAgent: An Autonomous Agent System for Explainable Differential Diagnosis in Cardiology
- 用多个专业子代理协同分析病历，生成透明推理过程。
- 在两个数据集上诊断准确率比现有方法高20%至36%。
- 医生使用后诊断准确率提升26.9%，解释质量提高22.7%。
心脏病仍是全球致病和致死的主要原因，亟需准确且可信的鉴别诊断。然而，现有的基于人工智能的诊断方法常受限于心血管知识不足、复杂推理支持不够以及可解释性差。本文提出HeartAgent，一种专为心脏病学设计的自主代理系统，旨在实现可靠且可解释的鉴别诊断。HeartAgent整合定制化工具与精选数据资源，协调多个专业化子代理进行复杂推理，同时生成透明的推理路径和可验证的参考依据。在MIMIC数据集和一个私有电子病历队列上的评估显示，HeartAgent在顶3诊断准确率上分别较现有方法提升超过36%和20%。此外，接受HeartAgent辅助的临床医生相比独立判断的专家，诊断准确率提升26.9%，解释质量提高22.7%。结果表明，HeartAgent为心血管诊疗提供了可靠、可解释且临床可用的决策支持。
原文摘要 · Abstract (English)
Heart diseases remain a leading cause of morbidity and mortality worldwide, necessitating accurate and trustworthy differential diagnosis. However, existing artificial intelligence-based diagnostic methods are often limited by insufficient cardiology knowledge, inadequate support for complex reasoning, and poor interpretability. Here we present HeartAgent, a cardiology-specific agent system designed to support a reliable and explainable differential diagnosis. HeartAgent integrates customized tools and curated data resources and orchestrates multiple specialized sub-agents to perform complex reasoning while generating transparent reasoning trajectories and verifiable supporting references. Evaluated on the MIMIC dataset and a private electronic health records cohort, HeartAgent achieved over 36% and 20% improvements over established comparative methods, in top-3 diagnostic accuracy, respectively. Additionally, clinicians assisted by HeartAgent demonstrated gains of 26.9% in diagnostic accuracy and 22.7% in explanatory quality compared with unaided experts. These results demonstrate that HeartAgent provides reliable, explainable, and clinically actionable decision support for cardiovascular care.
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