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| Hauptverfasser: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2603.10764 |
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| _version_ | 1866911506127388672 |
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| author | Zhou, Shuang Yu, Kai Wang, Song Xie, Wenya Zhan, Zaifu Tsai, Meng-Han Chung, Yuen-Hei Hou, Shutong Zhou, Huixue Zeng, Min Ramu, Bhavadharini Chen, Lin Yee Xie, Feng Zhang, Rui |
| author_facet | Zhou, Shuang Yu, Kai Wang, Song Xie, Wenya Zhan, Zaifu Tsai, Meng-Han Chung, Yuen-Hei Hou, Shutong Zhou, Huixue Zeng, Min Ramu, Bhavadharini Chen, Lin Yee Xie, Feng Zhang, Rui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_10764 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HeartAgent: An Autonomous Agent System for Explainable Differential Diagnosis in Cardiology Zhou, Shuang Yu, Kai Wang, Song Xie, Wenya Zhan, Zaifu Tsai, Meng-Han Chung, Yuen-Hei Hou, Shutong Zhou, Huixue Zeng, Min Ramu, Bhavadharini Chen, Lin Yee Xie, Feng Zhang, Rui Computation and Language 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. |
| title | HeartAgent: An Autonomous Agent System for Explainable Differential Diagnosis in Cardiology |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2603.10764 |