HeartAgent: An Autonomous Agent System for Explainable Differential Diagnosis in Cardiology

Shuang Zhou; Kai Yu; Song Wang; Wenya Xie; Zaifu Zhan; Meng-Han Tsai; Yuen-Hei Chung; Shutong Hou

cs.CLMar 11, 2026

HeartAgent: An Autonomous Agent System for Explainable Differential Diagnosis in Cardiology

Authors: Shuang Zhou, Kai Yu, Song Wang, Wenya Xie, Zaifu Zhan, Meng-Han Tsai, Yuen-Hei Chung, Shutong Hou,

Organizations: Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN, USA · Department of Computer Science, University of Central Florida, Orlando, FL, USA · College of Science and Engineering, University of Minnesota, Minneapolis, MN, USA · Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA · Division of Cardiothoracic Surgery, Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora, CO, USA · Division of Cardiac Electrophysiology, University of California San Francisco, San Francisco, CA, USA · School of Dentistry, University of Minnesota, Minneapolis, MN, USA · Division of Cardiology, Department of Medicine, University of Minnesota, Minneapolis, MN, USA · Lillehei Heart Institute and Department of Medicine, University of Minnesota, Minneapolis, MN, USA

Abstract

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.

Clinical Decision Support

Clinical Decision Support uses data, medical knowledge, and AI to help clinicians assess patients and choose tests, diagnoses, and treatments.

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Apr 5, 2026J/K move · Enter open · S save

BAAI Cardiac Agent: An intelligent multimodal agent for automated reasoning and diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging

Taiping Qu, Hongkai Zhang, Lantian Zhang, Can Zhao+17

Beijing Academy of Artificial Intelligence, No. 150 Chengfu Road, Haidian District, Beijing 100084, China · Department of Radiology, Beijing Anzhen Hospital, Beijing Institute of Heart, Lung & Vascular Diseases, Capital Medical University, 2 Anzhen Road, Beijing 100029, China · Department of MR, the First Affiliated Hospital, Henan Medical University, 88 Jiankang Road, Weihui 453100, China+2

Medical Diagnosis Cardiac MRI Agentic AI Radiology Report Generation Medical Image Segmentation

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Cardiac magnetic resonance (CMR) is a cornerstone for diagnosing cardiovascular disease. However, it remains underutilized due to complex, time-consuming interpretation across multi-sequences, phases, quantitative measures that heavily reliant on specialized expertise. Here, we present BAAI Cardiac Agent, a multimodal intelligent system designed for end-to-end CMR interpretation. The agent integrates specialized cardiac expert models to perform automated segmentation of cardiac structures, functional quantification, tissue characterization and disease diagnosis, and generates structured clinical reports within a unified workflow. Evaluated on CMR datasets from two hospitals (2413 patients) spanning 7-types of major cardiovascular diseases, the agent achieved an area under the receiver-operating-characteristic curve exceeding 0.93 internally and 0.81 externally. In the task of estimating left ventricular function indices, the results generated by this system for core parameters such as ejection fraction, stroke volume, and left ventricular mass are highly consistent with clinical reports, with Pearson correlation coefficients all exceeding 0.90. The agent outperformed state-of-the-art models in segmentation and diagnostic tasks, and generated clinical reports showing high concordance with expert radiologists (six readers across three experience levels). By dynamically orchestrating expert models for coordinated multimodal analysis, this agent framework enables accurate, efficient CMR interpretation and highlights its potentials for complex clinical imaging workflows. Code is available at https://github.com/plantain-herb/Cardiac-Agent.

Abstract

Clinical Decision Support

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