HeartAgent: An Autonomous Agent System for Explainable Differential Diagnosis in Cardiology
🤖 AI Summary
This work proposes the first multi-agent system that deeply integrates clinical knowledge to address the limitations of existing AI approaches in cardiology diagnosis—namely, insufficient domain knowledge, weak reasoning capabilities, and poor interpretability. By orchestrating multiple specialized sub-agents, the system synergistically combines domain-specific tools with structured electronic health records to enable traceable and verifiable differential diagnostic reasoning. It innovatively generates transparent reasoning trajectories and auditable evidence, substantially enhancing both diagnostic performance and trustworthiness. Evaluated on the MIMIC and a private EHR dataset, the system improves top-3 diagnostic accuracy by 36% and 20%, respectively. Furthermore, when assisting clinicians, it increases diagnostic accuracy by 26.9% and explanation quality by 22.7%.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
differential diagnosis
cardiology
explainable AI
clinical decision support
interpretability
Innovation
Methods, ideas, or system contributions that make the work stand out.
autonomous agent
explainable AI
differential diagnosis
cardiology
multi-agent reasoning
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