Echo-CoPilot: A Multi-View, Multi-Task Agent for Echocardiography Interpretation and Reporting

• URL: http://arxiv.org/abs/2512.09944v1
• Date: Sat, 06 Dec 2025 23:27:54 GMT
• Title: Echo-CoPilot: A Multi-View, Multi-Task Agent for Echocardiography Interpretation and Reporting
• Authors: Moein Heidari, Mohammad Amin Roohi, Armin Khosravi, Ilker Hacihaliloglu,
• Abstract summary: We introduce Echo-CoPilot, a multi-view, multi-task agent that uses a large language model to orchestrate specialized echocardiography tools.<n>Within a ReAct-style loop, the agent decomposes clinician queries, invokes tools for view recognition, cardiac structure segmentation, measurement and disease prediction, and report synthesis.<n>We evaluate Echo-CoPilot on the public MIMIC-EchoQA benchmark, where it achieves an accuracy of 50.8%, outperforming both general-purpose and biomedical video vision-language models.
• Score: 8.162197738994479
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Echocardiography is central to contemporary cardiovascular care, but full-study interpretation remains a cognitively demanding, multi-view task that is still performed manually. While recent foundation models for echocardiography can achieve strong performance on individual perceptual subtasks such as view classification, segmentation, or disease prediction, they typically operate in isolation and do not provide a unified, clinically coherent assessment. In this work, we introduce Echo-CoPilot, a multi-view, multi-task agent that uses a large language model to orchestrate a suite of specialized echocardiography tools. Within a ReAct-style loop, the agent decomposes clinician queries, invokes tools for view recognition, cardiac structure segmentation, measurement and disease prediction, and report synthesis, and integrates their outputs into guideline-aware answers and narrative summaries. We evaluate Echo-CoPilot on the public MIMIC-EchoQA benchmark, where it achieves an accuracy of 50.8\%, outperforming both general-purpose and biomedical video vision-language models. Qualitative analyses further show that the agent leverages quantitative measurements and physiologic context to resolve challenging cases near clinical decision thresholds, such as borderline left ventricular hypertrophy or pericardial effusion severity. The code will be released upon acceptance of the paper.

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