Echo-CoPilot: A Multiple-Perspective Agentic Framework for Reliable Echocardiography Interpretation
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910053452218368 |
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| author | Heidari, Moein Mehrabian, Ali Roohi, Mohammad Amin Chen, Wenjin Foran, David J. Grewal, Jasmine Hacihaliloglu, Ilker |
| author_facet | Heidari, Moein Mehrabian, Ali Roohi, Mohammad Amin Chen, Wenjin Foran, David J. Grewal, Jasmine Hacihaliloglu, Ilker |
| contents | Echocardiography interpretation requires integrating multi-view temporal evidence with quantitative measurements and guideline-grounded reasoning, yet existing foundation-model pipelines largely solve isolated subtasks and fail when tool outputs are noisy or values fall near clinical cutoffs. We propose Echo-CoPilot, an end-to-end agentic framework that combines a multi-perspective workflow with knowledge-graph guided measurement selection. Echo-CoPilot runs three independent ReAct-style agents, structural, pathological, and quantitative, that invoke specialized echocardiography tools to extract parameters while querying EchoKG to determine which measurements are required for the clinical question and which should be avoided. A self-contrast language model then compares the evidence-grounded perspectives, generates a discrepancy checklist, and re-queries EchoKG to apply the appropriate guideline thresholds and resolve conflicts, reducing hallucinated measurement selection and borderline flip-flops. On MIMICEchoQA, Echo-CoPilot provides higher accuracy compared to SOTA baselines and, under a stochasticity stress test, achieves higher reliability through more consistent conclusions and fewer answer changes across repeated runs. Our code is publicly available at~\href{https://github.com/moeinheidari7829/Echo-CoPilot}{\textcolor{magenta}{GitHub}}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_09944 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Echo-CoPilot: A Multiple-Perspective Agentic Framework for Reliable Echocardiography Interpretation Heidari, Moein Mehrabian, Ali Roohi, Mohammad Amin Chen, Wenjin Foran, David J. Grewal, Jasmine Hacihaliloglu, Ilker Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Image and Video Processing Echocardiography interpretation requires integrating multi-view temporal evidence with quantitative measurements and guideline-grounded reasoning, yet existing foundation-model pipelines largely solve isolated subtasks and fail when tool outputs are noisy or values fall near clinical cutoffs. We propose Echo-CoPilot, an end-to-end agentic framework that combines a multi-perspective workflow with knowledge-graph guided measurement selection. Echo-CoPilot runs three independent ReAct-style agents, structural, pathological, and quantitative, that invoke specialized echocardiography tools to extract parameters while querying EchoKG to determine which measurements are required for the clinical question and which should be avoided. A self-contrast language model then compares the evidence-grounded perspectives, generates a discrepancy checklist, and re-queries EchoKG to apply the appropriate guideline thresholds and resolve conflicts, reducing hallucinated measurement selection and borderline flip-flops. On MIMICEchoQA, Echo-CoPilot provides higher accuracy compared to SOTA baselines and, under a stochasticity stress test, achieves higher reliability through more consistent conclusions and fewer answer changes across repeated runs. Our code is publicly available at~\href{https://github.com/moeinheidari7829/Echo-CoPilot}{\textcolor{magenta}{GitHub}}. |
| title | Echo-CoPilot: A Multiple-Perspective Agentic Framework for Reliable Echocardiography Interpretation |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2512.09944 |