首个支持多轮对话的便携式心电图智能助手，能精准理解心电波形参数。
ECG-Agent: On-Device Tool-Calling Agent for ECG Multi-Turn Dialogue
- 基于大模型设计可调尺寸的心电图对话代理，支持多轮交互与工具调用。
- 在真实心电图多轮对话数据集上，准确率显著优于传统模型。
- 轻量版可在设备端运行，性能接近大型模型，适合临床实时应用。
近年来，多模态大语言模型在心电图领域迅速发展，聚焦于分类、报告生成和单轮问答任务。然而，这些模型在真实场景中表现不足，缺乏多轮对话能力、设备端效率以及对心电图测量参数（如PQRST间期）的精确理解。为此，我们提出ECG-Agent，首个基于大模型的心电图多轮对话工具调用代理。为促进其开发与评估，我们还构建了ECG-Multi-Turn-Dialogue（ECG-MTD）数据集，包含多种心电图导联配置下的真实用户-助手多轮对话。我们开发了不同规模的ECG-Agent，从可部署于设备端的小型模型到更大规模的代理。实验表明，ECG-Agent在响应准确性方面优于基线心电图大模型。此外，设备端代理在响应准确性、工具调用能力及幻觉抑制等多项评估中表现与大型代理相当，验证了其在真实应用中的可行性。
原文摘要 · Abstract (English)
Recent advances in Multimodal Large Language Models have rapidly expanded to electrocardiograms, focusing on classification, report generation, and single-turn QA tasks. However, these models fall short in real-world scenarios, lacking multi-turn conversational ability, on-device efficiency, and precise understanding of ECG measurements such as the PQRST intervals. To address these limitations, we introduce ECG-Agent, the first LLM-based tool-calling agent for multi-turn ECG dialogue. To facilitate its development and evaluation, we also present ECG-Multi-Turn-Dialogue (ECG-MTD) dataset, a collection of realistic user-assistant multi-turn dialogues for diverse ECG lead configurations. We develop ECG-Agents in various sizes, from on-device capable to larger agents. Experimental results show that ECG-Agents outperform baseline ECG-LLMs in response accuracy. Furthermore, on-device agents achieve comparable performance to larger agents in various evaluations that assess response accuracy, tool-calling ability, and hallucinations, demonstrating their viability for real-world applications.
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