首个可靠心电图分析多模态大模型，解决误判难题。
ECG-R1: Protocol-Guided and Modality-Agnostic MLLM for Reliable ECG Interpretation
- 用指南规范指令数据生成，确保分析基于可测量特征和诊断逻辑。
- 引入交错模态丢弃机制，缺失心电图信号或图像时仍保持稳定输出。
- 通过证据奖励强化学习，提升结果与临床证据的契合度，适合医疗研究者使用。
心电图在临床中不可或缺，但现有多模态大语言模型在心电图解读中仍不可靠，常产生看似合理却临床错误的分析。为此，我们提出 ECG-R1，首个面向可靠心电图解读的推理型多模态大模型，包含三项创新：第一，采用协议引导的指令数据生成方法，将解读过程锚定于可测量的心电图特征、专著定义的定量阈值及诊断逻辑；第二，设计解耦模态架构并引入交错模态丢弃策略，提升在心电图信号或图像缺失时的鲁棒性与跨模态一致性；第三，提出基于心电图诊断证据奖励的强化学习方法，增强分析结果的证据支撑性。此外，我们系统评估了商业、开源及医学多模态大模型的心电图解读能力，首次提供量化证据表明严重幻觉现象普遍存在，提示公众不应直接信任其输出，需独立验证。代码已开源。
原文摘要 · Abstract (English)
Electrocardiography (ECG) serves as an indispensable diagnostic tool in clinical practice, yet existing multimodal large language models (MLLMs) remain unreliable for ECG interpretation, often producing plausible but clinically incorrect analyses. To address this, we propose ECG-R1, the first reasoning ECG MLLM designed for reliable ECG interpretation via three innovations. First, we construct the interpretation corpus using \textit{Protocol-Guided Instruction Data Generation}, grounding interpretation in measurable ECG features and monograph-defined quantitative thresholds and diagnostic logic. Second, we present a modality-decoupled architecture with \textit{Interleaved Modality Dropout} to improve robustness and cross-modal consistency when either the ECG signal or ECG image is missing. Third, we present \textit{Reinforcement Learning with ECG Diagnostic Evidence Rewards} to strengthen evidence-grounded ECG interpretation. Additionally, we systematically evaluate the ECG interpretation capabilities of proprietary, open-source, and medical MLLMs, and provide the first quantitative evidence that severe hallucinations are widespread, suggesting that the public should not directly trust these outputs without independent verification. Code is available at \href{https://github.com/PKUDigitalHealth/ECG-R1}{here}.
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