ECG-R1: Protocol-Guided and Modality-Agnostic MLLM for Reliable ECG Interpretation

• URL: http://arxiv.org/abs/2602.04279v1
• Date: Wed, 04 Feb 2026 07:17:55 GMT
• Title: ECG-R1: Protocol-Guided and Modality-Agnostic MLLM for Reliable ECG Interpretation
• Authors: Jiarui Jin, Haoyu Wang, Xingliang Wu, Xiaocheng Fang, Xiang Lan, Zihan Wang, Deyun Zhang, Bo Liu, Yingying Zhang, Xian Wu, Hongyan Li, Shenda Hong,
• Abstract summary: Existing multimodal large language models (MLLMs) remain unreliable for ECG interpretation.<n>ECG-R1 is the first reasoning MLLM designed for reliable ECG interpretation.<n>Code and data are publicly available at hrefhttp://ai.heartvoice.com.cn/ECG-R1here.
• Score: 36.244601234085856
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 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 and data are publicly available at \href{https://github.com/PKUDigitalHealth/ECG-R1}{here}, and an online platform can be accessed at \href{http://ai.heartvoice.com.cn/ECG-R1/}{here}.

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