Currently submitted to: JMIR Medical Informatics
Date Submitted: Nov 7, 2025
Open Peer Review Period: Nov 25, 2025 - Jan 20, 2026
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ECG-R1: A Multi-modal Vision-Language Model with Reinforcement Learning for Differentiating Ischemic from Non-ischemic T-wave Inversion
ABSTRACT
Background:
The differentiation of ischemic from non-ischemic T-wave inversion (TWI) on electrocardiograms (ECGs) is a critical diagnostic challenge in cardiology. The non-specific nature of TWI leads to high false-positive rates, resulting in unnecessary, costly, and risky invasive procedures for patients. Existing deep learning models are often limited by being single-modality "black boxes".
Objective:
The objective of this study is to develop a novel diagnostic framework designed to address the critical clinical challenge of accurately differentiating ischemic from non-ischemic TWI. By utilizing a multi-modal Vision-Language Model trained with a Reinforcement Learning (RL) paradigm, this study aims to improve diagnostic accuracy and provide interpretable reasoning.
Methods:
We develop ECG-R1, a multi-modal framework using the Qwen2-VL-2B Vision-Language Model to analyze both ECG waveform images and associated clinical text. Instead of SFT, the model is trained using a RL paradigm with the Group Relative Policy Optimization (GRPO) algorithm. The model is trained to generate a structured output containing an explicit reasoning trace and a final "Yes" or "No" answer. A two-component, rule-based reward function is designed to assess both format adherence and diagnostic accuracy. Performance is compared against strong Supervised Fine-Tuning (SFT) baselines.
Results:
On a multi-modal dataset of 12,917 cases with TWI, our GRPO model achieves an average accuracy of 74.07%, demonstrating strong generalization with 72.93% accuracy in cross-hospital validation. This result is an improvement of ~24 % over the ~50% diagnostic accuracy of clinicians and 8.2% higher than the best SFT baseline, using ~71% fewer parameters.
Conclusions:
The RL-based ECG-R1 framework successfully differentiates ischemic from non-ischemic TWI and demonstrates significantly better generalization than standard SFT methods. By enhancing diagnostic accuracy and providing interpretable reasoning, this approach offers a more robust and trustworthy tool to support clinical decision-making in cardiology.
Citation
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