GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images
Introduction
GEM is a multimodal LLM unifying ECG time series, 12-lead ECG images and text for grounded and clinician-aligned ECG interpretation. GEM enables feature-grounded analysis, evidence-driven reasoning, and a clinician-like diagnostic process.
🔥 Updates
Project Page: 📖 Page
Paper: 📄 Arxiv
Code: 💻 GitHub
Model: 🤗 GEM
Data: 🤗 ECG-Grounding
Citation
If you find GEM helpful for your research and applications, please cite our paper:
@misc{lan2025gemempoweringmllmgrounded,
title={GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images},
author={Xiang Lan and Feng Wu and Kai He and Qinghao Zhao and Shenda Hong and Mengling Feng},
year={2025},
eprint={2503.06073},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.06073},
}