metadata

license: apache-2.0
pipeline_tag: image-text-to-text
library_name: transformers

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}, 
}