README.md

---
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

<!-- <p align="left">
    <img src="pics/fig1_v.png" width="90%">
</p> -->

## 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](https://www.lanxplanet.com/GEM-ECG/)

#### Paper: 📄 [Arxiv](https://arxiv.org/pdf/2503.06073)

#### Code: 💻 [GitHub](https://github.com/lanxiang1017/GEM)

#### Model: 🤗 [GEM](https://huggingface.co/LANSG/GEM)

#### Data: 🤗 [ECG-Grounding](https://huggingface.co/datasets/LANSG/ECG-Grounding)


## Citation

If you find GEM helpful for your research and applications, please cite our paper:

```bibtex
@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}, 
}
```