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| title: README | |
| emoji: 🏆 | |
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| license: apache-2.0 | |
| <div align="center"> | |
| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/64c0aaa4a8a2dcaa167ab5b3/tdo-7xOrIHU6DE4kCgu2N.png" width="150" alt="GMAI Logo"> | |
| <h1>General Medical AI (GMAI)</h1> | |
| <h3>Universal AI for Healthcare Research | Shanghai AI Lab</h3> | |
| <a href="https://github.com/uni-medical"> | |
| <img src="https://img.shields.io/badge/GitHub-uni--medical-181717?style=flat&logo=github" alt="GitHub"> | |
| </a> | |
| <a href="https://www.zhihu.com/people/gmai-38/"> | |
| <img src="https://img.shields.io/badge/Zhihu-GMAI%20Team-0084FF?style=flat&logo=zhihu&logoColor=white" alt="Zhihu"> | |
| </a> | |
| <a href="mailto:hejunjun@pjlab.org.cn"> | |
| <img src="https://img.shields.io/badge/Email-Contact%20Us-D14836?style=flat&logo=gmail&logoColor=white" alt="Email"> | |
| </a> | |
| </div> | |
| <br> | |
| The **General Medical AI (GMAI)** team at **Shanghai AI Lab** is dedicated to building general-purpose AI for healthcare. We aim to make healthcare AI more efficient and accessible through cutting-edge research and open-source contributions. | |
| Our research spans a wide spectrum of medical AI: | |
| * General medical image segmentation | |
| * General-purpose multimodal large models for medicine | |
| * 2D/3D medical image generation | |
| * Medical foundation models | |
| * Surgical video foundation & multimodal models | |
| * Surgical video generation | |
| --- | |
| ## 📊 Large-Scale Medical Data | |
| We have curated massive-scale medical data resources to fuel the vision of General Medical AI. | |
| * **Project Imaging-X**: A survey and collection of **1,000+** open-source medical imaging datasets. | |
| * [](https://github.com/uni-medical/Project-Imaging-X) | |
| **Key Statistics:** | |
| * **100M+** Medical images | |
| * **Hundreds of millions** of segmentation masks | |
| * **20M+** Medical text dialogue records | |
| * **10M+** Large-scale medical image–text pairs | |
| * **20M+** Multimodal Q&A entries | |
| --- | |
| ## 🚀 Selected Achievements | |
| ### Multimodal Large Models (LVLMs) | |
| * **SlideChat**: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding. | |
| * [](https://github.com/uni-medical/SlideChat) | |
| * **UniMedVL**: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysis. | |
| * [](https://github.com/uni-medical/UniMedVL) | |
| * **GMAI-VL**: A Large Vision-Language Model and Comprehensive Multimodal Dataset Towards General Medical AI. | |
| * [](https://github.com/uni-medical/GMAI-VL) | |
| * **OmniMedVQA**: A Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM. | |
| * [](https://github.com/OpenGVLab/Multi-Modality-Arena/tree/main/MedicalEval) | |
| * **GMAI-MMBench**: A Comprehensive Multimodal Benchmark for General Medical AI. | |
| * [](https://github.com/uni-medical/GMAI-MMBench) | |
| ### Foundation Models & Segmentation | |
| * **SAM-Med3D**: A Vision Foundation Model for General-Purpose Segmentation on Volumetric Medical Images. | |
| * [](https://github.com/uni-medical/SAM-Med3D) | |
| * **SAM-Med2D**: Comprehensive Segment Anything Model for 2D Medical Imaging. | |
| * [](https://github.com/OpenGVLab/SAM-Med2D) | |
| * **STU-Net**: Scalable and Transferable Medical Image Segmentation (1.4B parameters). | |
| * [](https://github.com/uni-medical/STU-Net) | |
| * **IMIS-Bench**: Interactive Medical Image Segmentation Benchmark and Baseline. | |
| * [](https://github.com/uni-medical/IMIS-Bench) | |
| --- | |
| ## 🔗 Connect with Us | |
| We welcome collaboration across academia, healthcare, and industry. | |
| * **GitHub Organization**: [github.com/uni-medical](https://github.com/uni-medical) | |
| * **Zhihu Blog**: [GMAI Team](https://www.zhihu.com/people/gmai-38/) | |
| * **Contact**: [hejunjun@pjlab.org.cn](mailto:hejunjun@pjlab.org.cn) |