General Medical AI (GMAI)

Universal AI for Healthcare Research | Shanghai AI Lab

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.
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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.
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• UniMedVL: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysis.
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• GMAI-VL: A Large Vision-Language Model and Comprehensive Multimodal Dataset Towards General Medical AI.
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• OmniMedVQA: A Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM.
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• GMAI-MMBench: A Comprehensive Multimodal Benchmark for General Medical AI.
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Foundation Models & Segmentation

• SAM-Med3D: A Vision Foundation Model for General-Purpose Segmentation on Volumetric Medical Images.
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• SAM-Med2D: Comprehensive Segment Anything Model for 2D Medical Imaging.
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• STU-Net: Scalable and Transferable Medical Image Segmentation (1.4B parameters).
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• IMIS-Bench: Interactive Medical Image Segmentation Benchmark and Baseline.
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🔗 Connect with Us

We welcome collaboration across academia, healthcare, and industry.

• GitHub Organization: github.com/uni-medical
• Zhihu Blog: GMAI Team
• Contact: hejunjun@pjlab.org.cn