RADAR: An Expert-Level Generalist AI for Abdominal CT Diagnosis

RADAR is a generalist vision-language model trained on over 400,000 contrast-enhanced abdominal CT examinations with 15 million anatomy-aware image–text pairs, learning directly from clinical reports without manual annotation. RADAR provides a scalable and versatile framework for radiology AI, demonstrating expert-level performance across both routine and complex clinical tasks.

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Setup

Create a conda environment and install the required dependencies:

conda create -n radar python=3.10
conda activate radar
pip install -r requirements.txt

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HuggingFace

• The pre-trained checkpoints and supporting files are available on HuggingFace.
• For convenience, we have provided the demo nifty, and predicted results in CSV format in this repo. The supporting files required for the inference demo and training can be downloaded from HuggingFace.
• Download via scripts: We provide two helper scripts under download_scripts/ to fetch the required files from HuggingFace:

cd download_scripts
# Download model checkpoints and support files into ckpt/
python download_checkpoints.py
# Download auxiliary data (processed masks)
python download_auxiliary_data.py

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Zenodo

Code can also be archived on Zenodo.

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Documentation

For detailed instructions, please refer to the following guides:

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Acknowledgements

This project is built upon the following open-source projects:

• LAVIS (BSD 3-Clause License)
• nnU-Net (Apache License 2.0)
• MONAI (Apache License 2.0)
• 3D-ResNets-PyTorch (MIT License)

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License

This project is released under the Apache License 2.0.

Portions of the code are derived from third-party open-source projects that are distributed under their own licenses (see the Acknowledgements above). Their original license texts are retained in THIRD_PARTY_LICENSES.md.

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Citation

If you find RADAR useful in your research, please cite our paper:

@article{damo-radar-2026,
    author = {Qi Zhang and Jianpeng Zhang and Weiwei Cao and Zilin Lu and Wanxing Chang and Haonan Ding and Cao Chen and Zhi Li and Xing Xue and Sinuo Wang and Shaoteng Zhang and Yutong Xie and Yong Xia and Qi Wu and Zhongyi Shui and Xi Li and Zhilin Zheng and Yanjie Zhou and Tony C.W. Mok and Yingda Xia and Hongkan Wang and Xianghua Ye and Tao Ma and Jie Peng and Xiaoguang Wang and Jian Ding and Yuming Gao and Huazhen Ye and Yiping Liu and Dongjie Chen and Zhaomin Ni and Jianwen Ning and Wei Zhang and Jian Liu and Chaohui Yu and Shenghong Ju and Jianfeng Zhang and Wenbo Xiao and Ling Zhang and Tingbo Liang },
    title = {An expert-level generalist AI for abdominal CT diagnosis},
    journal = {Science},
    volume = {393},
    number = {6817},
    pages = {eaec6129},
    year = {2026},
    doi = {10.1126/science.aec6129},
    URL = {https://www.science.org/doi/abs/10.1126/science.aec6129}
}