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.

Model Preparation

Pre-trained checkpoints are available on HuggingFace.

File	Description	Destination
checkpoint_radar_pretrain.pth	RADAR pre-trained on RAD-CT	radar/ckpt/checkpoint_radar_pretrain.pth
bert-base-chinese	BERT tokenizer and model (Chinese)	radar/ckpt/bert-base-chinese/
bert-base-uncased	BERT tokenizer and model (English)	radar/ckpt/bert-base-uncased/
checkpoint_unet.pth	Pretrained VisionBranch (UNet) checkpoint	radar/ckpt/checkpoint_unet.pth
checkpoint_radar_plus.pth	RADAR+ checkpoint trained from scratch on Merlin-CT-Train	radar/ckpt/checkpoint_radar_plus.pth
checkpoint_radar_plus_finetuned_on_merlin.pth	RADAR+ checkpoint pretrained on RAD-CT and finetuned on Merlin-CT-Train	radar/ckpt/checkpoint_radar_plus_finetuned_on_merlin.pth

Citation

If you use these models in your research, please cite:

@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}
}