An expert-level generalist AI for abdominal CT diagnosis
Abstract
Artificial intelligence (AI) in radiology aspires to deliver expert-level diagnosis across diverse clinical tasks, yet existing supervised strategies remain limited in scope. We developed RADAR, a generalist vision-language model trained on more than 400,000 contrast-enhanced abdominal computed tomography (CT) examinations and 15 million anatomy-wise image-text pairs, learning directly from clinical reports without manual annotation. Throughout internal and external evaluations across multiple centers and varied clinical scenarios, RADAR achieved high diagnostic performance and robust generalization for 18 anatomical structures and 146 imaging findings. In a reader study, RADAR assistance increased the diagnostic sensitivity of 26 radiologists by ~10%. RADAR offers a scalable, versatile, and interpretable solution for abdominal CT, demonstrating that generalist AI can match human experts in general and complicated radiology tasks.
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Authors: Qi Zhang, Jianpeng Zhang, Weiwei Cao, Zilin Lu, Wanxing Chang, He Ding, Cao Chen, Zhi Li, Xing Xue, Sinuo Wang, Shaoteng Zhang, Yutong Xie, Yong Xia, Qi Wu, Zhongyi Shui, Xi Li, Zhilin Zheng, Yanjie Zhou, Tony C. W. Mok, Yingda Xia, Hongkan Wang, Xianghua Ye, Tao Ma, Jie Peng, Xiaoguang Wang, Jian Ding, Yuming Gao, Huazhen Ye, Yiping Liu, Dongjie Chen, Zhaomin Ni, Jianwen Ning, Wei Zhang, Jian Liu, Chaohui Yu, Shenghong Ju, Jianfeng Zhang, Wenbo Xiao, Ling Zhang, Tingbo Liang
Institutions: First Affiliated Hospital Zhejiang University, Ningbo No. 2 Hospital, The University of Adelaide, Zhejiang University, University of Nottingham Ningbo China, Mohamed bin Zayed University of Artificial Intelligence, Alibaba Group (Cayman Islands), Alibaba Group (China), Shaoxing People's Hospital, Xinjiang Production and Construction Corps, First People's Hospital of Yuhang District, Zhejiang University of Science and Technology, Zhejiang Lab, Zhongda Hospital Southeast University, Jiaxing University, First Hospital of Jiaxing, Australian Institute of Business, Third People's Hospital of Huzhou, Jingning County People's Hospital, Heilongjiang University of Technology