A DeepSeek-powered AI system for automated chest radiograph interpretation in clinical practice.
Authors
Affiliations (22)
Affiliations (22)
- School of Computer Science, Wuhan University, Wuhan, China.
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong, University of Science and Technology, Wuhan, China.
- Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, Wuhan, China.
- National Engineering Research Center for Multimedia Software and Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China.
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
- School of Electronic Information and Communications, Huazhong, University of Science and Technology, Wuhan, China.
- Department of Interventional Radiology, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China.
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong, University of Science and Technology, Wuhan, China.
- Department of Nephrology, Union Hospital, Tongji Medical College, Huazhong, University of Science and Technology, Wuhan, China.
- Department of Urology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong, University of Science and Technology, Wuhan, China.
- Department of Interventional Radiology, The First Affiliated Hospital of University of Science and Technology of China, Hefei, China.
- Wuhan Artificial Intelligence Computing Center, Wuhan Supercomputing Center, Wuhan, China.
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore. [email protected].
- School of Computer Science, Wuhan University, Wuhan, China. [email protected].
- National Engineering Research Center for Multimedia Software and Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China. [email protected].
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong, University of Science and Technology, Wuhan, China. [email protected].
- Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, Wuhan, China. [email protected].
- School of Computer Science, Wuhan University, Wuhan, China. [email protected].
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong, University of Science and Technology, Wuhan, China. [email protected].
- National Engineering Research Center for Multimedia Software and Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China. [email protected].
- School of Computer Science, Wuhan University, Wuhan, China. [email protected].
- National Engineering Research Center for Multimedia Software and Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China. [email protected].
Abstract
A global shortage of radiologists has increased the burden of chest X-ray interpretation, particularly in primary and resource-limited settings. Although artificial intelligence systems can assist with report generation, most lack rigorous prospective validation in real clinical environments. Here we show that Janus-Pro-CXR, a lightweight artificial intelligence system optimized for chest radiograph interpretation, improves report quality and workflow efficiency in a multicenter prospective study (NCT07117266). Developed through domain-specific fine-tuning of a multimodal foundation model, Janus-Pro-CXR achieved strong diagnostic performance for key thoracic findings and generated clinically structured reports aligned with expert standards. In real-world deployment involving 296 patients, AI assistance significantly improved report quality scores and reduced interpretation time by 18.3% compared with standard practice. The system operates efficiently on standard hardware, supporting practical implementation in resource-constrained settings. These findings demonstrate the clinical value of lightweight, human-AI collaborative systems in radiology practice.