A DeepSeek-powered AI system for automated chest radiograph interpretation in clinical practice
- Yaowei Bai
- Ruiheng Zhang
- Yu Lei
- Xuhua Duan
- Jingfeng Yao
- Shuguang Ju
- Chaoyang Wang
- Wei Yao
- Yiwan Guo
- Guilin Zhang
- Chao Wan
- Qian Yuan
- Lei Chen
- Wenjuan Tang
- Biqiang Zhu
- Xinggang Wang
- Tao Sun
- Wei Zhou
- Dacheng Tao
- Yongchao Xu
- Chuansheng Zheng
- Huangxuan Zhao
- Bo Du
2026-05-07
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.