A DeepSeek-powered AI system for automated chest radiograph interpretation in clinical practice

作者：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, T R Sun, Wei Zhou, Dacheng Tao, Yi Xu, Chuansheng Zheng, Huangxuan Zhao, Bo Du · 发表于：Nature Communications · 年份：2026 · DOI：10.1038/s41467-026-72680-6 · 被引用次数：1 · 研究领域：COVID-19 diagnosis using AI、Artificial Intelligence in Healthcare and Education、Radiology practices and education

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.