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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月7日

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  • 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

摘要

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.