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.
Acknowledgments
We extend our sincere gratitude to David Ouyang, MD (Research Scientist, Division of Research, Kaiser Permanente Northern California; Adjunct Assistant Professor, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, California, USA) for his invaluable feedback and insightful suggestions during the revision of this manuscript.Funding/Support. This work was funded by the National Natural Science Foundation of China (Grant No. 62225113, to B.D.), the National Natural Science Foundation of China (Grant No. 82472070, to H.X.Z.), the National Key Research and Development Program of China (Grant No. 2023YFC2705700, to B.D.), the National Natural Science Foundation of China (Grant No. U25A20443, to B.D.), the National Natural Science Foundation of China (Grant No. 82203144, to B.Q.Z.), the Natural Science Foundation of Hubei Province of China (Grant No. 2023AFB1083, to W.J.T.), and the Natural Science Foundation of Hubei Province of China (Grant No. 2025DJA055, to H.X.Z.), and supported by the New Cornerstone Science Foundation through the XPLORER PRIZE (to B.D.).
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Bai, Y., Zhang, R., Lei, Y. et al. A DeepSeek-powered AI system for automated chest radiograph interpretation in clinical practice. Nat Commun (2026). https://doi.org/10.1038/s41467-026-72680-6
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DOI: https://doi.org/10.1038/s41467-026-72680-6
