A DeepSeek-Powered AI System for Automated Chest Radiograph Interpretation in Clinical Practice
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Bai, Yaowei Zhang, Ruiheng Lei, Yu Duan, Xuhua Yao, Jingfeng Ju, Shuguang Wang, Chaoyang Yao, Wei Guo, Yiwan Zhang, Guilin Wan, Chao Yuan, Qian Chen, Lei Tang, Wenjuan Zhu, Biqiang Wang, Xinggang Sun, Tao Zhou, Wei Tao, Dacheng Xu, Yongchao Zheng, Chuansheng Zhao, Huangxuan Du, Bo |
| author_facet | Bai, Yaowei Zhang, Ruiheng Lei, Yu Duan, Xuhua Yao, Jingfeng Ju, Shuguang Wang, Chaoyang Yao, Wei Guo, Yiwan Zhang, Guilin Wan, Chao Yuan, Qian Chen, Lei Tang, Wenjuan Zhu, Biqiang Wang, Xinggang Sun, Tao Zhou, Wei Tao, Dacheng Xu, Yongchao Zheng, Chuansheng Zhao, Huangxuan Du, Bo |
| contents | A global shortage of radiologists has been exacerbated by the significant volume of chest X-ray workloads, particularly in primary care. Although multimodal large language models show promise, existing evaluations predominantly rely on automated metrics or retrospective analyses, lacking rigorous prospective clinical validation. Janus-Pro-CXR (1B), a chest X-ray interpretation system based on DeepSeek Janus-Pro model, was developed and rigorously validated through a multicenter prospective trial (NCT07117266). Our system outperforms state-of-the-art X-ray report generation models in automated report generation, surpassing even larger-scale models including ChatGPT 4o (200B parameters), while demonstrating reliable detection of six clinically critical radiographic findings. Retrospective evaluation confirms significantly higher report accuracy than Janus-Pro and ChatGPT 4o. In prospective clinical deployment, AI assistance significantly improved report quality scores, reduced interpretation time by 18.3% (P < 0.001), and was preferred by a majority of experts in 54.3% of cases. Through lightweight architecture and domain-specific optimization, Janus-Pro-CXR improves diagnostic reliability and workflow efficiency, particularly in resource-constrained settings. The model architecture and implementation framework will be open-sourced to facilitate the clinical translation of AI-assisted radiology solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20344 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A DeepSeek-Powered AI System for Automated Chest Radiograph Interpretation in Clinical Practice Bai, Yaowei Zhang, Ruiheng Lei, Yu Duan, Xuhua Yao, Jingfeng Ju, Shuguang Wang, Chaoyang Yao, Wei Guo, Yiwan Zhang, Guilin Wan, Chao Yuan, Qian Chen, Lei Tang, Wenjuan Zhu, Biqiang Wang, Xinggang Sun, Tao Zhou, Wei Tao, Dacheng Xu, Yongchao Zheng, Chuansheng Zhao, Huangxuan Du, Bo Artificial Intelligence A global shortage of radiologists has been exacerbated by the significant volume of chest X-ray workloads, particularly in primary care. Although multimodal large language models show promise, existing evaluations predominantly rely on automated metrics or retrospective analyses, lacking rigorous prospective clinical validation. Janus-Pro-CXR (1B), a chest X-ray interpretation system based on DeepSeek Janus-Pro model, was developed and rigorously validated through a multicenter prospective trial (NCT07117266). Our system outperforms state-of-the-art X-ray report generation models in automated report generation, surpassing even larger-scale models including ChatGPT 4o (200B parameters), while demonstrating reliable detection of six clinically critical radiographic findings. Retrospective evaluation confirms significantly higher report accuracy than Janus-Pro and ChatGPT 4o. In prospective clinical deployment, AI assistance significantly improved report quality scores, reduced interpretation time by 18.3% (P < 0.001), and was preferred by a majority of experts in 54.3% of cases. Through lightweight architecture and domain-specific optimization, Janus-Pro-CXR improves diagnostic reliability and workflow efficiency, particularly in resource-constrained settings. The model architecture and implementation framework will be open-sourced to facilitate the clinical translation of AI-assisted radiology solutions. |
| title | A DeepSeek-Powered AI System for Automated Chest Radiograph Interpretation in Clinical Practice |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2512.20344 |