arXiv 2512.203442025-12-23cs.AI

基于DeepSeek的AI系统用于临床实践中自动胸部X光解读

A DeepSeek-Powered AI System for Automated Chest Radiograph Interpretation in Clinical Practice

Wuhan University（武汉大学）；Union Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属协和医院）；Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine（湖北省精准放射与介入医学临床医学研究中心）；The First Affiliated Hospital of Zhengzhou University（郑州大学第一附属医院）；Huazhong University of Science and Technology（华中科技大学）；The First Affiliated Hospital of Henan University of Science and Technology（河南科技大学第一附属医院）

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, Wenjua… 展开作者

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, Tao Sun, Wei Zhou, Dacheng Tao, Yongchao Xu, Chuansheng Zheng, Huangxuan Zhao, Bo Du

备注：: arXiv admin note: substantial text overlap with arXiv:2507.19493

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AI总结：

基于DeepSeek的AI系统在临床实践中实现自动胸部X光解读，提升诊断可靠性与效率，减少解读时间并获得专家认可。

摘要：

全球放射科医生短缺问题在胸部X光工作量显著增加的背景下进一步加剧，尤其是在初级医疗保健领域。尽管多模态大语言模型显示出潜力，但现有评估主要依赖于自动化指标或回顾性分析，缺乏严格前瞻性临床验证。Janus-Pro-CXR（1B）是一种基于DeepSeek Janus-Pro模型的胸部X光解读系统，通过多中心前瞻性试验（NCT07117266）进行了严格验证。我们的系统在自动报告生成方面优于最先进的X光报告生成模型，超越了包括ChatGPT 4o（200B参数）在内的更大规模模型，同时能够可靠地检测到六个临床关键的放射学发现。回顾性评估证实其报告准确率显著高于Janus-Pro和ChatGPT 4o。在前瞻性临床部署中，AI辅助显著提高了报告质量评分，将解读时间减少了18.3%（P < 0.001），并在54.3%的病例中被专家所偏好。通过轻量级架构和领域特定优化，Janus-Pro-CXR提高了诊断可靠性和工作流程效率，特别是在资源受限的环境中。模型架构和实现框架将开源，以促进AI辅助放射学解决方案的临床应用。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。