{"ID":2825838,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.20344","arxiv_id":"2512.20344","title":"A DeepSeek-Powered AI System for Automated Chest Radiograph Interpretation in Clinical Practice","abstract":"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 \u003c 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.","short_abstract":"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 v...","url_abs":"https://arxiv.org/abs/2512.20344","url_pdf":"https://arxiv.org/pdf/2512.20344v1","authors":"[\"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\"]","published":"2025-12-23T13:26:13Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Language Model\"]","has_code":false}