{"ID":6620572,"CreatedAt":"2026-07-15T01:01:48.440468303Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.12527","arxiv_id":"2607.12527","title":"Evidence-Grounded AI for Musculoskeletal Care","abstract":"Musculoskeletal diseases are among the leading causes of disability worldwide and create the greatest global need for rehabilitation. Because recovery, remodelling and degeneration often unfold over months to years, musculoskeletal care requires longitudinal management that repeatedly integrates evolving patient evidence, external medical knowledge and stage-specific functional goals. In routine practice, this evidence is fragmented across visits, departments and hospital systems, limiting individualized, evidence-based care. Here we report OrthoPilot, a clinical artificial intelligence system powered by a large language model that integrates hospital data streams with authoritative external knowledge for continuous musculoskeletal management. OrthoPilot autonomously retrieves real-time imaging, laboratory, pathology and order data and converts evolving patient states into evidence-based decisions from admission diagnosis to rehabilitation planning. We established a specialist-validated benchmark from real-world electronic health records spanning 1,000 disease codes. In a reader study across the complete care pathway, OrthoPilot was compared with 81 orthopaedic physicians and surpassed experts with 25 years of experience in diagnostic reasoning, clinical decision-making and management planning. It also outperformed all evaluated intelligent systems across 60 external clinical centres. In a prospective study of 1,870 complex cases, OrthoPilot increased full-chain management success by 10.6%. During an 8-month randomised deployment involving 8,240 inpatients, it increased cumulative cases per bed by 9.7% and improved patient-reported access to health information. These results move clinical AI from predicting isolated events toward executing longitudinal management across complete musculoskeletal care pathways.","short_abstract":"Musculoskeletal diseases are among the leading causes of disability worldwide and create the greatest global need for rehabilitation. Because recovery, remodelling and degeneration often unfold over months to years, musculoskeletal care requires longitudinal management that repeatedly integrates evolving patient eviden...","url_abs":"https://arxiv.org/abs/2607.12527","url_pdf":"https://arxiv.org/pdf/2607.12527v1","authors":"[\"Wenjie Li\",\"Yujie Zhang\",\"Fanrui Zhang\",\"Haoran Sun\",\"Renhao Yang\",\"Junjun He\",\"Weiran Huang\",\"Yuanfeng Ji\",\"Chenrun Wang\",\"Kailing Wang\",\"Hongcheng Gao\",\"Kaipeng Zhang\",\"Hanyu Wang\",\"Angela Lin Wang\",\"Xingqi He\",\"Yilin Huang\",\"Shiyi Yao\",\"Lilong Wang\",\"Yankai Jiang\",\"Yirong Chen\",\"Chenglong Ma\",\"Jiyao Liu\",\"Ming Hu\",\"Gen Li\",\"Yidong Xu\",\"Chengyu Zhuang\",\"Jiawei Liu\",\"Yin Zhang\",\"Lequan Yu\",\"Lu Chen\",\"Yinpeng Dong\",\"Lei Liu\",\"Carlos Gutierrez Sanroman\",\"Yu Qiao\",\"Weijie Ma\",\"Xiaosong Wang\",\"Lei Wang\"]","published":"2026-07-14T09:03:26Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Language Model\"]","has_code":false}