用AI打通骨科长期照护全流程，提升诊疗效率与患者体验。
Evidence-Grounded AI for Musculoskeletal Care
- 基于大语言模型整合医院数据与权威知识，实现骨科疾病全程智能管理。
- 在1870例复杂病例中，全链路管理成功率提升10.6%。
- 适合需要长期随访的骨科临床医生及医疗系统管理者参考。
骨骼肌肉疾病是导致残疾的主要原因，且全球康复需求最大。由于骨骼、关节及相关组织的恢复、重塑和退变过程持续数月到数年，护理需长期跟踪而非孤立决策。临床医生必须反复整合动态的患者证据、医学知识和阶段目标，但现有信息常分散于不同就诊环节、科室与医院系统中，难以实现连续、个性化的管理。本文报告了OrthoPilot——一种基于大语言模型（LLM）的临床人工智能系统，可融合医院数据流与外部权威知识，实现骨科照护的持续管理。该系统能自动获取实时影像、检验、病理及医嘱数据，将患者状态演变转化为循证决策，覆盖从入院诊断到康复计划的全流程。我们基于真实世界电子健康记录（EHRs）构建了包含1,000种疾病代码的专业验证基准。在涵盖81名骨科专家的全路径读者研究中，OrthoPilot在诊断推理、临床决策和管理规划方面均优于具有25年经验的专家。该优势在60个外部临床中心得到验证，其表现超越所有评估的智能系统。在涉及1,870例复杂病例的前瞻性医生决策研究中，全链路管理成功率提升10.6%。在一项随机部署研究中，针对8,240名住院患者，系统集成使每张床位累计病例数增加9.7%，并改善患者对健康信息的获取体验。这些结果标志着临床AI正从预测单一事件，转向执行完整的骨骼肌肉疾病管理路径。
原文摘要 · Abstract (English)
Musculoskeletal diseases are among the leading causes of disability and drive the greatest global need for rehabilitation. Because recovery, remodelling and degeneration of bones, joints and related tissues unfold over months to years, care requires longitudinal management rather than isolated decisions. Clinicians must repeatedly integrate evolving patient evidence, medical knowledge and stage-specific functional goals, yet evidence is often fragmented across visits, departments and hospital systems, disrupting continuous, individualised management. Here we report OrthoPilot, a clinical artificial intelligence (AI) system powered by a large language model (LLM) that integrates hospital data streams with authoritative external knowledge for continuous musculoskeletal care. It autonomously retrieves real-time imaging, laboratory, pathology and order data and translates evolving patient states into evidence-based decisions from admission diagnosis through rehabilitation planning. We established a specialist-validated benchmark from real-world electronic health records (EHRs) spanning 1,000 disease codes. In a full-pathway reader study against 81 orthopaedic physicians, OrthoPilot outperformed experts with 25 years of experience in diagnostic reasoning, clinical decision-making and management planning. This advantage generalised across 60 external clinical centres, where OrthoPilot surpassed all evaluated intelligent systems. In a prospective physician decision-making study of 1,870 complex cases, OrthoPilot improved full-chain management success by 10.6%. In a randomised deployment involving 8,240 inpatients, integration into routine care 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.
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