用于肌肉骨骼护理的基于证据的人工智能
Evidence-Grounded AI for Musculoskeletal Care
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中文总结 AI 辅助
研究针对肌肉骨骼护理中证据分散问题，提出基于大语言模型的OrthoPilot系统，整合多源数据进行持续管理。该系统在诊断推理等方面超越专家，优于其他智能系统，还提升了管理成功率和病床病例数等，推动临床人工智能实现纵向管理。
中文摘要 AI 辅助
肌肉骨骼疾病是全球残疾的主要原因之一，对康复的需求极大。由于恢复、重塑和退化过程漫长，肌肉骨骼护理需要长期管理，整合患者证据、外部医学知识和特定阶段功能目标。但日常实践中证据分散，限制了个性化循证护理。本文介绍了OrthoPilot，这是一个由大语言模型驱动的临床人工智能系统，整合医院数据流和外部权威知识以进行持续的肌肉骨骼管理。它能自主检索实时数据并做出循证决策。研究建立了包含1000种疾病代码的基准，在读者研究中，OrthoPilot在诊断推理、临床决策和管理规划方面超越了有25年经验的骨科医生，在外部临床中心也优于其他智能系统。在1870例复杂病例的前瞻性研究中，它提高了全链管理成功率10.6%，在8240例住院患者的随机部署中，增加了每张病床的累计病例数9.7%，并改善了患者获取健康信息的情况。这些结果推动临床人工智能从预测孤立事件转向执行完整肌肉骨骼护理路径的纵向管理。
英文摘要
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.
发表机构
- Ruijin Hospital, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院，健康科学技术学院）
- Shanghai Innovation Institute（上海创新研究院）
- Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
- College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）
- MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China（中国科学技术大学脑启发智能感知与认知教育部重点实验室）
- School of Basic Medical Sciences, Intelligent Medicine Institute, Fudan University（复旦大学基础医学院智能医学研究院）
- Department of Radiation Oncology, Stanford University School of Medicine（斯坦福大学医学院放射肿瘤学系）
- School of Computing and Data Science, The University of Hong Kong（香港大学计算与数据科学学院）
- College of AI, Tsinghua University（清华大学人工智能学院）
机构由 AI 辅助整理，请以论文原文为准。
