Pawelczyk Johannes, Kraus Moritz, Voigtlaender Sebastian, Siebenlist Sebastian, Rupp Marco-Christopher
Sports Orthopedics Department, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
Department of Traumatology, University Hospital Zurich, Zurich, Switzerland.
HSS J. 2025 May 30:15563316251341321. doi: 10.1177/15563316251341321.
Artificial intelligence (AI) and digital health (DH) solutions are reshaping musculoskeletal (MSK) care across diagnostics, treatment planning, workflow optimization, and administrative burden reduction. AI-enabled triage systems enhance patient flow efficiency, while automated scheduling, symptom checkers, and AI-powered virtual assistants streamline pre-visit interactions. In MSK radiographic diagnostics, AI augments imaging interpretation, enabling automated fracture detection, opportunistic screening, and quantitative imaging, improving diagnostic accuracy and standardization. Preoperative planning solutions facilitate implant templating, surgical navigation, and patient-specific instrumentation, reducing variability and enhancing surgical precision. Concurrently, digital scribes and AI-driven documentation tools alleviate administrative overhead, mitigating clinician burnout and enabling refocused patient engagement. Predictive analytics optimize treatment pathways by leveraging multimodal patient data for risk stratification and personalized decision support. However, algorithmic bias, model generalizability, regulatory hurdles, and legal ambiguities present substantial implementation barriers, necessitating rigorous validation, adaptive governance, and seamless clinical integration. The U.S. and EU regulatory landscapes diverge in their approaches to AI oversight, with the former favoring expedited market access and the latter imposing stringent compliance mandates under the EU AI Act. AI's integration into MSK care demands robust validation frameworks, standardized interoperability protocols, and dynamic regulatory pathways balancing safety and innovation. Emerging generalist foundation models, open-source large language models (LLMs), and specialized AI-driven medical applications herald a paradigm shift toward precision MSK care. These innovations will require prospective clinical validation to ensure patient benefit and mitigate risk. Addressing ethical considerations, ensuring equitable access, and fostering interdisciplinary collaboration remain paramount in translating AI's potential into tangible improvements in MSK healthcare delivery.
人工智能（AI）和数字健康（DH）解决方案正在重塑肌肉骨骼（MSK）护理，涵盖诊断、治疗规划、工作流程优化以及减轻管理负担等方面。启用人工智能的分诊系统提高了患者流程效率，而自动排班、症状检查器和人工智能驱动的虚拟助手简化了就诊前的互动。在MSK放射诊断中，人工智能增强了影像解读能力，实现了骨折自动检测、机会性筛查和定量成像，提高了诊断准确性和标准化程度。术前规划解决方案有助于植入物模板制作、手术导航和个性化器械定制，减少变异性并提高手术精度。同时，数字抄写员和人工智能驱动的文档工具减轻了管理负担，缓解了临床医生的职业倦怠，并使他们能够重新专注于患者参与。预测分析通过利用多模式患者数据进行风险分层和个性化决策支持，优化治疗路径。然而，算法偏差、模型通用性、监管障碍和法律模糊性构成了重大的实施障碍，需要进行严格的验证、适应性治理以及无缝的临床整合。美国和欧盟的监管格局在人工智能监管方法上存在差异，前者倾向于加快市场准入，而后者根据欧盟人工智能法案施加严格的合规要求。人工智能融入MSK护理需要强大的验证框架、标准化的互操作性协议以及平衡安全与创新的动态监管路径。新兴的通用基础模型、开源大语言模型（LLMs）和专门的人工智能驱动的医疗应用预示着向精准MSK护理的范式转变。这些创新需要进行前瞻性临床验证，以确保患者受益并降低风险。在将人工智能的潜力转化为MSK医疗服务的切实改进方面，解决伦理问题、确保公平获取并促进跨学科合作仍然至关重要。
