Artificial intelligence (AI) has rapidly emerged as a transformative force in musculoskeletal imaging and interventional radiology. This article explores how AI-based methods—including machine learning (ML) and deep learning (DL)—streamline diagnostic processes, guide interventions, and improve patient outcomes. Key applications discussed include ultrasound-guided procedures for joints, nerves, and tumor-targeted interventions, along with CT-guided biopsies and ablations, and fluoroscopy-guided facet joint and nerve block injections. AI-powered segmentation algorithms, real-time feedback systems, and dose-optimization protocols collectively enable greater precision, operator consistency, and patient safety. In rehabilitation, AI-driven wearables and predictive models facilitate personalized exercise programs that can accelerate recovery and enhance long-term function. While challenges persist—such as data standardization, regulatory hurdles, and clinical adoption—ongoing interdisciplinary collaboration, federated learning models, and the integration of genomic and environmental data hold promise for expanding AI’s capabilities. As personalized medicine continues to advance, AI is poised to refine risk stratification, reduce radiation exposure, and support minimally invasive, patient-specific interventions, ultimately reshaping musculoskeletal care from early detection and diagnosis to individualized treatment and rehabilitation.
人工智能（AI）已迅速成为肌肉骨骼影像学与介入放射学领域的变革性力量。本文探讨了基于人工智能的方法——包括机器学习（ML）和深度学习（DL）——如何优化诊断流程、引导介入操作并改善患者预后。文中讨论的关键应用涵盖关节、神经及肿瘤靶向介入的超声引导操作，以及CT引导下的活检与消融术、透视引导的小关节和神经阻滞注射。AI驱动的分割算法、实时反馈系统和剂量优化方案共同实现了更高的操作精度、操作者一致性和患者安全性。在康复领域，AI驱动的可穿戴设备和预测模型有助于制定个性化运动方案，从而加速康复进程并提升长期功能。尽管仍面临数据标准化、监管障碍和临床推广等挑战，但持续的跨学科合作、联邦学习模型以及基因组与环境数据的整合，为拓展AI应用能力带来了希望。随着个体化医疗的不断发展，人工智能有望完善风险分层、减少辐射暴露，并支持微创化、患者特异性的介入治疗，最终从早期检测诊断到个体化治疗与康复，全面重塑肌肉骨骼疾病的诊疗模式。
Role of Artificial Intelligence in Musculoskeletal Interventions
肿瘤(癌症)患者之家