Song Jian, Wang Guang-Chao, Wang Si-Cheng, He Chong-Ru, Zhang Ying-Ze, Chen Xiao, Su Jia-Can
Department of Orthopedics, Trauma Orthopedics Center, Institute of Musculoskeletal Injury and Translational Medicine of Organoids, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.
Department of Orthopedics, Shanghai Zhongye Hospital, Shanghai, 200941, China.
Mil Med Res. 2025 Aug 4;12(1):42. doi: 10.1186/s40779-025-00633-z.
Conventional diagnostic and therapeutic approaches in orthopedics are frequently time intensive and associated with elevated rates of diagnostic error, underscoring the urgent need for more efficient tools to improve the current situation. Recently, artificial intelligence (AI) has been increasingly integrated into orthopedic practice, providing data-driven approaches to support diagnostic and therapeutic processes. With the continuous advancement of AI technologies and their incorporation into routine orthopedic workflows, a comprehensive understanding of AI principles and their clinical applications has become increasingly essential. The review commences with a summary of the core concepts and historical evolution of AI, followed by an examination of machine learning and deep learning frameworks designed for orthopedic clinical and research applications. We then explore various AI-based applications in orthopedics, including image analysis, disease diagnosis, and treatment approaches such as surgical assistance, drug development, rehabilitation support, and personalized therapy. These applications are designed to help researchers and clinicians gain a deeper understanding of the current applications of AI in orthopedics. The review also highlights key challenges and limitations that affect the practical use of AI, such as data quality, model generalizability, and clinical validation. Finally, we discuss possible future directions for improving AI technologies and promoting their safe and effective integration into orthopedic care.
骨科传统的诊断和治疗方法通常耗时较长，且诊断错误率较高，这凸显了迫切需要更高效的工具来改善当前状况。近年来，人工智能（AI）已越来越多地融入骨科实践，提供数据驱动的方法来支持诊断和治疗过程。随着人工智能技术的不断进步及其纳入常规骨科工作流程，全面了解人工智能原理及其临床应用变得越来越重要。本文综述首先总结了人工智能的核心概念和历史演变，接着考察了为骨科临床和研究应用设计的机器学习和深度学习框架。然后，我们探讨了骨科中基于人工智能的各种应用，包括图像分析、疾病诊断以及手术辅助、药物研发、康复支持和个性化治疗等治疗方法。这些应用旨在帮助研究人员和临床医生更深入地了解人工智能在骨科中的当前应用。本文综述还强调了影响人工智能实际应用的关键挑战和局限性，如数据质量、模型通用性和临床验证。最后，我们讨论了改进人工智能技术并促进其安全有效地融入骨科护理的可能未来方向。
