Khojastehnezhad Mohammad Amin, Youseflee Pouya, Moradi Ali, Ebrahimzadeh Mohammad H, Jirofti Nafiseh
Orthopedic Research Center, Department of Orthopedic Surgery, Mashhad University of Medical Sciences, Mashhad, Iran.
Bone and Joint Research laboratory, Ghaem Hospital, Mashhad University of Medical Sciences, Mashhad, Iran.
Arch Bone Jt Surg. 2025;13(1):17-22. doi: 10.22038/ABJS.2024.84231.3829.
Artificial Intelligence (AI) is rapidly transforming healthcare, particularly in orthopedics, by enhancing diagnostic accuracy, surgical planning, and personalized treatment. This review explores current applications of AI in orthopedics, focusing on its contributions to diagnostics and surgical procedures. Key methodologies such as artificial neural networks (ANNs), convolutional neural networks (CNNs), support vector machines (SVMs), and ensemble learning have significantly improved diagnostic precision and patient care. For instance, CNN-based models excel in tasks like fracture detection and osteoarthritis grading, achieving high sensitivity and specificity. In surgical contexts, AI enhances procedures through robotic assistance and optimized preoperative planning, aiding in prosthetic sizing and minimizing complications. Additionally, predictive analytics during postoperative care enable tailored rehabilitation programs that improve recovery times. Despite these advancements, challenges such as data standardization and algorithm transparency hinder widespread adoption. Addressing these issues is crucial for maximizing AI's potential in orthopedic practice. This review emphasizes the synergistic relationship between AI and clinical expertise, highlighting opportunities to enhance diagnostics and streamline surgical procedures, ultimately driving patient-centric care.
人工智能(AI)正在迅速改变医疗保健行业,尤其是在骨科领域,它通过提高诊断准确性、手术规划和个性化治疗来实现这一目标。本综述探讨了AI在骨科领域的当前应用,重点关注其对诊断和外科手术的贡献。诸如人工神经网络(ANN)、卷积神经网络(CNN)、支持向量机(SVM)和集成学习等关键方法显著提高了诊断精度和患者护理水平。例如,基于CNN的模型在骨折检测和骨关节炎分级等任务中表现出色,具有高灵敏度和特异性。在手术方面,AI通过机器人辅助和优化术前规划来改进手术过程,有助于假肢尺寸确定并将并发症降至最低。此外,术后护理期间的预测分析能够制定量身定制的康复计划,从而缩短恢复时间。尽管取得了这些进展,但数据标准化和算法透明度等挑战阻碍了AI的广泛应用。解决这些问题对于最大限度发挥AI在骨科实践中的潜力至关重要。本综述强调了AI与临床专业知识之间的协同关系,突出了增强诊断和简化手术程序的机会,最终推动以患者为中心的护理。