Gupta Puneet, Haeberle Heather S, Zimmer Zachary R, Levine William N, Williams Riley J, Ramkumar Prem N
Department of Orthopaedic Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
Department of Orthopaedic Surgery, Columbia University Irving Medical Center, New York, NY, USA.
JSES Rev Rep Tech. 2023 Jan 7;3(2):189-200. doi: 10.1016/j.xrrt.2022.12.006. eCollection 2023 May.
Artificial intelligence (AI) aims to simulate human intelligence using automated computer algorithms. There has been a rapid increase in research applying AI to various subspecialties of orthopedic surgery, including shoulder surgery. The purpose of this review is to assess the scope and validity of current clinical AI applications in shoulder surgery literature.
A systematic literature review was conducted using PubMed for all articles published between January 1, 2010 and June 10, 2022. The search query used the terms as follows: . All studies that examined AI application models in shoulder surgery were included and evaluated for model performance and validation (internal, external, or both).
A total of 45 studies were included in the final analysis. Eighteen studies involved shoulder arthroplasty, 13 rotator cuff, and 14 other areas. Studies applying AI to shoulder surgery primarily involved (1) automated imaging analysis including identifying rotator cuff tears and shoulder implants (2) risk prediction analyses including perioperative complications, functional outcomes, and patient satisfaction. Highest model performance area under the curve ranged from 0.681 (poor) to 1.00 (perfect). Only 2 studies reported external validation.
Applications of AI in the field of shoulder surgery are expanding rapidly and offer patient-specific risk stratification for shared decision-making and process automation for resource preservation. However, model performance is modest and external validation remains to be demonstrated, suggesting increased scientific rigor is warranted prior to deploying AI-based clinical applications.
人工智能（AI）旨在使用自动化计算机算法模拟人类智能。将AI应用于骨科手术各个亚专业（包括肩部手术）的研究迅速增加。本综述的目的是评估当前临床AI在肩部手术文献中的应用范围和有效性。
使用PubMed对2010年1月1日至2022年6月10日发表的所有文章进行系统文献综述。搜索查询使用了以下术语： 。所有检查AI在肩部手术中应用模型的研究均被纳入，并对模型性能和验证（内部、外部或两者）进行评估。
最终分析共纳入45项研究。18项研究涉及肩关节置换术，13项涉及肩袖，14项涉及其他领域。将AI应用于肩部手术的研究主要涉及（1）自动成像分析，包括识别肩袖撕裂和肩部植入物；（2）风险预测分析，包括围手术期并发症、功能结果和患者满意度。曲线下面积最高的模型性能范围为0.681（差）至1.00（完美）。只有2项研究报告了外部验证。
AI在肩部手术领域的应用正在迅速扩展，为共同决策提供患者特异性风险分层，并为资源保存实现流程自动化。然而，模型性能一般，外部验证仍有待证明，这表明在部署基于AI的临床应用之前，需要提高科学严谨性。
