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探索人工智能在骨科手术培训中的当前应用:一项系统的范围综述。

Exploring the Current Applications of Artificial Intelligence in Orthopaedic Surgical Training: A Systematic Scoping Review.

作者信息

Al-Saadawi Ahmed, Tehranchi Sam, Ahmed Syed, Nzeako Obinna J

机构信息

School of Medicine and Dentistry, Queen Mary University of London, London, GBR.

Department of Trauma and Orthopaedic Surgery, Maidstone and Tunbridge Wells NHS Trust, London, GBR.

出版信息

Cureus. 2025 Apr 3;17(4):e81671. doi: 10.7759/cureus.81671. eCollection 2025 Apr.

Abstract

In recent years, the integration of artificial intelligence (AI) in surgical education has been prominent, as evidenced by recent publications. Given the unique requirements and challenges associated with orthopaedic training, we conducted a systematic scoping review that examined the applications of AI only in this setting. A comprehensive search was conducted across four databases: Embase, CENTRAL, Medline, and Scopus. Original research articles that utilised an AI model within a specific orthopaedic educational context were considered for inclusion. Data from the included studies were extracted onto a bespoke form, followed by a thematic analysis to detect patterns within the data. Our findings were then summarised descriptively. A total of 21 studies were included in the scoping review, encompassing 273 participants. In relation to the integration of AI in orthopaedic surgical training, two overarching themes were identified: refinement of surgical competencies and enhancement of knowledge acquisition. All included studies, with the exception of one, were conducted in the last five years. Twelve distinct AI models were utilised across the included studies, with large language models accounting for over half the applications. Multiple promising interventions were highlighted, particularly the use of personalised automated feedback models for evaluating performance in surgical tasks. AI holds major potential to revolutionise orthopaedic surgical training. However, evidence supporting its use in this field remains limited. Further studies, preferably randomised controlled trials with larger sample sizes, are required to strengthen the evidence base.

摘要

近年来,人工智能(AI)在外科教育中的整合十分突出,近期的出版物证明了这一点。鉴于骨科培训的独特要求和挑战,我们进行了一项系统的范围综述,仅考察AI在这种情况下的应用。我们在四个数据库中进行了全面检索:Embase、CENTRAL、Medline和Scopus。纳入的研究需是在特定骨科教育背景下使用AI模型的原创研究文章。将纳入研究的数据提取到定制表格中,然后进行主题分析以检测数据中的模式。我们的研究结果随后进行了描述性总结。范围综述共纳入21项研究,涉及273名参与者。关于AI在骨科手术培训中的整合,确定了两个总体主题:手术能力的提升和知识获取的增强。除一项研究外,所有纳入研究均在过去五年内进行。纳入研究共使用了12种不同的AI模型,其中大语言模型占应用的一半以上。强调了多种有前景的干预措施,特别是使用个性化自动反馈模型来评估手术任务中的表现。AI具有彻底改变骨科手术培训的巨大潜力。然而,支持其在该领域使用的证据仍然有限。需要进一步的研究,最好是样本量更大的随机对照试验,以加强证据基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73a2/12049242/28aa5f29ff18/cureus-0017-00000081671-i01.jpg

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