Abstract：
OBJECTIVE: To define the current state of peer-reviewed literature demonstrating the usability, acceptability, and implementation of artificial intelligence (AI) and machine learning (ML) techniques in surgical coaching and training.
METHODS: We conducted a literature search with defined inclusion and exclusion criteria. We searched five scholarly databases: MEDLINE via PubMed, Embase via Elsevier, Scopus via Elsevier, Cochrane Central Register of Controlled Trials, and the Healthcare Administration Database via ProQuest. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines.
RESULTS: Only 4 articles met the inclusion criteria and used standardized methods for performance evaluation with expert observation. We found no literature examining the impact on performance, user acceptance, or implementation of AI/ML techniques used for surgical coaching and training. We highlight the need for qualitative and quantitative research demonstrating these techniques\' effectiveness before broad implementation.
CONCLUSIONS: We emphasize the need for research to specifically evaluate performance, impact, user acceptance, and implementation of AI/ML techniques. Incorporating these facets of research when developing AI/ML techniques for surgical training is crucial to ensure emerging technology meets user needs without increasing cognitive burden or frustrating users.
METHODS: We conducted a literature search with defined inclusion and exclusion criteria. We searched five scholarly databases: MEDLINE via PubMed, Embase via Elsevier, Scopus via Elsevier, Cochrane Central Register of Controlled Trials, and the Healthcare Administration Database via ProQuest. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines.
RESULTS: Only 4 articles met the inclusion criteria and used standardized methods for performance evaluation with expert observation. We found no literature examining the impact on performance, user acceptance, or implementation of AI/ML techniques used for surgical coaching and training. We highlight the need for qualitative and quantitative research demonstrating these techniques\' effectiveness before broad implementation.
CONCLUSIONS: We emphasize the need for research to specifically evaluate performance, impact, user acceptance, and implementation of AI/ML techniques. Incorporating these facets of research when developing AI/ML techniques for surgical training is crucial to ensure emerging technology meets user needs without increasing cognitive burden or frustrating users.
摘要：
目的：为了定义证明可用性的同行评审文献的当前状态，可接受性,以及在手术指导和培训中实施人工智能（AI）和机器学习（ML）技术。
方法：我们根据确定的纳入和排除标准进行了文献检索。我们搜索了五个学术数据库：通过PubMed的MEDLINE，Embase通过Elsevier，Scopus通过Elsevier，Cochrane中央控制试验登记册,以及通过ProQuest建立的医疗保健管理数据库。我们遵循了系统审查的首选报告项目和范围审查的Meta分析扩展（PRISMA-ScR）指南。
结果：只有4篇文章符合纳入标准，并使用标准化方法进行绩效评估，并进行专家观察。我们没有发现任何文献检查对性能的影响，用户接受,或实施用于手术指导和培训的AI/ML技术。我们强调在广泛实施之前需要定性和定量研究来证明这些技术的有效性。
结论：我们强调需要进行专门评估绩效的研究，影响，用户接受,以及AI/ML技术的实现。在开发用于手术训练的AI/ML技术时，结合这些方面的研究对于确保新兴技术满足用户需求而不增加认知负担或使用户感到沮丧至关重要。
方法：我们根据确定的纳入和排除标准进行了文献检索。我们搜索了五个学术数据库：通过PubMed的MEDLINE，Embase通过Elsevier，Scopus通过Elsevier，Cochrane中央控制试验登记册,以及通过ProQuest建立的医疗保健管理数据库。我们遵循了系统审查的首选报告项目和范围审查的Meta分析扩展（PRISMA-ScR）指南。
结果：只有4篇文章符合纳入标准，并使用标准化方法进行绩效评估，并进行专家观察。我们没有发现任何文献检查对性能的影响，用户接受,或实施用于手术指导和培训的AI/ML技术。我们强调在广泛实施之前需要定性和定量研究来证明这些技术的有效性。
结论：我们强调需要进行专门评估绩效的研究，影响，用户接受,以及AI/ML技术的实现。在开发用于手术训练的AI/ML技术时，结合这些方面的研究对于确保新兴技术满足用户需求而不增加认知负担或使用户感到沮丧至关重要。
