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美国手术培训中的人工智能:一项全面审查,绘制当前应用程序并确定未来研究应用程序的差距
Yasmine Zerrouki1, Jessica V Baran2, Rishiraj Bandi1
1Department of Medicine, Florida Atlantic University Charles E. Schmidt College of Medicine, Boca Raton, USA.
Cureus
|November 4, 2025
概括
人工智能 (AI) 和机器学习 (ML) 通过帮助技能获取和能力评估来增强外科教育. 需要进一步的研究来验证AI/ML工具,并解决整合在整体外科培训中的挑战.
科学领域:
- 医学教育 医学教育
- 进行外科手术培训.
- 人工智能在医学中的应用
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 越来越多地被用于医学教育.
- 它们对各个学习者级别的整体外科培训的具体影响需要全面的理解.
研究的目的:
- 将AI/ML在一般外科教育中的当前应用映射出来.
- 专注于AI/ML在技能获取,风险分层和能力评估中的作用.
- 确定AI/ML整合到外科课程中的障碍和挑战.
主要方法:
- 在PubMed,Scopus,Embase和IEEE Xplore中进行系统的文献搜索.
- 纳入标准侧重于AI/ML干预在一般外科培训的学生,住院医生和执业外科医生.
- 关于研究设计,培训水平,AI/ML工具类型,教育重点和结果的数据提取.
主要成果:
- 选择了18项研究,突出了AI/ML在基于模拟的培训,技能评估和决策支持方面的研究.
- 常见的障碍包括缺乏标准化和有限的课程整合.
- 人工智能/肌肉复杂性证明了改善外科教育成果的潜力.
结论:
- 人工智能/ML工具在提高整体外科教育方面表现有前途.
- 进一步的研究对于验证AI/ML工具和测量其影响至关重要.
- 解决整合挑战对于在外科培训中广泛采用这一技术至关重要.
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