质量问题:基于人工智能的反质量评估预测了技术技能的提高
Divya Kewalramani1, Diego Sanhueza Roman2, Sofía Abedrapo Lagos3
1Department of Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ.
Surgery
|August 29, 2025
概括
通过人工智能 (AI) 评估的高质量反可以显著提高模拟手术中的外科技术技能. 像Teach1这样的人工智能工具可以标准化反,提高技能发展和潜在的患者结果.
科学领域:
- 医疗教育
- 手术培训
- 医疗保健中的人工智能
背景情况:
- 在外科教育中提供一致,高质量的反至关重要但具有挑战性.
- 这项研究研究了人工智能评估的反质量与模拟体的技术技能获取之间的相关性.
研究的目的:
- 确定人工智能 (AI) 评估的反质量是否与模拟体训练中的技术技能改进相关.
- 根据已确定的标准评估人工智能工具 (Teach1) 的有效性.
主要方法:
- 医学四年级学生 (n=75) 进行模拟造,获得数字反,并重复该程序.
- 一个人工智能工具 (Teach1) 根据5个质量标准评估了688条反意见.
- 分析了客观结构化技术技能评估 (ΔOSATS) 成绩的变化与反质量百分比.
主要成果:
- 目标 技术技能结构化评估 (OSATS) 评分在反后显著改善 (15.76至21.68).
- 较高的反质量百分比与更大的性能改善 (ΔOSATS) 有正相关 (r=0.309,P<.001).
- 人工智能反质量评估显示与专家评估有很大一致性 (κ=0.78).
结论:
- 人工智能驱动的反质量评估与模拟外科培训中的技术技能提高有显著的相关性.
- 人工智能工具Teach1提供了一种可扩展的方法来标准化反质量,这是技能发展的关键驱动力.
- 通过人工智能提高反质量有可能提高外科培训和患者的结果.
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