在评分眼科住院申请时,比较教师和人工智能
Jared Moon1, Owen Sorensen2, Priyam Mazumdar3
1Department of Ophthalmology, Mitchel and Shannon Wong Eye Institute, Dell Medical School at the University of Texas at Austin, Austin, TX, United States.
概括
人工智能 (AI) 可以预测眼科实习生匹配结果,提供一个高效和客观的工具,以协助教师审查员. 这种人工智能应用程序有助于减少在竞争性居留选择过程中的行政工作量和潜在的人类偏见.
科学领域:
- 医学教育 医学教育
- 人工智能在医学中的应用
- 眼科医生 眼科 眼科
背景情况:
- 居留申请量和每个申请人的数据正在增加,强调整体审查,导致申请通货膨胀.
- 人工智能 (AI) 已经显示出在居民选择中增强人类审查的潜力,识别那些可能会被忽视的成功候选人.
研究的目的:
- 为了确定AI是否可以准确地预测眼科住院医生的匹配结果.
- 评估AI的有效性,作为提高居住匹配过程的客观性和效率的工具.
主要方法:
- 一项对513名美国医学博士毕业生进行的前性研究,来自2023-2024年旧金山比赛周期.
- 人工智能 (GPT 3.5 Turbo) 和教师使用不同的方法 (人工智能使用5个标准,教师使用标准化标签) 来前性分析完整的应用数据.
- 主要结果是每个排名列表对申请人的比赛结果的预测能力.
主要成果:
- 人工智能和教师排名清单都显著预测了与眼科住院相匹配 (P < .001).
- 人工智能排名每增加10个百分点,都与匹配几率增加23%相关 (OR=1.23).
- 每增加10个百分点的教师排名与匹配几率增加41%相关 (OR=1.41).
结论:
- 人工智能准确地预测眼科住院生匹配结果.
- 人工智能可以作为教师审查的辅助,减少行政工作量和人类偏见.
- 人工智能提供了一种可扩展的方法,以支持居住申请审查,同时保持教师监督.
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