一个新的框架,将关于性别作为生物变量的数据纳入医学教育中
Haleigh Larson1, Aeka Guru2, Kelsey Martin3
1Yale School of Medicine, New Haven, Connecticut, USA.
Journal of women's health (2002)
|December 3, 2025
概括
人工智能 (AI) 发现了医学课程中关于性别作为生物变量 (SABV) 的差距. 这一人工智能驱动的审查提供了一个可扩展的方法,通过整合关键的SABV数据来改善医疗教育,为未来的医生提供了关键的SABV数据.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 健康差异 在健康上的差异
背景情况:
- 越来越多的证据强调了性别和性别对健康结果的影响.
- 医学院的课程往往缺乏全面整合这种基于性别和性别的健康信息.
- 需要系统地评估和加强将此类数据纳入医疗培训的必要性.
研究的目的:
- 在医学课程中使用人工智能 (AI) 来评估性别作为生物变量 (SABV) 和性别作为健康的社会决定因素的存在.
- 确定课程中的特定领域,可以增强SABV和性别相关内容.
- 制定建议,将基本的基于性别和性别的健康数据纳入医学教学材料.
主要方法:
- 利用人工智能工具Humata.ai分析了26个临床学科的耶鲁医学院2023-2024年职务预备课程的教育材料.
- 发布的AI命令确定有关SABV和性别的现有讨论,并确定适合纳入此类数据的内容区域.
- 对AI分析的重点是绘制性别和基于性别的健康信息的纳入和潜在整合的地图.
主要成果:
- 作为一个生物变量 (SABV) 的性别被发现在26个临床学科中的12个被审查.
- 发现了与病理生理学,药理学,诊断和治疗有关的SABV数据的整合的重大机会.
- 虽然性别和性别都被查询,但AI的输出主要集中在SABV.
结论:
- 这项研究展示了一个新的,可扩展的AI框架,用于高效地审查医疗课程.
- 人工智能辅助的方法提高了医疗教育中临床相关的SABV内容的整合.
- 实施这种人工智能驱动的审查可以帮助确保未来的医生接受全面的,基于证据的关于性别对健康影响的培训,最终改变临床护理.
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