心脏衰竭数据集中的多样性和包容性:系统性审查
Elinor Laws1, Maria Charalambides2, Sonam Vadera3
1University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom; Institute of Applied Health Science, University of Birmingham, United Kingdom.
JACC. Advances
|March 28, 2025
概括
对心力衰竭 (HF) 的AI需要多样化的数据. 大多数数据集缺乏人口统计细节,报告的种族/种族中有89%是白人,这限制了AI的公平性. 更容易获得和透明的数据对于高频的公平AI至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 健康 公平 卫生 公平
背景情况:
- 心力衰竭 (HF) 影响全球超过6400万,需要先进的诊断和治疗工具.
- 人工智能 (AI) 显示出早期HF诊断和治疗分层的希望.
- 人工智能在HF中的有效性取决于其整个生命周期中使用的数据的代表性.
研究的目的:
- 识别和描述用于高频AI开发的数据集.
- 评估这些数据集中的数据多样性和包容性.
- 评估数据集的可访问性,地理来源,元数据报告和组成.
主要方法:
- 在MEDLINE和Embase (2012-2022) 的系统文献搜索.
- 两名审稿人对文章进行独立选,以确定相关数据集.
- 分析数据集文档,重点关注可访问性,来源,元数据和组成.
主要成果:
- 来自23个国家的72个数据集被确定,包括超过200万个人.
- 报告年龄 (86%) 和性别 (85%) 是常见的;种族/种族 (29%) 和社会经济地位 (11%) 是较少的.
- 在报告种族/种族的数据集中,89%主要是"白人"或"高加索人";只有28%的数据集是完全可访问的.
结论:
- 在HF数据集中对性别,性别和社会经济地位等关键人口统计数据的不一致报告.
- 对于高频人工智能开发的透明报告和可访问的数据集有极大需求.
- 生成和报告人口统计数据,并采取适当的保障措施,对于创建公平的人工智能驱动的健康技术至关重要.
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