参与者流动图用于人工智能的健康公平
Jacob G Ellen1, João Matos2, Martin Viola1
1Harvard Medical School, Boston, MA, USA.
Journal of biomedical informatics
|March 28, 2024
概括
医学研究中的选择偏见,特别是人工智能 (AI) 和机器学习 (ML),可能导致不公平的临床算法. 拟议的参与者流程图可以帮助识别和减轻这些偏见,以实现更公平的AI应用.
科学领域:
- 医学研究方法论医学研究方法论.
- 医疗保健中的人工智能
- 卫生公平性健康公平性
背景情况:
- 选择偏见是医学研究中的一个重要问题,通常源于弱势群体的代表性不足.
- 在人工智能 (AI) 和机器学习 (ML) 中使用非代表性样本可以加剧临床算法中的这些偏差.
研究的目的:
- 建议在人工智能研究中添加参与者流程图,以提高透明度.
- 在研究阶段详细介绍被排除在外的参与者的社会人口统计和临床特征.
- 在临床实施之前识别和减轻潜在的算法偏差.
主要方法:
- 倡导用于AI研究的标准化参与者流程图模型.
- 在"数据卡"倡议的基础上,为人工智能研究提供透明度.
- 介绍一个案例研究,以实际实施拟议的流程图.
主要成果:
- 拟议的参与者流程图旨在系统地记录参与者排除.
- 标准化报告可以揭示嵌入在AI应用程序中的不平等.
- 早期识别潜在偏差可以导致更可靠的算法.
结论:
- 参与者流动图对于识别人工智能研究中的选择偏差至关重要.
- 实施这些图表可以促进开发更公平,更可靠的临床算法.
- 这种方法促进了人工智能驱动的医疗保健的透明度和公平性.
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