基于社区的初级医疗保健人工智能模型中弱势和多元群体的偏见风险缓解:快速审查议定书
Maxime Sasseville1,2, Steven Ouellet1, Caroline Rhéaume2,3
1Faculté des sciences infirmières, Université Laval, Quebec, QC, Canada.
本综述确定了评估和减轻人工智能 (AI) 算法在初级医疗保健中使用偏差的策略,重点关注弱势群体. 其目标是促进AI开发和部署中的公平,多样性和包容性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 算法偏差评估算法偏差评估
背景情况:
- 人工智能 (AI) 对人口健康和医疗保健效率有好处.
- 在了解初级和社区卫生人工智能算法如何解决偏见方面存在差距.
- 现有的AI算法可能会延续或引入影响弱势群体的偏见.
研究的目的:
- 确定用于评估初级医疗保健偏见风险的方法,针对弱势或多样化的群体的人工智能算法.
- 记录旨在减轻偏见和促进公平,多样性和包容 (EDI) 的干预措施.
- 确定哪些易受伤害的群体在AI偏见研究中被考虑.
主要方法:
- 科学文献的快速系统审查.
- 一个信息专家在4个数据库 (过去5年) 中制定了搜索策略.
- 标题/摘要由两名审稿人独立选;冲突通过共识解决.
主要成果:
- 选1022个已识别的来源;到2023年5月选47%的标题/摘要.
- 全文选和数据提取计划于2023年6月至9月进行.
- 结果将在2023年底前以定性叙事总结形式呈现.
结论:
- 该审查主要使用定性方法来确定偏见缓解策略.
- 如果有足够的定量数据出现,可能进行元分析.
- 这些发现将有助于研究人员和利益相关者识别和减少医疗保健中的AI偏见.
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