确保在人工智能生成的医学图像中适当的表示:解决皮肤色调偏差的方法方法方案协议
Andrew O'Malley1, Miriam Veenhuizen1, Ayla Ahmed1
1School of Medicine, University of St Andrews, St Andrews, United Kingdom.
JMIR AI
|November 27, 2024
概括
生成型人工智能 (AI) 模型显示偏见,在医疗图像中低估了较深的皮肤色调. 用人口统计数据修改人工智能提示可以创建更具包容性和准确性的人工智能生成的医学插图.
科学领域:
- 医学教育 医学教育
- 人工智能的人工智能
- 医学插图 医学插图
背景情况:
- 生成型人工智能为教育提供定制的医疗插图.
- 在训练数据中,较深色肤色的代表性不足,在AI输出中产生了偏见.
- 确保多样化和包容性的教育材料是一个重大挑战.
研究的目的:
- 在人工智能生成的医学图像中评估皮肤色调的多样性.
- 测试修改AI提示是否可以改善皮肤色调的表现.
- 将AI输出与美国人口统计数据对齐.
主要方法:
- 两个标准的人工智能模型 (Dall-E,Midjourney) 每一个生成了100张牛皮患者的图像.
- 一个定制的人工智能模型 (Dall-E3) 使用快速注射来反映美国人口皮肤色分布.
- 用新移民调查尺度评估皮肤色调;基平方分析比较分布.
主要成果:
- 标准人工智能模型显著过度代表了较浅的皮肤色调 (P<.001).
- 采用修改提示的定制模型与美国人口皮肤色调分布 (P=.04) 密切匹配.
- 快速修改有效地解决了人工智能生成的医疗图像中的偏见.
结论:
- 人工智能生成的医疗图像表现出偏见,低估了较深的皮肤色调.
- 用人口统计数据修改人工智能提示可以确保多样化和代表性的输出.
- 对人工智能偏见的认识对于医疗和教育环境中的用户至关重要.
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