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皮肤色调和性别在皮肤病学中的表现由生成人工智能:一项比较研究

Goranit Sakunchotpanit^1,2, Krithika Nayudu^1,3, Ryan Chen^1,4

¹Department of Dermatology, Brigham and Women's Hospital, Boston, MA, USA.

Clinical and experimental dermatology

|March 13, 2025

概括

生成型人工智能图像平台显示显著的偏见,压倒性地产生皮肤肤明亮和皮肤病的男性描绘. 这凸显了对人工智能算法的迫切需要,以改善产生的医学图像中的皮肤色调和性别多样性.

科学领域:

皮肤病学皮肤病学
人工智能的人工智能
医学教育医学教育

背景情况:

生成型人工智能 (AI) 在皮肤学教育中表现有前途.
在人工智能生成的图像中评估皮肤色调多样性对于公平的患者护理至关重要.
当前的人工智能平台可能会延续医学表述中的偏见.

研究的目的:

在人工智能生成的皮肤图像中评估皮肤色调和性别表示.
为了识别流行的生成人工智能平台中的偏见:DALL-E3,Midjourney和DreamStudio Beta.
为了强调对各种医学图像改进人工智能算法的需求.

主要方法:

使用DALL-E3,Midjourney和DreamStudio Beta生成了八种常见皮肤病的五张图像.
使用马西-马丁皮肤颜色尺度评估皮肤色调的图像.
根据可识别的特征确定性别,并评估评分器间的可靠性.

主要成果:

产生了120张图像;88%的图像具有一致的皮肤色调评级,91%的图像显示了可识别的性别.
在评级图像中,96.6%是浅色,3.4%是中色,0%是深色.
在具有可识别性别的图像中,68%是男性,32%是女性.

结论:

常见的AI平台在皮肤色和性别表现方面表现出相当大的偏见.
人工智能生成的皮肤学图像主要包括浅色皮肤和男性个体.
人工智能算法非常需要解决和减轻这些偏见,以实现包容性医学教育和患者护理.

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