Gender and Ethnicity Bias of Text-to-Image Generative Artificial Intelligence in Medical Imaging, Part 2: Analysis of DALL-E 3.

IF 1 Q4 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Geoffrey Currie, Johnathan Hewis, Elizabeth Hawk, Eric Rohren
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Abstract

Disparity among gender and ethnicity remains an issue across medicine and health science. Only 26%-35% of trainee radiologists are female, despite more than 50% of medical students' being female. Similar gender disparities are evident across the medical imaging professions. Generative artificial intelligence text-to-image production could reinforce or amplify gender biases. Methods: In March 2024, DALL-E 3 was utilized via GPT-4 to generate a series of individual and group images of medical imaging professionals: radiologist, nuclear medicine physician, radiographer, nuclear medicine technologist, medical physicist, radiopharmacist, and medical imaging nurse. Multiple iterations of images were generated using a variety of prompts. Collectively, 120 images were produced for evaluation of 524 characters. All images were independently analyzed by 3 expert reviewers from medical imaging professions for apparent gender and skin tone. Results: Collectively (individual and group images), 57.4% (n = 301) of medical imaging professionals were depicted as male, 42.4% (n = 222) as female, and 91.2% (n = 478) as having a light skin tone. The male gender representation was 65% for radiologists, 62% for nuclear medicine physicians, 52% for radiographers, 56% for nuclear medicine technologists, 62% for medical physicists, 53% for radiopharmacists, and 26% for medical imaging nurses. For all professions, this overrepresents men compared with women. There was no representation of persons with a disability. Conclusion: This evaluation reveals a significant overrepresentation of the male gender associated with generative artificial intelligence text-to-image production using DALL-E 3 across the medical imaging professions. Generated images have a disproportionately high representation of white men, which is not representative of the diversity of the medical imaging professions.

医学影像中文本到图像生成人工智能的性别和种族偏差,第 2 部分:《DALL-E 3》分析。
在整个医学和健康科学领域,性别和种族差异仍然是一个问题。只有 26%-35% 的放射科实习医生是女性,尽管 50% 以上的医学生是女性。类似的性别差异在医学影像专业中也很明显。人工智能文本到图像的生成可能会强化或放大性别偏见。方法:2024 年 3 月,通过 GPT-4 使用 DALL-E 3 生成一系列医学影像专业人员的个人和群体图像:放射科医师、核医学医师、放射技师、核医学技师、医学物理学家、放射药剂师和医学影像护士。使用各种提示多次重复生成图像。总共生成了 120 幅图像,用于评估 524 个字符。所有图像均由 3 位医学影像专业的专家评审员进行独立分析,以确定明显的性别和肤色。结果总体而言(个人和群体图像),57.4%(n = 301)的医学影像专业人员为男性,42.4%(n = 222)为女性,91.2%(n = 478)为浅肤色。男性在放射科医生中占 65%,在核医学医生中占 62%,在放射技师中占 52%,在核医学技师中占 56%,在医学物理学家中占 62%,在放射药剂师中占 53%,在医学影像护士中占 26%。在所有职业中,男性的比例都高于女性。没有残疾人代表。结论这项评估显示,在使用 DALL-E 3 生成人工智能文本到图像的过程中,医学影像专业的男性比例明显偏高。生成的图像中白人男性比例过高,这并不代表医学影像专业的多样性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of nuclear medicine technology
Journal of nuclear medicine technology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
1.90
自引率
15.40%
发文量
57
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