Implications of predicting race variables from medical images
成果类型：
Editorial Material
署名作者：
Zou, James; Gichoya, Judy Wawira; Ho, Daniel E. E.; Obermeyer, Ziad
署名单位：
Stanford University; Emory University; Stanford University; University of California System; University of California Berkeley
刊物名称：
SCIENCE
ISSN/ISSBN：
0036-10662
DOI：
10.1126/science.adh4260
发表日期：
2023-07-14
页码：
149-150
关键词：
摘要：
There are now more than 500 US Food and Drug Administration (FDA)-approved medical artificial intelligence (AI) devices, and AI is used in diverse medical tasks such as assessing the risk of heart failure and diagnosing cancer from images (1). Beyond predicting standard diagnoses, AI models can infer a substantial number of patient features from medical images in ways that humans cannot. For example, several studies have demonstrated that some AI models can infer race variables (crude simplistic categories) directly from medical images such as chest x-rays and cardiac ultrasounds, even though there are no known human-readable race correlates in these images (2-4). This has raised concerns about the possibility of AI systems to discriminate. At the same time, AI predictions of demographic attributes, including race variables, also have the potential to help assess and monitor health care disparities and generate new insights into risk factors (5).