Medicine has historically been a male-dominated profession, but in recent years, women have made up more than half of medical graduates. However, studies show that up to two-thirds of women face selection bias and workplace discrimination, particularly in surgical specialties. In the United States, only about 7% of surgeons are non-White, and Black female surgeons make up less than 1% of academic surgical faculty despite comprising over 7.5% of the population. Black principal investigators also received less than 0.4% of National Institutes of Health (NIH) grants between 1998 and 2017.
A study published in JAMA Network Surgery examined the disparities in programming artificial intelligence (AI) text-to-image generators and found that two out of three of the most frequently used AI generators showed over 98% of images representing surgeons as White and male. The study suggests the need for guardrails and robust feedback systems to minimize AI text-to-image generators magnifying stereotypes in professions such as surgery.
The study found that Whites and males were over-represented among attending surgeons, while females made up 15% and non-Whites 23%. Among surgical trainees, about 36% were female, and 37% were non-White. When the surgeon prompt was used with DALL E 2, the proportions of female and non-White images produced reflected demographic data accurately at 16% and 23%, respectively. However, when using Midjourney and Stable Diffusion, images of female surgeons were absent or made up less than 2% of the total, and images of non-White surgeons were almost absent at less than 1% in each case.
The study also found that when geographic prompts were added, the proportion of non-White surgeons increased among the images, but none of the models increased female representation after specifying China or Nigeria. The researchers compared actual surgeons and surgical students to the representations produced by the study’s three most popular AI generators and found that current societal biases were magnified using two out of three of the most frequently used AI generators.
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