Demographic inaccuracies and biases in the depiction of patients by artificial intelligence text-to-image generators - Tim Luca Till Wiegand; Leonard Ben Jung; Jonas Anton Gudera; Luisa Sophie Schuhmacher; Paulina Moehrle; Jon Felix Rischewski; Pardiss Mehrzad; Subin Jeong; Lisa Ha Nguyen; Michael Poeschla; Laura Isabella Velezmoro; Linus Kruk; et al. (npj Digital Medicine)
Abstract The wide usage of artificial intelligence (AI) text-to-image generators raises concerns about the role of AI in amplifying misconceptions in healthcare. This study therefore evaluated the demographic accuracy and potential biases in the depiction of patients by four commonly used text-to-image generators. A total of 9060 images of patients with 29 different diseases was generated using Adobe Firefly, Bing Image Generator, Meta Imagine, and Midjourney. Twelve independent raters determined the sex, age, weight, and race and ethnicity of the patients depicted. Comparison to the real-world epidemiology showed that the generated images failed to depict demographical characteristics such as sex, age, and race and ethnicity accurately. In addition, we observed an over-representation of White and normal weight individuals. Inaccuracies and biases may stem from non-representative and non-specific training data as well as insufficient or misdirected bias mitigation strategies. In consequence, new strategies to counteract such inaccuracies and biases are needed.