Demographic Inaccuracies and Biases in the Depiction of Patients by Artificial Intelligence Text-to-Image Generators

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 two commonly used text-to-image generators. A total of 4,580 images of patients with 29 different diseases was generated using the Bing Image Generator and Meta Imagine. Eight independent raters determined the sex, age, weight group, 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 as well as 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.