Utilizing Generative Text-to-Image AI Models to Explore Race, Gender, and Age in Plastic and Aesthetic Surgery.

Abstract

Background: It is unclear how representative and inclusive of various patient populations generative text-to-image AI models are.

Objectives: This project explores the diversity of race, gender, and age in the images generated by AI models: DALL-E3, Midjourney, and Adobe Firefly, in response to prompts focused on liposuction, blepharoplasty, and rhinoplasty.

Methods: Prompts were designed to prompt the AI model to generate images of surgical outcomes for liposuction, blepharoplasty, and rhinoplasty for each gender, race and age combination: male vs. female, Caucasian or white, Black or African American, Latino or Hispanic, and age groups: 20-30 years, 31-45 years, and 46+ years. Each generated image was evaluated for representation of skin color by Fitzpatrick and Monk scales, sex parity using a 4-item questionnaire, and the incorporation of Westernized beauty standards. Analysis was then conducted, utilizing the Kruskal-Walis test or the Fischer's exact test between the 3 models (p<0.05).

Results: There was no significant difference between the representation of light skin color (Fitzpatrick I-III & Monk 1-5) vs. dark skin color (Fitzpatrick IV-VI & Monk 6-10) between the models (p=0.26 & p=0.31). A significant difference was found between the models and between females vs. males regarding aging (p<0.0001 & p=0.0009). There were also significant differences found for the depiction of clear skin (p <0.0001), large and/or light-colored eyes (p=0.0010), and narrow noses (p<0.0001).

Conclusions: Although there is fair representation of light skin colors and dark skin colors across the models, the depiction of gender bias and Westernized beauty standards can be improved.