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.