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

[BACKGROUND] It is unclear how representative and inclusive of various patient populations generative text-to-image artificial intelligence (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 to 30, 31 to 45, 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-Wallis test or the Fisher'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 and Monk 1-5) vs dark skin color (Fitzpatrick IV-VI and Monk 6-10) between the models (P = 0.26 and P = 0.31). A significant difference was found between the models and between females vs males regarding aging (P < 0.0001 and 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.