Utilizing Generative Text-to-Image Artificial Intelligence Models to Explore Race, Gender, and Age in Plastic and Aesthetic Surgery
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.
Authors
- Sahar Borna (ORCID: https://orcid.org/0000-0002-7845-7356)
- Antonio J. Forte (ORCID: https://orcid.org/0000-0003-2004-7538)
- Cui Tao (ORCID: https://orcid.org/0000-0002-4267-1924)
- Cesar A. Gomez-Cabello (ORCID: https://orcid.org/0009-0008-0603-3192)
- Syed Ali Haider (ORCID: https://orcid.org/0009-0007-5621-2861)
- A. Genovese
- Srinivasagam Prabha (ORCID: https://orcid.org/0000-0003-2573-8493)
- Maissa Trabilsy
Institutions
- Jacksonville College (US)
- WinnMed (US)
- Digital Science (United States) (US)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- Aesthetic Surgery Journal
- Published
- 2025-05-15
- DOI
- https://doi.org/10.1093/asj/sjaf084
- Citations
- 8
- Primary Topic
- Empathy and Medical Education
- Type
- article
- Field-Weighted Citation Impact
- 11.77