Gender Disparities in Artificial Intelligence–Generated Images of Hospital Leadership in the United States
Artificial intelligence text-to-image models reflect and amplify systemic biases, overrepresenting men and White leaders, while underrepresenting diversity. Ethical AI practices, including diverse training data sets and fairness-aware algorithms, are essential to ensure equitable representation in health care leadership.
Authors
- Odmara L. Barreto Chang (ORCID: https://orcid.org/0000-0002-4781-404X)
- Mia Gisselbaek (ORCID: https://orcid.org/0000-0002-2296-8106)
- Mélanie Suppan (ORCID: https://orcid.org/0000-0002-8807-9619)
- Arnout Devos (ORCID: https://orcid.org/0000-0002-9425-1163)
- Joana Berger‐Estilita (ORCID: https://orcid.org/0000-0002-8695-4264)
- Sarah Saxena (ORCID: https://orcid.org/0000-0002-7510-3579)
- Ekin Köselerli
- Laurens Minsart
- Francisco Maio Matos
- Peter Dieckmann
Institutions
- University of Geneva (CH)
- University of Bern (CH)
- University of Mons (BE)
- Ankara University (TR)
- University of California, San Francisco (US)
- Hospitais da Universidade de Coimbra (PT)
- Cork University Hospital (IE)
- ETH Zurich (CH)
- Copenhagen Academy for Medical Education and Simulation (DK)
- University Hospital of Geneva (CH)
- Centre for Health Technology and Services Research (PT)
- Geneva College (US)
- University of Coimbra (PT)
- University of Stavanger (NO)
Publication Details
- Journal
- Mayo Clinic Proceedings Digital Health
- Published
- 2025-04-08
- DOI
- https://doi.org/10.1016/j.mcpdig.2025.100218
- Citations
- 3
- Primary Topic
- Diversity and Career in Medicine
- Type
- article
- Field-Weighted Citation Impact
- 11.60