Gender Disparities in Artificial Intelligence–Generated Images of Hospital Leadership in the United States
Year: 2025
Type: article
Abstract: Objective: To evaluate demographic representation in artificial intelligence (AI)-generated images of hospital leadership roles and compare them with real-world data from US hospitals. Patients and Methods: This cross-sectional study, conducted from October 1, 2024 to October 31, 2024, analyzed images generated by 3 AI text-to-image models: Midjourney 6.0, OpenAI ChatGPT DALL-E 3, and Google Gemini Imagen 3. Standardized prompts were used to create 1200 images representing 4 key leadership roles: chief executive officers, chief medical officers, chief nursing officers, and chief financial officers. Real-world demographic data from 4397 US hospitals showed that chief executive officers were 73.2% men; chief financial officers, 65.2% men; chief medical officers, 85.7% men; and chief nursing officers, 9.4% men (overall: 60.1% men). The primary outcome was gender representation, with secondary outcomes including race/ethnicity and age. Two independent reviewers assessed images, with interrater reliability evaluated using Cohen κ. Results: Interrater agreement was high for gender (κ=0.998) and moderate for race/ethnicity (κ=0.670) and age (κ=0.605). DALL-E overrepresented men (86.5%) and White individuals (94.5%). Midjourney showed improved gender balance (69.5% men) but overrepresented White individuals (75.0%). Imagen achieved near gender parity (50.3% men) but remained predominantly White (51.5%). Statistically significant differences were observed across models and between models and real-world demographics. Conclusion: 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.
Institutions: University of Geneva, University Hospital of Geneva, University of Bern, Universidade do Porto, Centre for Health Technology and Services Research, Cork University Hospital, Ankara University, ETH Zurich, Unidade Local de Saúde de Coimbra, University of Coimbra, University of California, San Francisco, University of Copenhagen, Copenhagen Academy for Medical Education and Simulation, University of Stavanger, University of Mons
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,500
OpenAlex ID: W4409258356