ArticleMayo Clinic proceedings. Digital health2025
Gender Disparities in Artificial Intelligence-Generated Images of Hospital Leadership in the United States.
Article in Mayo Clinic proceedings. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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Who cites it
2 citing papers in PubMed.
- Improving nursing students' clinical experience with genetics: the influence of prior knowledge and leadership support through the Donabedian Model.BMC nursing · 2026Article
- AI and inclusion in simulation education and leadership: a global cross-sectional evaluation of diversity.Advances in simulation (London, England) · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.