Gender Disparities in Artificial Intelligence-Generated Images of Hospital Leadership in the United States
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View abstract on PubMed
Summary
This summary is machine-generated.Artificial intelligence (AI) image generators create biased hospital leadership visuals, overrepresenting men and White individuals compared to real-world data. Ethical AI development is crucial for diverse representation in healthcare.
Area Of Science
- Healthcare Management
- Artificial Intelligence Ethics
- Medical Informatics
Background
- Demographic representation in leadership is critical for healthcare equity.
- Artificial intelligence (AI) text-to-image models are increasingly used, raising concerns about their outputs.
- Existing research highlights AI biases in various domains, necessitating investigation in healthcare leadership representation.
Purpose Of The Study
- To evaluate demographic representation in AI-generated images of hospital leadership roles.
- To compare AI-generated demographics with real-world data from US hospitals.
- To identify potential biases in AI models regarding gender, race/ethnicity, and age in healthcare leadership.
Main Methods
- A cross-sectional study analyzed 1200 AI-generated images from Midjourney, DALL-E 3, and Gemini Imagen 3 using standardized prompts for four leadership roles.
- Real-world demographic data from 4397 US hospitals were used for comparison.
- Two independent reviewers assessed image demographics, with interrater reliability measured using Cohen κ.
Main Results
- AI models significantly differed in demographic representation compared to real-world data.
- DALL-E 3 and Midjourney overrepresented men and White individuals.
- Gemini Imagen 3 showed near gender parity but still predominantly depicted White individuals.
Conclusions
- AI text-to-image models currently reflect and amplify systemic biases, overrepresenting men and White individuals in healthcare leadership imagery.
- Underrepresentation of diverse groups in AI-generated healthcare leadership images is a significant concern.
- Ethical AI practices, including diverse training data and fairness-aware algorithms, are essential for equitable representation in healthcare.
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