Observational studyCritical care (London, England)2024
Representation of intensivists' race/ethnicity, sex, and age by artificial intelligence: a cross-sectional study of two text-to-image models.
Observational study in Critical care (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The trial behind it
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Who cites it
8 citing papers in PubMed.
- Unmasking bias in artificial intelligence: sex and racial representation in critical care medicine through text-to-image generators.BJA open · 2026Article
- Bias, representation, and clinical fidelity in AI-generated images for medical education: a systematic literature review.NPJ digital medicine · 2026Article
- Perception of sex and race diversity in critical care medicine by generative AI: biases, measurement and implications.Critical care (London, England) · 2026Article
- Artificial intelligence to improve patient care in emergency medicine: a workflow-based analysis.Internal and emergency medicine · 2026Article
- Gender Disparities in Artificial Intelligence-Generated Images of Hospital Leadership in the United States.Mayo Clinic proceedings. Digital health · 2025Article
- Bridging the gap between scientists and clinicians: addressing collaboration challenges in clinical AI integration.BMC anesthesiology · 2025Article
- AI and inclusion in simulation education and leadership: a global cross-sectional evaluation of diversity.Advances in simulation (London, England) · 2025Article
- Association of impostor phenomenon and burnout among Swiss residents and junior anaesthesiologists: results of a cross-sectional survey.BMC anesthesiology · 2025Observational
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIntegrating artificial intelligence (AI) into intensive care practices can enhance patient care by providing real-time predictions and aiding clinical decisions. However, biases in AI models can undermine diversity, equity, and inclusion (DEI) efforts, particularly in visual representations of healthcare professionals. This work aims to examine the demographic representation of two AI text-to-image models, Midjourney and ChatGPT DALL-E 2, and assess their accuracy in depicting the demographic characteristics of intensivists.
methodsThis cross-sectional study, conducted from May to July 2024, used demographic data from the USA workforce report (2022) and intensive care trainees (2021) to compare real-world intensivist demographics with images generated by two AI models, Midjourney v6.0 and ChatGPT 4.0 DALL-E 2. A total of 1,400 images were generated across ICU subspecialties, with outcomes being the comparison of sex, race/ethnicity, and age representation in AI-generated images to the actual workforce demographics.
resultsThe AI models demonstrated noticeable biases when compared to the actual U.S. intensive care workforce data, notably overrepresenting White and young doctors. ChatGPT-DALL-E2 produced less female (17.3% vs 32.2%, p < 0.0001), more White (61% vs 55.1%, p = 0.002) and younger (53.3% vs 23.9%, p < 0.001) individuals. While Midjourney depicted more female (47.6% vs 32.2%, p < 0.001), more White (60.9% vs 55.1%, p = 0.003) and younger intensivist (49.3% vs 23.9%, p < 0.001). Substantial differences between the specialties within both models were observed. Finally when compared together, both models showed significant differences in the Portrayal of intensivists.
conclusionsSignificant biases in AI images of intensivists generated by ChatGPT DALL-E 2 and Midjourney reflect broader cultural issues, potentially perpetuating stereotypes of healthcare worker within the society. This study highlights the need for an approach that ensures fairness, accountability, transparency, and ethics in AI applications for healthcare.
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