ArticleBJA open2026
Unmasking bias in artificial intelligence: sex and racial representation in critical care medicine through text-to-image generators.
Article in BJA open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
3 authors.
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Abstract
Background: The development of artificial intelligence (AI) has revolutionised medicine, impacting areas such as radiology, patient education, and clinical training. However, concerns about AI perpetuating societal biases are increasing. This study examines whether AI-generated images of critical care medicine physicians align with real-world demographic data, focusing on female and non-White representation. Methods: We conducted a cross-sectional study comparing 300 AI-generated physician images of adult and paediatric critical care medicine attendings and trainees from DALL-E 3, Midjourney 6.0, and Stable Diffusion 3.0 with demographic data from the Association of American Medical Colleges (AAMC) and Graduate Medical Education. Images were assessed by two reviewers with assistance from the Chicago Face Database. Discrepancies were resolved by consensus. AI and real-world data were compared using χ Results: For adult critical care medicine, the proportion of female physicians was 27.3% in real-world data, while AI platforms showed 78.3% (P<0.001) on DALL-E 3, 9.3% ( Conclusions: Significant discrepancies exist between AI-generated critical care medicine physician images and real-world demographics. Midjourney 6.0 underrepresents, while DALL-E 3 overrepresents female physicians. DALL-E 3 overrepresents non-White physicians, while other platforms more accurately depict demographics in paediatric critical care medicine. These findings highlight potential biases that have important implications for education, clinical decision-making, and public perception.
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