ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2025
Reinforcing the stereotype: an analysis of AI-generated images of patients with obstructive sleep apnea.
Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
What it found
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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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Who cites it
1 citing paper in PubMed.
- Bias, representation, and clinical fidelity in AI-generated images for medical education: a systematic literature review.NPJ digital medicine · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Obstructive sleep apnea (OSA) is stereotypically a condition of the middle-aged, obese, snoring man, a depiction that obscures the true diversity of vast affected populations. The growing use of artificial intelligence (AI) text-to-image generators for medical applications risks further reinforcement of this bias through prejudiced visual depictions of disease. We analyzed 1,000 images generated by ChatGPT-4o using the prompt "Person with obstructive sleep apnea" to evaluate demographics represented in its portrayals of OSA. ChatGPT-4o consistently portrayed individuals as middle-aged (98.3%), male (99.8%), White (94.7%), and obese/overweight (97.2%)-a representation differing markedly and in all axes from real-world prevalence. These findings suggest that AI-generated imagery may draw on and reinforce a narrow and outdated perception of OSA, potentially contributing to diagnostic bias and health disparities. As AI becomes increasingly integrated into clinical practice and educational tools, ensuring accurate and inclusive representations will be essential to advancing equity in sleep medicine.
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