ArticleClinical ophthalmology (Auckland, N.Z.)2023
Perception of Race and Sex Diversity in Ophthalmology by Artificial Intelligence: A DALL E-2 Study.
Article in Clinical ophthalmology (Auckland, N.Z.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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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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Who cites it
7 citing papers in PubMed, 19 citations in OpenAlex.
- 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
- Recapitulation of Ageism in Artificial Intelligence-Generated Images: Longitudinal Comparative Study.Journal of medical Internet research · 2025Article
- Representation of Demographics in Otolaryngology by Artificial Intelligence Text-to-Image Platforms.Laryngoscope investigative otolaryngology · 2025Article
- Evaluation of Large Language Models' Concordance With Guidelines on Olfaction.Laryngoscope investigative otolaryngology · 2025Article
- Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: In the past few years, there has been remarkable progress in accessibility of open-source artificial intelligence (AI) image generators, developed to help humans understand how AI sees our world. Here, we characterize perception of racial and sex diversity in ophthalmology by AI. Methods: OpenAI's open-source DALL E-2 AI was used for image generation of ophthalmologists with queries that all included "American" and "portrait photo". Factors used for queries contained categories of following: "Positive Characteristic", "Negative Characteristic", "Finances", "Region", "Experience", "Academic Rank", and "Subspecialty". The first 40 faces for each search were categorized on the basis of race and sex by two independent reviewers. If race or sex was not agreed upon, a third reviewer independently provided a classification, or if still indeterminate, the image was labeled as such. Images that did not adequately show facial features were excluded from categorization. Results: A total of 1560 images were included in the analysis. Control search queries specifying solely ophthalmologist sex and/or race outputted (100%) accurate images validating the tool. The query "American ophthalmologist, portrait photo" portrayed the majority of ophthalmologists as White (75%) and male (77.5%). Young/inexperienced/amateur ophthalmologists were perceived to have greater non-White racial diversity (27.5%) and female representation (28.3%) relative to old/experienced/mature ophthalmologists (23.3% non-White and 18.3% female). Ophthalmology department chairs (25%) had slightly more racial diversity compared to residents (22.5%), but residents had greater female representation (30%) compared to chairs (15%). Conclusion: Our results suggest the DALL E-2 AI may perceive a trend of increasing racial and sex diversity in younger, newer ophthalmologists compared to more senior ophthalmologists. Future investigations should attempt to validate how AI may be used as a tool to evaluate ophthalmology's progress towards becoming more inclusive of increasingly diverse ophthalmologists.
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Registered trials
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