Clinical Ophthalmology (Oct 2023)
Perception of Race and Sex Diversity in Ophthalmology by Artificial Intelligence: A DALL E-2 Study
Abstract
Hassaam S Choudhry,1 Usman Toor,1 Alexandra J Sanchez,1 Shahzad I Mian2 1Rutgers New Jersey Medical School, Newark, NJ, USA; 2University of Michigan Medical School, Ann Arbor, MI, USACorrespondence: Shahzad I Mian, University of Michigan Medical School, 1000 Wall Street, University of Michigan Medical School, Ann Arbor, MI, 48105, USA, Email [email protected]: 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.Keywords: artificial intelligence, race, sex
