{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T11:05:40Z","timestamp":1774609540192,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Introduction: While the global medical graduate and student population is approximately 50% female, only 13\u201315% of cardiologists and 20\u201327% of training fellows in cardiology are female. The potentially transformative use of text-to-image generative artificial intelligence (AI) could improve promotions and professional perceptions. In particular, DALL-E 3 offers a useful tool for promotion and education, but it could reinforce gender and ethnicity biases. Method: Responding to pre-specified prompts, DALL-E 3 via GPT-4 generated a series of individual and group images of cardiologists. Overall, 44 images were produced, including 32 images that contained individual characters and 12 group images that contained between 7 and 17 characters. All images were independently analysed by three reviewers for the characters\u2019 apparent genders, ages, and skin tones. Results: Among all images combined, 86% (N = 123) of cardiologists were depicted as male. A light skin tone was observed in 93% (N = 133) of cardiologists. The gender distribution was not statistically different from that of actual Australian workforce data (p = 0.7342), but this represents a DALL-E 3 gender bias and the under-representation of females in the cardiology workforce. Conclusions: Gender bias associated with text-to-image generative AI when using DALL-E 3 among cardiologists limits its usefulness for promotion and education in addressing the workforce gender disparities.<\/jats:p>","DOI":"10.3390\/info15100594","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T07:19:37Z","timestamp":1727680777000},"page":"594","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Gender Bias in Text-to-Image Generative Artificial Intelligence When Representing Cardiologists"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6180-8586","authenticated-orcid":false,"given":"Geoffrey","family":"Currie","sequence":"first","affiliation":[{"name":"School of Dentistry and Medical Sciences, Charles Sturt University, Wagga Wagga, NSW 2678, Australia"},{"name":"Department of Radiology, Baylor College of Medicine, Houston, TX 77030, USA"}]},{"given":"Christina","family":"Chandra","sequence":"additional","affiliation":[{"name":"Faculty of Science, School of Psychology, UNSW, Sydney NSW 2052, Australia"}]},{"given":"Hosen","family":"Kiat","sequence":"additional","affiliation":[{"name":"Cardiac Health Institute, Sydney, NSW 2121, Australia"},{"name":"College of Health and Medicine, Australian National University, Canberra, ACT 2601, Australia"},{"name":"Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW 2109, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e214820","DOI":"10.1001\/jamahealthforum.2021.4820","article-title":"Distinguishing workforce diversity from health equity efforts in medicine","volume":"2","author":"Crews","year":"2021","journal-title":"JAMA Health Forum"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"E912","DOI":"10.1001\/amajethics.2021.912","article-title":"How should medical school admissions drive health care workforce diversity?","volume":"23","author":"Lee","year":"2021","journal-title":"AMA J. 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