Diversity and dermatology through the lens of generative artificial intelligence portrayals: a demographic analysis of race and gender representation.

  • Published In: International Journal of Dermatology, 2025, v. 64, n. 7. P. 1317 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Braun, Natalie; Lee, Nari; Morcos, Mary; Young, Jason 3 of 3

Abstract

The article focuses on the representation of gender and race in dermatology as depicted by text-to-image generative artificial intelligence (AI). It highlights that dermatology is one of the least diverse medical specialties and examines how AI-generated images reflect existing biases in demographic representation. The study found that AI-generated images depicted a higher proportion of White and Asian dermatologists and a lower proportion of Black dermatologists compared to the 2022 Association of American Medical Colleges (AAMC) report. Additionally, a greater percentage of women dermatologists were represented in AI images than in the AAMC data. The findings underscore the need for caution in using generative AI and call for further research to address biases in AI datasets and their implications for diversity in the field. [Extracted from the article]

Additional Information

  • Source:International Journal of Dermatology. 2025/07, Vol. 64, Issue 7, p1317
  • Document Type:Article
  • Subject Area:Communication and Mass Media
  • Publication Date:2025
  • ISSN:0011-9059
  • DOI:10.1111/ijd.17648
  • Accession Number:187570840
  • Copyright Statement:Copyright of International Journal of Dermatology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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