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ORIGINAL RESEARCH article
Front. Digit. Health
Sec. Human Factors and Digital Health
Volume 7 - 2025 | doi: 10.3389/fdgth.2025.1537907
Evaluating Diversity and Stereotypes Amongst AI Generated Representations of Healthcare Providers
Provisionally accepted- 1 Department of Computer Science, College of Natural Sciences, The University of Texas at Austin, Austin, Texas, United States
- 2 Ridgewood Public Schools, Ridgewood, New Jersey, United States
- 3 Rouse High School, Leander, United States
- 4 Valley Health System, Paramus, New Jersey, United States
Introduction - Generative artificial intelligence (AI) can simulate existing societal data, which led us to explore diversity and stereotypes among AI-generated representations of healthcare providers. Methods - We used DALL-E 3, a text-to-image generator, to generate 360 images from healthcare profession terms tagged with specific race and sex identifiers. These images were evaluated for sex and race diversity using consensus scoring. To explore stereotypes present in the images, we employed Google Vision to label objects, actions, and backgrounds in the images. Results - We found modest levels of sex diversity (3.2) and race diversity (2.8) on a 5-point scale, where 5 indicates maximum diversity. These findings align with existing workforce statistics, suggesting that Generative AI reflects real-world diversity patterns. The analysis of Google Vision image labels revealed sex and race-linked stereotypes related to appearance, facial expressions, and attire.Discussion - This study is the first of its kind to provide a ML-based framework for quantifying diversity and biases amongst generated AI images of healthcare providers. These insights can guide policy decisions involving the use of Generative AI in healthcare workforce training and recruitment.
Keywords: Generative AI, Sex diversity, Race diversity, Healthcare provider, stereotypes, DALL.E, Google vision, machine learning
Received: 02 Dec 2024; Accepted: 31 Mar 2025.
Copyright: © 2025 Agrawal, Gupta, Agrawal and Gupta. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Himanshu Gupta, Valley Health System, Paramus, New Jersey, United States
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