Representation of intensivists’ race/ethnicity, sex, and age by artificial intelligence: a cross-sectional study of two text-to-image models
Significant biases in AI images of intensivists generated by ChatGPT DALL-E 2 and Midjourney reflect broader cultural issues, potentially perpetuating stereotypes of healthcare worker within the society. This study highlights the need for an approach that ensures fairness, accountability, transparency, and ethics in AI applications for healthcare.
Authors
- Odmara L. Barreto Chang (ORCID: https://orcid.org/0000-0002-4781-404X)
- Sheila Nainan Myatra (ORCID: https://orcid.org/0000-0001-6761-163X)
- Mia Gisselbaek (ORCID: https://orcid.org/0000-0002-2296-8106)
- Joana Berger‐Estilita (ORCID: https://orcid.org/0000-0002-8695-4264)
- Idit Matot (ORCID: https://orcid.org/0000-0003-4830-222X)
- Sarah Saxena (ORCID: https://orcid.org/0000-0002-7510-3579)
- Ekin Köselerli
- Mélanie Suppan
- Laurens Minsart
Institutions
- University of Bern (CH)
- University of Mons (BE)
- Ankara University (TR)
- Tel Aviv University (IL)
- University of California, San Francisco (US)
- Homi Bhabha National Institute (IN)
- Tata Memorial Hospital (IN)
- Hirslanden Salem-Spital (CH)
- Antwerp University Hospital (BE)
- University Hospital of Geneva (CH)
- Centre for Health Technology and Services Research (PT)
Publication Details
- Journal
- Critical Care
- Published
- 2024-11-11
- DOI
- https://doi.org/10.1186/s13054-024-05134-4
- Citations
- 21
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 2.53