Representation of intensivists' race/ethnicity, sex, and age by artificial intelligence: a cross-sectional study of two text-to-image models.

Journal: Critical care (London, England)
PMID: 39529104

Abstract

BACKGROUND: Integrating artificial intelligence (AI) into intensive care practices can enhance patient care by providing real-time predictions and aiding clinical decisions. However, biases in AI models can undermine diversity, equity, and inclusion (DEI) efforts, particularly in visual representations of healthcare professionals. This work aims to examine the demographic representation of two AI text-to-image models, Midjourney and ChatGPT DALL-E 2, and assess their accuracy in depicting the demographic characteristics of intensivists.

Authors

  • Mia Gisselbaek
    Division of Anesthesiology, Department of Anesthesiology, Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland. Mia.gisselbaek@gmail.com.
  • Mélanie Suppan
    Division of Anesthesiology, Department of Anesthesiology, Clinical Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland.
  • Laurens Minsart
    Department of Anesthesia, Antwerp University Hospital (UZA), Edegem, Belgium.
  • Ekin Köselerli
    Department of Anesthesiology and ICU, University of Ankara School of Medicine, Ankara, Turkey.
  • Sheila Nainan Myatra
    Department of Anaesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, India.
  • Idit Matot
    Department of Anesthesia and Critical Care, Tel-Aviv Medical Center, Tel-Aviv, Israel.
  • Odmara L Barreto Chang
    Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, USA.
  • Sarah Saxena
    Department of Anesthesiology, Helora, Mons, Belgium.
  • Joana Berger-Estilita
    Institute for Medical Education, University of Bern, Bern, Switzerland.

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