Demographic inaccuracies and biases in the depiction of patients by artificial intelligence text-to-image generators.

Journal: NPJ digital medicine
Published Date: Jul 19, 2025

Abstract

The wide usage of artificial intelligence (AI) text-to-image generators raises concerns about the role of AI in amplifying misconceptions in healthcare. This study therefore evaluated the demographic accuracy and potential biases in the depiction of patients by four commonly used text-to-image generators. A total of 9060 images of patients with 29 different diseases was generated using Adobe Firefly, Bing Image Generator, Meta Imagine, and Midjourney. Twelve independent raters determined the sex, age, weight, and race and ethnicity of the patients depicted. Comparison to the real-world epidemiology showed that the generated images failed to depict demographical characteristics such as sex, age, and race and ethnicity accurately. In addition, we observed an over-representation of White and normal weight individuals. Inaccuracies and biases may stem from non-representative and non-specific training data as well as insufficient or misdirected bias mitigation strategies. In consequence, new strategies to counteract such inaccuracies and biases are needed.

Authors

  • Tim Luca Till Wiegand
    cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany. tim.wiegand@med.uni-muenchen.de.
  • Leonard Ben Jung
    cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany.
  • Jonas Anton Gudera
    OneAIM, Munich, Germany.
  • Luisa Sophie Schuhmacher
    cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany.
  • Paulina Moehrle
    OneAIM, Munich, Germany.
  • Jon Felix Rischewski
    OneAIM, Munich, Germany.
  • Pardiss Mehrzad
    Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, USA.
  • Subin Jeong
    Harvard School of Dental Medicine, Boston, MA, USA.
  • Lisa Ha Nguyen
    cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany.
  • Michael Poeschla
    Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, USA.
  • Laura Isabella Velezmoro
    OneAIM, Munich, Germany.
  • Linus Kruk
    OneAIM, Munich, Germany.
  • Konstantinos Dimitriadis
    OneAIM, Munich, Germany.
  • Inga Katharina Koerte
    cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany.

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