Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review.

Journal: Cancer medicine
Published Date: Mar 1, 2025

Abstract

INTRODUCTION: Artificial intelligence (AI) has significant potential to improve health outcomes in oncology. However, as AI utility increases, it is imperative to ensure that these models do not systematize racial and ethnic bias and further perpetuate disparities in health. This scoping review evaluates the transparency of demographic data reporting and diversity of participants included in published clinical studies utilizing AI in oncology.

Authors

  • Anjali J D'Amiano
    Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Tia Cheunkarndee
    Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Chinenye Azoba
    Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Krista Y Chen
    Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Raymond H Mak
    Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States.
  • Subha Perni
    Harvard Radiation Oncology Program, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Mass General Brigham, Boston, MA.

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