Don't Fear the Artificial Intelligence: A Systematic Review of Machine Learning for Prostate Cancer Detection in Pathology.

Journal: Archives of pathology & laboratory medicine
Published Date: May 1, 2024

Abstract

CONTEXT: Automated prostate cancer detection using machine learning technology has led to speculation that pathologists will soon be replaced by algorithms. This review covers the development of machine learning algorithms and their reported effectiveness specific to prostate cancer detection and Gleason grading.

Authors

  • Aaryn Frewing
    From the Department of Physics and Astronomy, Brigham Young University, Provo, Utah.
  • Alexander B Gibson
    From the Department of Physics and Astronomy, Brigham Young University, Provo, Utah.
  • Richard Robertson
    From the Department of Physics and Astronomy, Brigham Young University, Provo, Utah.
  • Paul M Urie
    From the Department of Physics and Astronomy, Brigham Young University, Provo, Utah.
  • Dennis Della Corte
    From the Department of Physics and Astronomy, Brigham Young University, Provo, Utah.

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