Introduction to Artificial Intelligence and Machine Learning for Pathology.

Journal: Archives of pathology & laboratory medicine
Published Date: Oct 1, 2021

Abstract

CONTEXT.—: Recent developments in machine learning have stimulated intense interest in software that may augment or replace human experts. Machine learning may impact pathology practice by offering new capabilities in analysis, interpretation, and outcomes prediction using images and other data. The principles of operation and management of machine learning systems are unfamiliar to pathologists, who anticipate a need for additional education to be effective as expert users and managers of the new tools.

Authors

  • James H Harrison
    From the Division of Laboratory Medicine, Department of Pathology, University of Virginia School of Medicine and Health System, Charlottesville. Dr Yu is currently located in the Department of Pathology and Laboratory Medicine, University of Kentucky Medical Center, Lexington.
  • John R Gilbertson
    The Departments of Biomedical Informatics and Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania (Gilbertson).
  • Matthew G Hanna
    Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
  • Niels H Olson
    The Defense Innovation Unit, Mountain View, California (Olson).
  • Jansen N Seheult
    The Department of Pathology, University of Pittsburgh, and Vitalant Specialty Labs, Pittsburgh, Pennsylvania (Seheult).
  • James M Sorace
    The US Department of Health and Human Services, retired, Lutherville, Maryland (Sorace).
  • Michelle N Stram
    The Department of Forensic Medicine, New York University, and Office of Chief Medical Examiner, New York, New York (Stram).

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