Applications of Artificial Intelligence in Pediatric Oncology: A Systematic Review.

Journal: JCO clinical cancer informatics
Published Date: Dec 1, 2021

Abstract

PURPOSE: There is a need for an improved understanding of clinical and biologic risk factors in pediatric cancer to improve patient outcomes. Machine learning (ML) represents the application of computational inference from advanced statistical methods that can be applied to increasing amount of data available for study in pediatric oncology. The goal of this systematic review was to systematically characterize the state of ML in pediatric oncology and highlight advances and opportunities in the field.

Authors

  • Siddhi Ramesh
    Pritzker School of Medicine, University of Chicago, Chicago, IL.
  • Sukarn Chokkara
    Pritzker School of Medicine, University of Chicago, Chicago, IL.
  • Timothy Shen
    Pritzker School of Medicine, University of Chicago, Chicago, IL.
  • Ajay Major
    Department of Medicine, Section of Hematology/Oncology, University of Chicago, Chicago, IL.
  • Samuel L Volchenboum
    The University of Chicago, Chicago, IL.
  • Anoop Mayampurath
    Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States.
  • Mark A Applebaum
    Department of Pediatrics, Section of Hematology/Oncology, University of Chicago, Chicago, IL.

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