Artificial intelligence to unlock real-world evidence in clinical oncology: A primer on recent advances.

Journal: Cancer medicine
Published Date: Jun 1, 2024

Abstract

PURPOSE: Real world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time-consuming manual case-finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology.

Authors

  • Alex K Bryant
    Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.
  • Rafael Zamora-Resendiz
    Applied Mathematics & Computational Research Division, Lawrence Berkeley National Laboratory, US Department of Energy, Berkeley, CA, United States.
  • Xin Dai
    Computational Science Initiative, Brookhaven National Laboratory, Upton, NY, USA. xdai@bnl.gov.
  • Destinee Morrow
    Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
  • Yuewei Lin
    Computational Science Initiative, Brookhaven National Laboratory, Upton, New York, USA.
  • Kassidy M Jungles
    Department of Pharmacology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.
  • James M Rae
    Department of Pharmacology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.
  • Akshay Tate
    Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.
  • Ashley N Pearson
    Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.
  • Ralph Jiang
    Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.
  • Lars Fritsche
    Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA.
  • Theodore S Lawrence
    Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
  • Weiping Zou
    Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA.
  • Matthew Schipper
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Nithya Ramnath
    Division of Hematology Oncology, Department of Medicine, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.
  • Shinjae Yoo
    Computer Science and Math, Computer Science Initiative, Brookhaven National Laboratory, Upton, New York, USA.
  • Silvia Crivelli
    Applied Mathematics & Computational Research Division, Lawrence Berkeley National Laboratory, US Department of Energy, Berkeley, CA, United States.
  • Michael D Green
    Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, Michigan, USA.

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