Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions.

Journal: Cancer discovery
Published Date: May 1, 2024

Abstract

UNLABELLED: Artificial intelligence (AI) in oncology is advancing beyond algorithm development to integration into clinical practice. This review describes the current state of the field, with a specific focus on clinical integration. AI applications are structured according to cancer type and clinical domain, focusing on the four most common cancers and tasks of detection, diagnosis, and treatment. These applications encompass various data modalities, including imaging, genomics, and medical records. We conclude with a summary of existing challenges, evolving solutions, and potential future directions for the field.

Authors

  • William Lotter
    DeepHealth Inc, Cambridge, Massachusetts.
  • Michael J Hassett
    Debra P. Ritzwoller, PhD, Institute for Health Research, Kaiser Permanente Colorado, Aurora, CO; Michael J. Hassett, MD, MPH, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, Harvard Medical School, Boston, MA; and Hajime Uno, PhD, Harvard Medical School, Boston, MA.
  • Nikolaus Schultz
    Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
  • Kenneth L Kehl
    Department of Medicine, Dana-Farber Cancer Institute, Boston, MA, 02215, United States.
  • Eliezer M Van Allen
    Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Harvard University, Boston, Massachusetts.
  • Ethan Cerami
    Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.

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