Artificial intelligence across oncology specialties: current applications and emerging tools.

Journal: BMJ oncology
Published Date: Jan 17, 2024

Abstract

Oncology is becoming increasingly personalised through advancements in precision in diagnostics and therapeutics, with more and more data available on both ends to create individualised plans. The depth and breadth of data are outpacing our natural ability to interpret it. Artificial intelligence (AI) provides a solution to ingest and digest this data deluge to improve detection, prediction and skill development. In this review, we provide multidisciplinary perspectives on oncology applications touched by AI-imaging, pathology, patient triage, radiotherapy, genomics-driven therapy and surgery-and integration with existing tools-natural language processing, digital twins and clinical informatics.

Authors

  • John Kang
    Department of Radiation Oncology, University of Washington, Seattle, Washington, USA.
  • Kyle Lafata
    Department of Radiation Oncology, Duke University, Durham, North Carolina, USA.
  • Ellen Kim
    Department of Radiation Oncology, Brigham and Women's Hospital, Boston, Massachusetts, USA.
  • Christopher Yao
    Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada.
  • Frank Lin
    Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia.
  • Tim Rattay
    Department of Genetics and Genome Biology, University of Leicester Cancer Research Centre, Leicester, UK.
  • Harsha Nori
    Microsoft Research, Redmond, Washington, USA.
  • Evangelia Katsoulakis
    Department of Radiation Oncology, University of South Florida, Tampa, Florida, USA.
  • Christoph Ilsuk Lee
    Department of Radiology, University of Washington, Seattle, Washington, USA.

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