Artificial Intelligence in Radiation Oncology

Christopher R. Deig, Aasheesh Kanwar, Reid F. Thompson

Research output: Contribution to journalReview article

1 Scopus citations

Abstract

The integration of artificial intelligence in the radiation oncologist's workflow has multiple applications and significant potential. From the initial patient encounter, artificial intelligence may aid in pretreatment disease outcome and toxicity prediction. It may subsequently aid in treatment planning, and enhanced dose optimization. Artificial intelligence may also optimize the quality assurance process and support a higher level of safety, quality, and efficiency of care. This article describes components of the radiation consultation, planning, and treatment process and how the thoughtful integration of artificial intelligence may improve shared decision making, planning efficiency, planning quality, patient safety, and patient outcomes.

Original languageEnglish (US)
Pages (from-to)1095-1104
Number of pages10
JournalHematology/Oncology Clinics of North America
Volume33
Issue number6
DOIs
StatePublished - Dec 2019

Keywords

  • Artificial intelligence
  • Deep learning
  • Machine learning

ASJC Scopus subject areas

  • Hematology
  • Oncology

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