Artificial Intelligence in Radiation Oncology

Christopher R. Deig, Aasheesh Kanwar, Reid Thompson

Research output: Contribution to journalReview article

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)
JournalHematology/Oncology Clinics of North America
DOIs
StateAccepted/In press - Jan 1 2019

Fingerprint

Radiation Oncology
Artificial Intelligence
Workflow
Quality of Health Care
Patient Safety
Decision Making
Referral and Consultation
Radiation
Safety
Therapeutics

Keywords

  • Artificial intelligence
  • Deep learning
  • Machine learning

ASJC Scopus subject areas

  • Hematology
  • Oncology

Cite this

Artificial Intelligence in Radiation Oncology. / Deig, Christopher R.; Kanwar, Aasheesh; Thompson, Reid.

In: Hematology/Oncology Clinics of North America, 01.01.2019.

Research output: Contribution to journalReview article

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