Artificial Intelligence in Radiation Oncology.

Journal: Hematology/oncology clinics of North America
Published Date:

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.

Authors

  • Christopher R Deig
    Department of Radiation Oncology, Simon Cancer Center, Indiana University School of Medicine, Indianapolis, IN.
  • Aasheesh Kanwar
    Radiation Medicine, Oregon Health & Science University, 3181 Southwest Sam Jackson Park Road, Portland, OR 97239, USA.
  • Reid F Thompson
    Oregon Health and Science University, Portland, Oregon; Veterans Affairs Portland Health Care System, Portland, Oregon. Electronic address: thompsre@ohsu.edu.