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Hematol Oncol Clin North Am. 2019 Dec;33(6):1095-1104. doi: 10.1016/j.hoc.2019.08.003. Epub 2019 Sep 11.

Artificial Intelligence in Radiation Oncology.

Author information

1
Radiation Medicine, Oregon Health & Science University, 3181 Southwest Sam Jackson Park Road, Portland, OR 97239, USA.
2
Radiation Medicine, Oregon Health & Science University, 3181 Southwest Sam Jackson Park Road, Portland, OR 97239, USA; Hospital & Specialty Medicine, VA Portland Healthcare System, 3710 SW US Veterans Hospital Road, Portland, OR 97239, USA. Electronic address: thompsre@ohsu.edu.

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.

KEYWORDS:

Artificial intelligence; Deep learning; Machine learning

PMID:
31668208
DOI:
10.1016/j.hoc.2019.08.003

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