| Literature DB >> 31668208 |
Christopher R Deig1, Aasheesh Kanwar1, Reid F Thompson2.
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. Published by Elsevier Inc.Entities:
Keywords: Artificial intelligence; Deep learning; Machine learning
Mesh:
Year: 2019 PMID: 31668208 DOI: 10.1016/j.hoc.2019.08.003
Source DB: PubMed Journal: Hematol Oncol Clin North Am ISSN: 0889-8588 Impact factor: 3.722