• Malvika Pillai, Karthik Adapa, Shiva K Das, Lukasz Mazur, John Dooley, Lawrence B Marks, Reid F Thompson, and Bhishamjit S Chera.
• Carolina Health Informatics Program, University of North Carolina, Chapel Hill, North Carolina.
• J Am Coll Radiol. 2019 Sep 1; 16 (9 Pt B): 1267-1272.

AbstractWithin artificial intelligence, machine learning (ML) efforts in radiation oncology have augmented the transition from generalized to personalized treatment delivery. Although their impact on quality and safety of radiation therapy has been limited, they are increasingly being used throughout radiation therapy workflows. Various data-driven approaches have been used for outcome prediction, CT simulation, clinical decision support, knowledge-based planning, adaptive radiation therapy, plan validation, machine quality assurance, and process quality assurance; however, there are many challenges that need to be addressed with the creation and usage of ML algorithms as well as the interpretation and dissemination of findings. In this review, the authors present current applications of ML in radiation oncology quality and safety initiatives, discuss challenges faced by the radiation oncology community, and suggest future directions.Published by Elsevier Inc.

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