• Liesbeth Vandewinckele, Michaël Claessens, Anna Dinkla, Charlotte Brouwer, Wouter Crijns, Dirk Verellen, and Wouter van Elmpt.
• Department Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium; Department of Radiation Oncology, UZ Leuven, Belgium. Electronic address: liesbeth.vandewinckele@uzleuven.be.
• Radiother Oncol. 2020 Dec 1; 153: 55-66.

AbstractArtificial Intelligence (AI) is currently being introduced into different domains, including medicine. Specifically in radiation oncology, machine learning models allow automation and optimization of the workflow. A lack of knowledge and interpretation of these AI models can hold back wide-spread and full deployment into clinical practice. To facilitate the integration of AI models in the radiotherapy workflow, generally applicable recommendations on implementation and quality assurance (QA) of AI models are presented. For commonly used applications in radiotherapy such as auto-segmentation, automated treatment planning and synthetic computed tomography (sCT) the basic concepts are discussed in depth. Emphasis is put on the commissioning, implementation and case-specific and routine QA of AI models needed for a methodical introduction in clinical practice.Copyright © 2020. Published by Elsevier B.V.

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