Using Artificial Intelligence to Improve the Quality and Safety of Radiation Therapy.

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ID: 39927
2019
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Ranked #16 of 22 articles by views in journal of the american college of radiology : jacr

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Abstract
Within 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.
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pillai2019usingjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Pillai, Malvika;Adapa, Karthik;Das, Shiva K;Mazur, Lukasz;Dooley, John;Marks, Lawrence B;Thompson, Reid F;Chera, Bhishamjit S;
Journal journal of the american college of radiology : jacr
Year 2019
DOI
S1546-1440(19)30708-2
URL
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