TY - JOUR
T1 - Using Artificial Intelligence to Improve the Quality and Safety of Radiation Therapy
AU - Pillai, Malvika
AU - Adapa, Karthik
AU - Das, Shiva K.
AU - Mazur, Lukasz
AU - Dooley, John
AU - Marks, Lawrence B.
AU - Thompson, Reid F.
AU - Chera, Bhishamjit S.
N1 - Funding Information:
Malvika Pillai is funded by the NLM T15 training grant #T15-LM012500. The authors state that they have no conflict of interest related to the material discussed in this article. The contents do not represent the views of the US Department of Veterans Affairs or the US government. We would like to thank Dr Join Luh for his critical reading of the manuscript.
Publisher Copyright:
© 2019
PY - 2019/9
Y1 - 2019/9
N2 - 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.
AB - 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.
KW - Radiation oncology
KW - artificial intelligence
KW - machine learning
KW - quality and safety
KW - radiation therapy
UR - http://www.scopus.com/inward/record.url?scp=85069506397&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069506397&partnerID=8YFLogxK
U2 - 10.1016/j.jacr.2019.06.001
DO - 10.1016/j.jacr.2019.06.001
M3 - Article
C2 - 31492404
AN - SCOPUS:85069506397
SN - 1558-349X
VL - 16
SP - 1267
EP - 1272
JO - Journal of the American College of Radiology
JF - Journal of the American College of Radiology
IS - 9
ER -