Current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy

Table 2 Summary of contemporary deep learning methods in quality assurance

Ref.	Architecture	Purpose
Chang et al[52], 2017	Bayesian network model	To verify and detect external beam radiotherapy physician prescription errors
Kalet et al[53], 2015	Bayesian network model	To detect any unusual outliners from treatment plan parameters
Tomori et al[54], 2018	Convolutional neural network	To predict gamma evaluation of patient-specific QA in prostate treatment planning
Nyflot et al[55], 2019	Convolutional neural network	To detect the presence of introduced RT delivery errors from patient-specific IMRT QA gamma images
Granville et al[56], 2019	Support vector classifier	To predict VMAT patient-specific QA results
Li et al[57], 2017	ANNs and ARMA time-series prediction modelling	To evaluate the prediction ability of Linac’s dosimetry trends from routine machine data for two methods (ANNs and ARMA)

QA: Quality assurance; RT: Radiotherapy; IMRT: Intensity modulated radiotherapy; VMAT: Volumetric-modulated arc therapy; ANNs: Artificial neural networks; ARMA: Autoregressive moving average.