Table 1.

Applications and Challenges of AI in radiology

Study	Applications	Challenges
Lee et al. [1]	Image segmentation and registration Automatic labelling and captioning CAD	Quality and amount of training data Explaining “technical bases” of the system Legal/ethical issues
Sailer et al. [4]	Diagnostic support through classification of images and outcome/risk predictions	N/A
Do et al. [5]	Automation using AI notified clinicians faster by a median of 1 hour and decreased radiologist exam interpretation time by 37% Concordance of target lesion measurements improved from 22.5% to 67.8%	N/A
Chassagnon et al. [7]	Thoracic imaging, specifically lung nodule evaluation, tuberculosis/pneumonia detection, and quantification of diffuse lung diseases	Current algorithms are limited to isolated findings
Stoel et al. [8]	Rheumatological imaging with a focus on rheumatoid arthritis and systemic sclerosis	N/A
Maurowski et al. [9]	Improve disease detection, decrease unnecessary procedures, improve outcomes, and reduce costs	Concern for diminished pay and prestige for radiologists Goals of AI developers may not coincide with the altruistic goals of healthcare
Poortmans et al. [10]	Dose distribution optimization to reduce unnecessary radiation to non-target organs	N/A
Kulkarni et al. [11]	Tuberculosis diagnosis through chest radiography, computer-aided diagnosis systems, and DL algorithms	N/A
Iezzi et al. [12]	Pattern recognition and identification, language comprehension, object and sound recognition, prognosticating diseases, determining indication for therapy, estimating the outcomes/benefits	High quality data sets are required for training Often do not disclose the statistical rationale, which makes medical application difficult
Meek et al. [13]	Imaging, prediction modelling, and decision support Angiography-based ML to provide real-time estimates of fractional flow reserve, which is used to identify ischaemia-related stenosis in coronary artery disease Fusion of images would allow precise guidance during procedures Generate CT image from MRI data ML algorithms may be used to guide treatment plans Combining ML with augmented reality systems offers a new method of training and testing trainees	Large amounts of data would be required to train the algorithms, which is further complicated by the ever-changing nature of clinical practice, which may limit “the usefulness of retrospective data”