Deyer et al. [21]



Discussing application of AI to radiology

ANN process allows the continuous progression of the system, learning in a similar way that corresponds with the human brain, but with the benefit of a permanent memory function

Schier [22]



Providing an alternative view on radiology practice and AI

The accuracy rates of ANNs currently surpass human radiologists in narrow-based tasks, such as detecting lung nodule features

Stivaros et al. [23]


Review article

Reviews role of decision support systems in radiological assessment

Machine learning permits reliable automated detection of lung nodules in CT scans, and pneumonia in chest x-rays

Porto Pazos [24]



Use of biological process application in advancement of AI

The behaviour of pre-cancerous lesions on CT scans is predicted by means of modeling, or regression, and prevents superfluous invasive examinations, such as biopsy

Pesapane et al. [25]


Narrative review

Discusses the role of practicing radiologist in promoting AI in radiology

AI will help radiologists in being a multidisciplinary team, be more on forefront with patients and add value to tasks

Kamar [26]


Review paper

Considering Human intelligence in AI

Electronic health records could improve the success rate of radiologists

Fieschi [27]



Discusses expert systems of AI in medicne

Radiologists can distinguish themselves by creating AI that does hybrid work through collective intelligence software

Danforth et al. [28]


Project writing

Developed a virtual patient simulations system that will help in medical education

Intelligence augmentation in radiology is where higher levels of accuracy in diagnosis are achieved through the amalgamation of human radiologists and AI to form hybrid intelligence

Thrall et al. [29]


Descriptive study

Discusses opportunities and challenges faced by AI in radiology

Value created by AI will measure its role in radiology i.e., by increasing diagnostic certainty, quicker turnaround, improved patient outcomes, and for radiologists a better quality of work life

Gillies et al. [30]


Special report

Describes the process of radiomics, its potential power to facilitate better clinical decision making and its challenges; especially in cancer patients

It is important to consider aspects such as picture archiving and communication system (PACS), electronic health records, IT environments, and radiology information systems in the implementation phase of AI

Prevedello et al. [31]


Retrospective study

AI algorithm performance was tested on a separate dataset containing 35 with noncritical findings, 15 with suspected acute infarct findings, and 50 with hemorrhage, mass effect, or hydrocephalus findings

AI (deep learning) is promising for detecting noncontrast-enhanced head CT, whereas, to detect suspected acute infarction required dedicated algorithm. Detection of suspected acute infarct had lower sensitivity compared to hemorrhage, mass effect, or hydrocephalus detection, but showed reasonable performance

Hanson [32]


Review article

Databases were searched for articles regarding application of AI in radiographs used in intensive care units

Unsupervised learning is one approach that allows automated data curation

Kulikowski [33]


Review article

A brief history of articles before 1988 on the use of AI in medicine is reviewed in a systematic way

Recent developments in unsupervised learning include different auto-encoders and generative adversarial networks, which are highly effective, where discriminated aspects are learned despite a lack of comprehensive labelling