Name of Author

Year of Publication

Study Type

Study Methods

Final outcomes

Atkinson [1]


Descriptive Report

Discussing myths around AI and its role in hijacking jobs in radiology

The activation of AI technologies was found to be one of the most crucial challenges for the user in the initial phases

Wong et al. [2]


Descriptive study

Discussion on recent developments in medicine using AI

Collaborations for use of AI with radiological assessment are incorporated which can reduce workload of clinical radiologists

Mesko [3]



Discussing role of AI in precision medicine

AI system can be updated by personalized expert knowledge, where both individual observations and images can be utilized as inputs for ANNs

Kahn [5]


Review Article

Reviewed AI techniques and its application in radiology

Invention of microchips have advanced radiological assessment of medical images

Honsy et al. [6]


Opinion Article

Discuss multiple facets of radiology

AI methods (deep learning) automatically recognizes complicated patterns in clinical images and provide qualitative assessment.

Fazal et al. [7]


Descriptive study

Discussing benefits of AI in radiology

Errors in report assessment can be reduced by using AI

Schmidt et al. [8]


Descriptive studies

Case-based reasoning was applied in making knowledge-based reasoning

Knowledge based system in medical sciences is advanced by hypertext, rule-based and case-based reasoning

Brady [11]


Review article

It outlined the errors and discrepancies in radiology, and categorized them to help understand, and contribute, both human- and system-based radiology

Humans are not able to account for the many wide-ranging qualitative characteristics in routine medical imaging examinations

Ringler et al. [12]


Retrospective study

Reports generated by Syntactic and semantic errors in radiology which were signed by 147 different radiologists from 3 January 2011 through 16 April 2014 were analyzed

Extensive medical data is required for moderate ease of retrieval and access in radiology

Motyer et al. [13]


Retrospective study

Audit of 378 finalized radiology reports

Automation using AI and leveraging big data incorporates a large number of quantitative aspects collaboratively, using an iterative technique

Grayev [17]


Editorial Paper


Even though significant outcomes were accomplished from the various unique applications of early computers AI has been more advanced in recent years.

Daniel et al. [18]


Descriptive study

On use of AI (deep learning) in image based medical diagnosis

(AI) can classify retinal images from optical coherence tomography for early diagnosis of retinal diseases

Blumke [19]


Editorial Paper

Revision of radiology criteria

The weighting of each connection as well as each neuron is used to represent the knowledge base of the system which activates other neurons

Pravedello [20]


Original study

The study annotated and adjudicated dataset of chest radiographs to make them publicly available

AI learning can train ANNs, based on generic techniques such as clustering, anomaly detection, and association, which are enhanced with each case to assure authentic diagnoses