| Name of Author | Year of Publication | Study Type | Study Methods | Final outcomes |
| Atkinson [1] | 2016 | 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] | 2019 | 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] | 2017 | Editorial | 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] | 1994 | Review Article | Reviewed AI techniques and its application in radiology | Invention of microchips have advanced radiological assessment of medical images |
| Honsy et al. [6] | 2018 | 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] | 2018 | Descriptive study | Discussing benefits of AI in radiology | Errors in report assessment can be reduced by using AI |
| Schmidt et al. [8] | 2000 | 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] | 2017 | 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] | 2017 | 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] | 2016 | 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] | 2019 | Editorial Paper | Descriptive | 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] | 2018 | 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] | 2018 | 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] | 2019 | 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 |