Deep learning is a subset of machine learning, which itself is a subset of artificial intelligence (AI) the border field of how computers mimic human behaviour. There have been huge advancements in computer vision following the success of Deep Convolutional Neural Networks (CNN) at the 2012 ImageNet challenge. Registration number PROSPERO: CRD42020154790 We will disseminate our results in a peer-reviewed publication. Ethics and disseminationĮthical approval will not be required for this study, as data will be obtained from publicly available clinical trials. Sub-group analysis will be performed by segmentation tasks, identification tasks, classification tasks, pegression/prediction tasks as well as a sub-analysis for paediatric patients. We will take a task-centred approach rather than focusing on modality or clinical subspecialty.
We will perform a literature search through MEDLINE (Pubmed), and EMBASE, a detailed data extraction of trial characteristics and a narrative synthesis of the data. We will follow the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and by the Cochrane Collaboration Handbook. We aim to investigate the use of artificial intelligence as applied to radiology, to identify the clinical questions being asked, which methodological approaches are applied to these questions and trends in use over time. While recent guidelines have been established to advise on ethics, data management and the potential directions of future research, systematic reviews of the entire field are lacking.
These studies, however, vary significantly in design and quality. There has been a recent explosion of research into the field of artificial intelligence as applied to clinical radiology with the advent of highly accurate computer vision technology.
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