Accepted for/Published in: JMIR Research Protocols
Date Submitted: May 14, 2024
Open Peer Review Period: May 14, 2024 - Jul 9, 2024
Date Accepted: Dec 5, 2024
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Artificial Intelligence in radiography education: A scoping review protocol.
ABSTRACT
Background:
The expansion of Artificial Intelligence (AI) in radiography is predicted to transform future clinical workflows, decision-making, and the role and responsibilities of practitioners. If AI is to be an important aspect of radiography practice it may be reasonable to expect that graduates need to be educated and equipped to engage in competent and safe utilisation of these technologies. Therefore, the current and emerging status of AI in radiography education needs to be explored.
Objective:
The purpose of this scoping review is to map the existing literature on AI applications in radiography education and how AI technologies will potentially impact radiography education. The review will identify the various influences of AI in academic and clinical education to inform future radiography curricula.
Methods:
Using the Johanna Briggs Institute methodology, an initial search was run in EBSCOhost to determine whether the search strategy that was developed with a library specialist would capture the relevant literature by screening the title and abstract of the first 50 articles. Any additional search terms identified in the articles were then added to the search strategy. Thereafter, PubMed, Scopus, and Web of Science databases were searched. Also, grey literature was sourced from ProQuest and relevant institutional repositories. Abstract and full-text articles will be independently reviewed by two reviewers according to the predefined inclusion and exclusion criteria. Thereafter, search results will be reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR).
Results:
The database searches were concluded in April 2024 and yielded 2 827 results. The final scoping review will present the findings in tabular form and through narrative descriptions.
Conclusions:
This review will provide a better understanding of the AI applications in radiography education and how AI tools impact radiography education. Clinical Trial: https://doi.org/10.17605/OSF.IO/3NX2A
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.