The Introduction of Artificial Intelligence in Diagnostic Radiology Curricula: a Text and Opinion Systematic Review.

Background: Artificial intelligence (AI) is able to emulate human performance on a task and may improve the radiologists' work. This text and opinion review explored the implementation of AI in diagnostic radiology education curricula at pre-licensure training/education in healthcare. The question was: what are the pedagogical possibilities, advantages and challenges of AI use in diagnostic radiology education? Methods: Primary research studies, reviews, systematic reviews, meta-analyses, letters, texts, expert opinions, expert consensus, discussion papers and guidelines about diagnostic radiology education at the undergraduate and postgraduate levels of any field of health sciences were considered. Searches were conducted on indexed databases and grey literature. Data on the context, potentials and challenges were collected from the text and opinion papers and the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Text and Opinion Papers was applied to assess methodological quality. From the experience papers, intervention, experiences and results were extracted parameters and an adapted JBI Critical Appraisal Checklist for Case Reports was applied. Results: Seventeen studies met the inclusion criteria. Personalization, training facilities and the standardization of radiology teaching were the main potentials identified. Five main challenges were also observed: the validation of AI tools in radiology education, the learning curve, universities' aptitude to teach AI, the digitization of radiological images and how to include AI in radiology curricula. Conclusion: The necessity to update radiology curricula to include AI is a consensus. Time is required for development of the learning curve among AI developers, teachers and trainees. When and to what extent AI should be taught in radiology courses needs further exploration. [ABSTRACT FROM AUTHOR]