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

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Abstract

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.

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Data Availability

The authors declare that they had full access to all of the data in this study and the authors take complete responsibility for the integrity of the data and the accuracy of the data analysis.

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Authors and Affiliations

  1. University of Brasília, 70840-901, Brasília, DF, Brazil

    Glaucia Nize Martins Santos MsC, Helbert Eustáquio Cardoso da Silva MsC, Paulo Tadeu de Souza Figueiredo PhD, Carla Ruffeil Moreira Mesquita PhD, Nilce Santos Melo PhD, Cristine Miron Stefani PhD & André Ferreira Leite PhD

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  1. Glaucia Nize Martins Santos MsC
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  2. Helbert Eustáquio Cardoso da Silva MsC
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Contributions

Glaucia N M Santos and Helbert E C da Silva contributed substantially to the conception or design of the work and the acquisition, analysis and interpretation of data for the work. Paulo T S Figueiredo, Carla R M Mesquita, Nilce S Melo, Cristine M Stefani and André F Leite drafted the work and revised it critically for important intellectual content.

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Correspondence to Glaucia Nize Martins Santos MsC.

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Santos, G.N.M., da Silva, H.E.C., Figueiredo, P.T.d. et al. The Introduction of Artificial Intelligence in Diagnostic Radiology Curricula: a Text and Opinion Systematic Review. Int J Artif Intell Educ 33, 1145–1174 (2023). https://doi.org/10.1007/s40593-022-00324-z

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