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Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study

van de Venter, R., Skelton, E. ORCID: 0000-0003-0132-7948, Matthew, J. , Woznitza, N., Tarroni, G. ORCID: 0000-0002-0341-6138, Hirani, S. P. ORCID: 0000-0002-1577-8806, Kumar, A., Malik, R. & Malamateniou, C. ORCID: 0000-0002-2352-8575 (2023). Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study. Insights Imaging, 14(1), 25. doi: 10.1186/s13244-023-01372-2


BACKGROUND: Artificial intelligence (AI)-enabled applications are increasingly being used in providing healthcare services, such as medical imaging support. Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI training programmes for radiologists, formal AI education for radiographers is lacking. Therefore, this study aimed to evaluate and discuss a postgraduate-level module on AI developed in the UK for radiographers.

METHODOLOGY: A participatory action research methodology was applied, with participants recruited from the first cohort of students enrolled in this module and faculty members. Data were collected using online, semi-structured, individual interviews and focus group discussions. Textual data were processed using data-driven thematic analysis.

RESULTS: Seven students and six faculty members participated in this evaluation. Results can be summarised in the following four themes: a. participants' professional and educational backgrounds influenced their experiences, b. participants found the learning experience meaningful concerning module design, organisation, and pedagogical approaches, c. some module design and delivery aspects were identified as barriers to learning, and d. participants suggested how the ideal AI course could look like based on their experiences.

CONCLUSIONS: The findings of our work show that an AI module can assist educators/academics in developing similar AI education provisions for radiographers and other medical imaging and radiation sciences professionals. A blended learning delivery format, combined with customisable and contextualised content, using an interprofessional faculty approach is recommended for future similar courses.

Publication Type: Article
Additional Information: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit
Publisher Keywords: Action research; Artificial intelligence; Education; Evaluation; Radiography
Subjects: L Education > LB Theory and practice of education > LB2300 Higher Education
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RG Gynecology and obstetrics
Departments: School of Health & Psychological Sciences > Healthcare Services Research & Management
School of Health & Psychological Sciences > Midwifery & Radiography
School of Science & Technology > Computer Science
Text - Published Version
Available under License Creative Commons: Attribution International Public License 4.0.

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