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

Journal article

van de Venter, Riaan, Skelton, Emily, Matthew, Jacqueline, Woznitza, N., Tarroni, Giacomo, Hirani, Shashivadan P, Kumar, Amrita, Malik, Rizwan and Malamateniou, Christina 2023. Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study. Insights into Imaging. 14 (1), p. 25. https://doi.org/10.1186/s13244-023-01372-2
Authorsvan de Venter, Riaan, Skelton, Emily, Matthew, Jacqueline, Woznitza, N., Tarroni, Giacomo, Hirani, Shashivadan P, Kumar, Amrita, Malik, Rizwan and Malamateniou, Christina
AbstractArtificial 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. 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. 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. 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. [Abstract copyright: © 2023. The Author(s).]
KeywordsRadiography; Action research; Evaluation; Education; Artificial intelligence
Year2023
JournalInsights into Imaging
Journal citation14 (1), p. 25
PublisherSpringer
ISSN1869-4101
Digital Object Identifier (DOI)https://doi.org/10.1186/s13244-023-01372-2
Official URLhttps://insightsimaging.springeropen.com/articles/10.1186/s13244-023-01372-2
Publication dates
Online03 Feb 2023
Publication process dates
Accepted15 Jan 2023
Deposited20 Feb 2023
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Woznitza, N., Piper, K., Aladangady, N., Iliadis, K., Prakash, R. and Santos, R. 2014. Agreement in neonatal x-ray interpretation: a comparison between consultant paediatric radiologists and a reporting radiographer.
Advanced radiographic practice in adult chest imaging
Woznitza, N. 2014. Advanced radiographic practice in adult chest imaging.
Can reporting radiographers produce clinically relevant reports?
Woznitza, N. and Piper, K. 2011. Can reporting radiographers produce clinically relevant reports?