TY - JOUR
T1 - Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research
T2 - a pilot study
AU - van de Venter, Riaan
AU - Skelton, Emily
AU - Matthew, Jacqueline
AU - Woznitza, Nick
AU - Tarroni, Giacomo
AU - Hirani, Shashivadan P.
AU - Kumar, Amrita
AU - Malik, Rizwan
AU - Malamateniou, Christina
N1 - Funding Information:
The project was funded by the Global Challenges Research Fund (GCRF) 2020–2021, which covered the first author’s research assistant position at City, University of London for the length of the project. Dissemination costs were covered jointly between Nelson Mandela University and the City Radiography Research Fund (CRRF).
Publisher Copyright:
© 2023, The Author(s).
PY - 2023/12
Y1 - 2023/12
N2 - 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.
AB - 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.
KW - Action research
KW - Artificial intelligence
KW - Education
KW - Evaluation
KW - Radiography
UR - http://www.scopus.com/inward/record.url?scp=85147446376&partnerID=8YFLogxK
U2 - 10.1186/s13244-023-01372-2
DO - 10.1186/s13244-023-01372-2
M3 - Article
AN - SCOPUS:85147446376
SN - 1869-4101
VL - 14
JO - Insights into imaging
JF - Insights into imaging
IS - 1
M1 - 25
ER -