Radiographer Education and Learning in Artificial Intelligence (REAL-AI): A survey of radiographers, radiologists, and students’ knowledge of and attitude to education on AI

Doherty, G.; McLaughlin, L.; Hughes, C.; McConnell, J.; Bond, R.; McFadden, S.

Abstract

Introduction
In Autumn 2023, amendments to the Health and Care Professions Councils (HCPC) Standards of Proficiency for Radiographers were introduced requiring clinicians to demonstrate awareness of the principles of AI and deep learning technology, and its application to practice’ (HCPC 2023; standard 12.25). With the rapid deployment of AI in departments, staff must be prepared to implement and utilise AI. AI readiness is crucial for adoption, with education as a key factor in overcoming fear and resistance. This survey aimed to assess the current understanding of AI among students and qualified staff in clinical practice.

Methods
A survey targeting radiographers (diagnostic and therapeutic), radiologists and students was conducted to gather demographic data and assess awareness of AI in clinical practice. Hosted online via JISC, the survey included both closed and open-ended questions and was launched in March 2023 at the European Congress of Radiology (ECR).

Results
A total of 136 responses were collected from participants across 25 countries and 5 continents. The majority were diagnostic radiographers 56.6 %, followed by students 27.2 %, dual-qualified 3.7 % and radiologists 2.9 %. Of the respondents, 30.1 % of respondents indicated that their highest level of qualification was a Bachelor's degree, 29.4 % stated that they are currently using AI in their role, whilst 27 % were unsure. Only 10.3 % had received formal AI training.

Conclusion
This study reveals significant gaps in training and understanding of AI among medical imaging staff. These findings will guide further research into AI education for medical imaging professionals.

Implications for practice
This paper lays foundations for future qualitative studies on the provision of AI education for medical imaging professionals, helping to prepare the workforce for the evolving role of AI in medical imaging.

Citation

Doherty, G., McLaughlin, L., Hughes, C., McConnell, J., Bond, R., & McFadden, S. (2024). Radiographer Education and Learning in Artificial Intelligence (REAL-AI): A survey of radiographers, radiologists, and students’ knowledge of and attitude to education on AI. Radiography, 30(Supplement 2), 79-87. https://doi.org/10.1016/j.radi.2024.10.010

• Journal Article Type: Article
• Acceptance Date: Oct 16, 2024
• Online Publication Date: Oct 30, 2024
• Publication Date: Oct 30, 2024
• Deposit Date: Dec 6, 2024
• Publicly Available Date: Dec 6, 2024
• Journal: Radiography
• Print ISSN: 1078-8174
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 30
• Issue: Supplement 2
• Pages: 79-87
• DOI: https://doi.org/10.1016/j.radi.2024.10.010