City Research Online

Abstract

Introduction
Artificial Intelligence (AI) is rapidly changing healthcare delivery and radiography, impacting both practice and education. Despite its significance, there is limited agreement on educational priorities and curriculum organisation for radiographers. This study gathered relevant Learning Outcomes (LOs) for AI education in radiography from existing literature, structuring them according to the European Qualifications Framework (EQF) model of Knowledge, Skills and Competences (KSCs).

Methods
A literature review was conducted systematically utilising PRISMA reporting guidelines, and thematic analysis was applied using Saldaña’s coding framework. Data were coded deductively with the EQF and open-coded to highlight further important aspects of AI education for radiographers.

Results
Three major themes emerged: Knowledge, Skills, Competencies, with 15 subthemes. The recommended LOs for radiographers range from fundamental practice, such as patient safety, to advanced tasks like coding AI.

Conclusion
The variety of LOs identified suggests that AI education in radiography cannot be presented as one unified framework. Instead, educational approaches should reflect the different roles radiographers may have in relation to AI, ensuring practitioners at all levels gain the most relevant AI-KSCs.

Implications for practice
AI education should be embedded in existing educational structures, with tailored outcomes for specific AI-related roles. Ongoing research is needed to determine which LOs should be prioritised for roles and educational stages.

Publication Type:	Article
Additional Information:	© 2026 The Authors. Published by Elsevier Ltd on behalf of The College of Radiographers. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords:	Artificial intelligence, Education, Learning objectives, KSCs, Radiography, Thematic analysis
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RC Internal medicine
Departments:	School of Health & Medical Sciences School of Health & Medical Sciences > Department of Allied Health
SWORD Depositor:	Symplectic Administrator

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