Artificial intelligence learning objectives in radiography education: A document analysis

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.