Artificial intelligence in medical imaging education: Recommendations for undergraduate curriculum development

Abstract

Objectives: Artificial intelligence (AI) is rapidly being integrated into medical imaging practice, prompting calls to enhance AI education in undergraduate radiography programs. Combining evidence from literature, practitioner insights, and industry perspectives, this paper provides recommendations for medical imaging undergraduate education, including curriculum revision and re-alignment.

Key findings: A proposed modular framework is outlined to assist course providers in integrating AI into university programs. An example course design includes modules on data science fundamentals, machine learning, AI ethics and patient safety, governance and regulation, AI tool evaluation, and clinical applications. A proposal to embed these longitudinally in the curriculum combined with hands-on experience and work-integrated learning will help develop the necessary knowledge of AI and its real-world impacts. Authentic assessment examples reinforce learning, such as critically appraising published research and reflecting on current technologies. Maintenance of an up-to-date curriculum will require a collaborative, multidisciplinary approach involving educators, clinicians, and industry professionals.

Conclusion: Integrating AI education into undergraduate medical imaging programs equips future radiographers in an evolving technological landscape. A strategic approach to embedding AI modules throughout degree programs assures students a comprehensive understanding of AI principles, skills in utilising AI tools effectively, and the ability to critically evaluate their implications.

Implications for practice: The practical implementation of undergraduate AI education will prepare radiographers to incorporate these technologies while assuring patient care.

Keywords: Artificial intelligence; Education; Machine learning; Medical imaging.