Work

Teaching artificial intelligence to future radiographers: Curriculum enhancement in a New Zealand radiography course

Year: 2026

Type: article

Abstract: INTRODUCTION: Artificial intelligence (AI) is increasingly transforming radiography practice, creating a need for radiography students to develop foundational AI literacy. While it is acknowledged that AI is gaining momentum in radiography education and training initiatives are being created, an evidence gap still exists about the integration of AI content in undergraduate programmes. The aim of this Educational Perspective is to describe an approach used to integrate AI in an undergraduate programme in New Zealand (NZ). Authors' reflections and a description of how this integration was implemented are presented. METHODS: AI content was integrated into a radiography undergraduate programme to prepare students for clinical practice. The content covered key topics such as image preprocessing, segmentation, enhancement, explainable AI, and ethics-delivered through clinical examples and visual explanations tailored for students without a computer science background. Our approach emphasises accessibility, clinical relevance, and alignment with national digital health priorities. RESULTS: Preliminary student feedback was positive, highlighting increased awareness of AI's clinical applications. The paper discusses implementation insights, the importance of curriculum sequencing, and the value of ethics and explainability as entry points for engagement. Future directions include formal evaluation, integration into assessments, and potential pan-NZ collaboration on open AI teaching resources. CONCLUSION: This Educational Perspective paper highlights an approach that could be considered to integrate AI in undergraduate radiography programmes. This approach demonstrates pedagogical considerations that ensure early exposure to AI and the development of essential practice skills. Early AI education can empower future radiographers to confidently engage with emerging technologies in clinical practice.

Source: Journal of medical imaging and radiation sciences

DOI: 10.1016/j.jmir.2026.102193

Authors: Alan Wang, Beau Pontre, Sibusiso Mdletshe

Institution: University of Auckland

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Quality education

Open Access status: hybrid

Estimated APC paid: $2,900