Related Experiment Video
Updated: May 20, 2025

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
Published on: May 20, 2016
Integrating AI into medical imaging curricula: Insights from UK HEIs
G Doherty1, C Hughes1, J McConnell2
1Ulster University, School of Health Sciences, Faculty of Life and Health Sciences, Shore Road, Newtownabbey, Northern Ireland, United Kingdom.
Introduction:
With artificial intelligence (AI) becoming increasingly integrated into medical imaging, the Health and Care Professions Council (HCPC) updated its Standards of Proficiency for Radiographers in Autumn 2023. These changes require clinicians to be both competent and confident in operating AI and related technologies within their role. Responsibility for meeting these standards extends beyond individual clinicians to higher education institutions (HEIs), which play a crucial role in preparing future professionals. This study examines the current and planned provision of AI education for medical imaging students and staff, identifying potential challenges in its implementation.
Methods:
An electronic survey was developed and hosted on the Joint Information Systems Committee (JISC) platform. It was disseminated in April 2023 by the Society of Radiographers to UK HEIs offering medical imaging programmes.
Results:
24 HEIs responded, with representation from all four UK nations. Of these, 71 % (n = 17) had already integrated AI into their curriculum. Reported challenges included timetabling constraints and the need to upskill staff. 21 % (n = 5) indicated that AI would be incorporated following course revalidation in the 2024/25 academic year, while the remaining two HEIs were unaware of planned changes.
Conclusion:
Most UK HEIs have begun integrating AI education into medical imaging programmes. However, significant disparities exist in the depth and scope of AI content across institutions. Further efforts are needed to develop a comprehensive and standardised AI curriculum for medical imaging in the UK.
Implications For Practice:
This study highlights key areas for improvement in AI education within medical imaging programmes. Further research into content and delivery methods is essential to ensure radiography professionals adequately equipped to navigate the evolving clinical environment.
More Related Videos
09:55Bridging the Technology Divide in the COVID-19 Era: Using Virtual Outreach to Expose Middle and High School Students to Imaging Technology
Published on: September 28, 2022
05:49Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
Published on: February 23, 2024
Related Concept Videos
Issues And Trends In Healthcare Delivery System
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Brain Imaging
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
Imaging Studies I: CT and MRI
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
Positron Emission Tomography
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
Current Trends in Nursing II
Magnetic Resonance Imaging