Generative AI and large language models in radiography education: Possibilities, obstacles, and expectations for academic staff
Loading...
Files
Date
2026-05-01
Authors
Rainey, Clare
McLaughlin, L.
England, Andrew
Malamateniou, C.
McFadden, S. L.
Woznitza, N.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Published Version
Abstract
Artificial Intelligence (AI) has become a part of day-to-day life for many. This includes the use of AI in healthcare, where its use has been proposed to improve accuracy, make efficiencies in workflows and streamline administrative processes. The recency of the widespread use of advanced technologies has resulted in knowledge gaps for some. There remains disparity in the opinion of the public about AI in general and AI used in healthcare [1]. In the case of modern forms of AI, willingness and acceptance have been proposed to be, in part, determined by generational preferences [2], [3], [4]. The majority of the undergraduate student population in the UK is under 21 years of age (74.6%, n = 1126,070 in the academic year 2023–24) [5]. This demographic is technologically adept, as they have grown up with advanced technology and are willing to seek and use emergent technologies to their advantage in many tasks, including learning. However, a recent survey of 5218 so-called ‘Gen Z’ respondents indicated that while they are comfortable with AI use in their daily lives, they may be ‘overconfident’ in their abilities to use it critically.
Description
Keywords
Artifical intelligence , Generative AI , Higher education , Large language models , Radiography education , Students , [Medicine] , Research and Theory , Radiological and Ultrasound Technology , Health Professions (miscellaneous) , Radiology, Nuclear Medicine and Imaging , Assessment and Diagnosis
Citation
Rainey, C., McLaughlin, L., England, A., Malamateniou, C., McFadden, S. L. and Woznitza, N. (2026) 'Generative AI and large language models in radiography education: Possibilities, obstacles, and expectations for academic staff', Journal of Medical Imaging and Radiation Sciences, 57(4), 102435 (4pp). https://doi.org/10.1016/j.jmir.2026.102435