City Research Online

Abstract

Background:
With the rapid implementation of artificial intelligence (AI) in radiographer workflows, leadership roles are necessary for its safe and effective integration into practice. Due to their dual professional identity (encompassing patient-centred care skills and technical skills) radiographers emerge as natural AI leaders within the medical imaging and radiotherapy ecosystems.

Aim:
To examine how UK radiographers perceive their readiness, confidence and potential roles in AI leadership, and to identify the barriers and enablers for their engagement within the AI-ecosystem.

Methods:
A UK-wide, cross-sectional, online survey of radiographers and students (n=273) combined demographic questions, AI knowledge and experience questions, Likert-type assessments of preparedness and free text responses. Quantitative data were analysed using descriptive statistics and Mann-Whitney U tests; qualitative data underwent thematic content analysis.

Results:
Most respondents reported limited AI literacy and minimal hands-on experience, citing insufficient education, protected time and managerial support as key barriers to leadership readiness. Confidence varied: women and those with little AI exposure, expressed statistically significant lower confidence to lead in AI-enabled environments. Respondents felt more comfortable taking on leadership responsibilities once AI systems were already in place than leading their implementation. Qualitative findings indicated that in this predominantly frontline sample, radiographers described AI leadership mainly as operational, practice-based work. Motivations for leadership focused on improving workflows, supporting colleagues and ensuring safe practice.

Discussion/conclusions:
Radiographers recognise the relevance of AI leadership but understand it as practice-proximal, operational-focused responsibilities, due to limited AI exposure, uneven confidence and the absence of defined leadership pathways in national policy. Role ambiguity and limited experiential learning constrain radiographers’ ability to envision strategic or organisation-wide AI leadership. Profession-specific education, structured experiential opportunities and organisational support are essential for enabling radiographers to participate equitably and effectively in AI-enabled service transformation.

Publication Type:	Article
Additional Information:	© The Authors. Published by BMJ. This is an open-access article distributed under the terms of Creative Commons: Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management H Social Sciences > HN Social history and conditions. Social problems. Social reform Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RA Public aspects of medicine R Medicine > RC Internal medicine
Departments:	Bayes Business School School of Health & Medical Sciences School of Health & Medical Sciences > Department of Allied Health Bayes Business School > Faculty of Management
SWORD Depositor:	Symplectic Administrator

Export

Downloads