Digital Radiographer Leaders: Exploring leadership roles and competencies for radiographers to lead safe and effective AI digital transformation

Background: Diagnostic radiographers use cutting edge technology to produce medical diagnostic images such as x-rays or scans. Therapeutic radiographers use complex equipment to treat cancer or skin deformities with radiation. Both radiographer roles have a unique healthcare skillset that encompasses the use of technology alongside a strong patient centred care ethos.

Radiographers hold a bridging role, being the interface between technology and patients. Artificial Intelligence (AI) is increasingly being used by radiographers to image and plan treatment for people in the UK. Robust leadership has been highlighted as a vital component of safe and effective AI adoption, and while there has been huge investment in health technology and AI, there has been little investment in preparing digital leaders to manage and implement such technologies.

Within healthcare, and radiology in particular, there are leadership gaps when it comes to implementing AI. Most healthcare workers state they lack the knowledge, skills, or confidence to work with AI, let alone lead in its implementation. Radiographers are ideally positioned to lead AI changes and enable safe and effective AI implementation.

Currently there are no established AI leadership roles for radiographers, and the workforce requires managerial support, guidance, and specific AI educational resources to establish these roles.

Aim: To explore leadership tasks, competencies, and roles for radiographers, so they can lead the safe and effective implementation of AI in healthcare.

Objectives: i) Identify gaps in radiographer competencies and educational provisions needed to transform radiographers into digital leaders ii) Produce recommendations on the key competencies and career pathways for the development of radiographers into AI leadership roles iii) Create role profiles (role description and responsibilities) for different radiographer AI leadership roles iv) Propose educational resources required by potential AI leader radiographers, and help guide their development.

Methods: Utilisation of a pragmatic philosophical approach with a participatory action research (PAR) methodology. PAR is ideal when a knowledge gap or practice gap is identified by practitioners, and they set off to address this challenge using research. Data collection methods include: i) An extensive document analysis to scope out information on current AI healthcare leadership roles, role of leadership in AI implementation in healthcare, challenges and associated educational resources.

Content analysis will be used to review this data. ii) Focus groups with key AI stakeholders in healthcare and leadership experts outside of healthcare to source their opinions on the development of radiographers into AI leadership roles iii) A nationwide survey to explore differences in leadership opportunities across the UK. Descriptive and inferential statistics will be applied to survey responses iv) Interviews with radiographers, other healthcare professionals and industry personnel currently working in AI implementation and/or leadership to gain insight into their roles and responsibilities.

Interviews and focus groups will be thematically analysed. Data from all methods will be triangulated and synthesised to consolidate study results/outputs.

Expected Results/Outputs: This study will provide the basis for education, policy and practice to aid establishment of AI radiographer leaders in the UK. Outputs will include i) The production of guidance documents for radiographers, offering insight into career development with the aim of entering an AI leadership role; ii) Explore an advanced practice 'AI lead' role with the Society of Radiographers (SoR) iii) Create the basis of an AI leadership course within City, University of London (UoL) iv) 4 publications in peer reviewed journals from each aspect of the study (PhD by publication).