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An insight into the current perceptions of UK radiographers on the future impact of AI on the profession: A cross-sectional survey

Research output: Contribution to journalArticlepeer-review

Clare Rainey, Tracy O'Regan, Jacqueline Matthew, Emily Skelton, Nick Woznitza, Kwun Ye Chu, Spencer Goodman, Jonathan McConnell, Ciara Hughes, Raymond Bond, Christina Malamateniou, Sonyia McFadden

Original languageEnglish
Pages (from-to)347-361
Number of pages15
JournalJournal of Medical Imaging and Radiation Sciences
Issue number3
Early online date26 Aug 2022
E-pub ahead of print26 Aug 2022
PublishedSep 2022

Bibliographical note

Funding Information: The authors wish to acknowledge the UK Society of Radiographers’ AI Working Party and subsequently formed SoR AI Advisory Group for their support of this work. Funding: This study was funded by the College of Radiographers Industry Partnership Scheme (CoRIPS) and forms part of a PhD study (P.I. C Rainey). Competing Interest: Authors report no competing interests. Dr Woznitza declares consultancy fees for InHealth Reporting and NIHR AAC funding unrelated to the current submission. Ethical approval: Ethical permission was granted from City, University of London Research Ethics Committee (ETH1920-1989). Funding Information: This study was funded by the College of Radiographers Industry Partnership Scheme (CoRIPS) and forms part of a PhD study (P.I. C Rainey). Publisher Copyright: © 2022

King's Authors


Introduction: As a profession, radiographers have always been keen on adapting and integrating new technologies. The increasing integration of artificial intelligence (AI) into clinical practice in the last five years has been met with scepticism by some, who predict the demise of the profession, whilst others suggest a bright future with AI, full of opportunities and synergies. Post COVID-19 pandemic need for economic recovery and a backlog of medical imaging and reporting may accelerate the adoption of AI. It is therefore timely to appreciate practitioners’ perceptions of AI used in clinical practice and their perception of the short-term impact on the profession. Aim: This study aims to explore the perceptions of AI in the UK radiography workforce and to investigate its current AI applications and future technological expectations of radiographers. Methods: An online survey (Qualtrics) was created by a team of radiography AI experts. The survey was disseminated via social media and professional networks in the UK. Demographic information and perceptions of the impact of AI on several aspects of the radiography profession were gathered, including the current use of AI in practice, future expectations and the perceived impact of AI on the profession. Results: 411 responses were collected (80% diagnostic radiographers (DR); 20% therapeutic radiographers (TR)). Awareness of AI used in clinical practice is low, with DR respondents suggesting AI will have the most value/potential in cross sectional imaging and image reporting. TR responses linked AI as having most value in treatment planning, contouring, and image acquisition/matching. Respondents felt that AI will impact radiographers’ daily work (DR, 79.6%; TR, 88.9%) by standardising some aspects of patient care and technical factors of radiography practice. A mixed response about impact on careers was reported. Conclusions: Respondents were unsure about the ways in which AI is currently used in practice and how AI will impact on careers in the future. It was felt that AI integration will lead to increased job opportunities to contribute to decision making as an end user. Job security was not identified as a cause for concern.

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