‘UK radiographers’ and radiotherapists’ perceptions and expectations of AI in radiology – current status and future developments’

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

Research output: Contribution to conferencePosterpeer-review


Purpose or learning objective
This survey aimed to assess perceptions and expectations of AI within UK radiographers.
(13 wds)
Methods or background
Evaluating perceptions of AI and expectations for future development is imperative to ensure a seamless implementation of AI in Radiography.
The survey used convenience sampling and was promoted via social media (Twitter® / LinkedIn®) and disseminated via authors’ professional networks. Demographic information, perceptions of AI and expectations for its future development was gathered. Analysis performed using SPSS v23.
(57 wds)

Results or findings
411 responses were collected (80% diagnostic radiographers (DR); 20% therapeutic radiographers (TR)).
There is a lack of awareness of AI currently used in clinical practice. CT, reporting, MRI and mammography were identified by DR as areas where AI has greatest potential for development. TR responses identified treatment planning, contouring, and image acquisition/matching respectively. Most respondents felt that AI would impact their daily work (DR, 79.6%; TR, 88.9%) and agreed that AI will help standardising patient care and technical aspects of radiography practice. Statements regarding the impact of AI on career opportunities and the appeal of radiography as a career option received mixed responses.
(103 wds)

There is still uncertainty on the use of AI in current practice and its impact on radiography as a career. Most agree that AI will impact clinical practice. Areas with scope for future development were identified, providing opportunities for input from clinical end-users.
(44 wds)

This exploratory study surveyed UK radiographers. Further studies should be considered internationally, including other medical professions. Focus groups would allow in-depth understanding of issues raised. Correlation and subgroup analyses may be valuable in further studies where true randomised sampling has taken place.
(42 wds)
Ethics committee approval
City, University of London, School of Health Sciences Research Ethics Committee (ETH1920-1989)
(12 wds)
Funding for this study
No external funding nor conflicts of interest.
(7 wds)
Original languageEnglish
Publication statusAccepted/In press - 2 Mar 2022
EventEuropean Congress of Radiology 2022 - Vienna; online, Vienna, Austria
Duration: 2 Mar 202217 Jul 2022


ConferenceEuropean Congress of Radiology 2022
Abbreviated titleECR 2022


  • AI
  • artifical intelligence
  • education
  • Radiology
  • radiographer
  • radiography


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