Beauty Is in the AI of the Beholder: Are We Ready for the Clinical Integration of Artificial Intelligence in Radiography? An Exploratory Analysis of Perceived AI Knowledge, Skills, Confidence, and Education Perspectives of UK Radiographers

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

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Introduction: The use of artificial intelligence (AI) in medical imaging and radiotherapy
has been met with both scepticism and excitement. However, clinical integration of AI
is already well-underway. Many authors have recently reported on the AI knowledge
and perceptions of radiologists/medical staff and students however there is a paucity of
information regarding radiographers. Published literature agrees that AI is likely to have
significant impact on radiology practice. As radiographers are at the forefront of radiology
service delivery, an awareness of the current level of their perceived knowledge, skills, and
confidence in AI is essential to identify any educational needs necessary for successful
adoption into practice.
Aim: The aim of this survey was to determine the perceived knowledge, skills, and
confidence in AI amongst UK radiographers and highlight priorities for educational
provisions to support a digital healthcare ecosystem.
Methods: A survey was created on Qualtrics® and promoted via social media
(Twitter®/LinkedIn®). This survey was open to all UK radiographers, including students
and retired radiographers. Participants were recruited by convenience, snowball
sampling. Demographic information was gathered as well as data on the perceived,
self-reported, knowledge, skills, and confidence in AI of respondents. Insight into what
the participants understand by the term “AI” was gained by means of a free text response.
Rainey et al. AI-Related Survey of UK Radiographers
Quantitative analysis was performed using SPSS® and qualitative thematic analysis was
performed on NVivo®.
Results: Four hundred and eleven responses were collected (80% from diagnostic
radiography and 20% from a radiotherapy background), broadly representative of the
workforce distribution in the UK. Although many respondents stated that they understood
the concept of AI in general (78.7% for diagnostic and 52.1% for therapeutic radiography
respondents, respectively) there was a notable lack of sufficient knowledge of AI
principles, understanding of AI terminology, skills, and confidence in the use of AI
technology. Many participants, 57% of diagnostic and 49% radiotherapy respondents,
do not feel adequately trained to implement AI in the clinical setting. Furthermore 52%
and 64%, respectively, said they have not developed any skill in AI whilst 62% and 55%,
respectively, stated that there is not enough AI training for radiographers. The majority
of the respondents indicate that there is an urgent need for further education (77.4% of
diagnostic and 73.9% of therapeutic radiographers feeling they have not had adequate
training in AI), with many respondents stating that they had to educate themselves to
gain some basic AI skills. Notable correlations between confidence in working with AI
and gender, age, and highest qualification were reported.
Conclusion: Knowledge of AI terminology, principles, and applications by healthcare
practitioners is necessary for adoption and integration of AI applications. The results
of this survey highlight the perceived lack of knowledge, skills, and confidence for
radiographers in applying AI solutions but also underline the need for formalised
education on AI to prepare the current and prospective workforce for the upcoming
clinical integration of AI in healthcare, to safely and efficiently navigate a digital future.
Focus should be given on different needs of learners depending on age, gender, and
highest qualification to ensure optimal integration.
Original languageEnglish
Article number739327
Pages (from-to)1-19
Number of pages19
JournalFrontiers in Digital Health
Publication statusPublished - 11 Nov 2021


  • artificial intelligence
  • AI
  • Radiography
  • Education
  • Workforce training
  • Digital Health
  • Radiotherapy
  • adoption


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