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

Journal article

Rainey, Clare, O'Regan, Tracy, Matthew, Jacqueline, Skelton, Emily, Woznitza, Nick, Chu, Kwun-Ye, Goodman, Spencer, McConnell, Jonathan, Hughes, Ciara, Bond, Raymond, McFadden, Sonyia and Malamateniou, Christina 2021. 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. Frontiers in Digital Health. 3, p. 739327. https://doi.org/10.3389/fdgth.2021.739327
AuthorsRainey, Clare, O'Regan, Tracy, Matthew, Jacqueline, Skelton, Emily, Woznitza, Nick, Chu, Kwun-Ye, Goodman, Spencer, McConnell, Jonathan, Hughes, Ciara, Bond, Raymond, McFadden, Sonyia and Malamateniou, Christina
Abstract 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. 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. 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. Quantitative analysis was performed using SPSS® and qualitative thematic analysis was performed on NVivo®. 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. 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. [Abstract copyright: Copyright © 2021 Rainey, O'Regan, Matthew, Skelton, Woznitza, Chu, Goodman, McConnell, Hughes, Bond, McFadden and Malamateniou.]
KeywordsRadiography; Education; AI; Workforce training; Radiotherapy; Artificial intelligence; Digital health
Year2021
JournalFrontiers in Digital Health
Journal citation3, p. 739327
PublisherFrontiers
ISSN2673-253X
Digital Object Identifier (DOI)https://doi.org/10.3389/fdgth.2021.739327
Official URLhttps://www.frontiersin.org/articles/10.3389/fdgth.2021.739327/full
Publication dates
Online11 Nov 2021
Publication process dates
Accepted19 Oct 2021
Deposited20 Dec 2021
Publisher's version
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Output statusPublished
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Woznitza, N., Hayes, N., Malshava, N. and McGuinness, D. 2014. Optimizing neonatal X-ray quality: results of an audit.
Agreement in neonatal x-ray interpretation: a comparison between consultant paediatric radiologists and a reporting radiographer
Woznitza, N., Piper, K., Aladangady, N., Iliadis, K., Prakash, R. and Santos, R. 2014. Agreement in neonatal x-ray interpretation: a comparison between consultant paediatric radiologists and a reporting radiographer.
Advanced radiographic practice in adult chest imaging
Woznitza, N. 2014. Advanced radiographic practice in adult chest imaging.
Can reporting radiographers produce clinically relevant reports?
Woznitza, N. and Piper, K. 2011. Can reporting radiographers produce clinically relevant reports?