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

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 st...

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Autores principales:	Clare Rainey, Tracy O'Regan, Jacqueline Matthew, Emily Skelton, Nick Woznitza, Kwun-Ye Chu, Spencer Goodman, Jonathan McConnell, Ciara Hughes, Raymond Bond, Sonyia McFadden, Christina Malamateniou
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	artificial intelligence AI radiography education workforce training digital health Medicine R Public aspects of medicine RA1-1270 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/d744b14f1bea422f8b8c2b9a73c9c126
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id	oai:doaj.org-article:d744b14f1bea422f8b8c2b9a73c9c126
record_format	dspace
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence AI radiography education workforce training digital health Medicine R Public aspects of medicine RA1-1270 Electronic computers. Computer science QA75.5-76.95
spellingShingle	artificial intelligence AI radiography education workforce training digital health Medicine R Public aspects of medicine RA1-1270 Electronic computers. Computer science QA75.5-76.95 Clare Rainey Tracy O'Regan Jacqueline Matthew Emily Skelton Emily Skelton Nick Woznitza Nick Woznitza Kwun-Ye Chu Kwun-Ye Chu Spencer Goodman Jonathan McConnell Ciara Hughes Raymond Bond Sonyia McFadden Christina Malamateniou Christina Malamateniou 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
description	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. 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.
format	article
author	Clare Rainey Tracy O'Regan Jacqueline Matthew Emily Skelton Emily Skelton Nick Woznitza Nick Woznitza Kwun-Ye Chu Kwun-Ye Chu Spencer Goodman Jonathan McConnell Ciara Hughes Raymond Bond Sonyia McFadden Christina Malamateniou Christina Malamateniou
author_facet	Clare Rainey Tracy O'Regan Jacqueline Matthew Emily Skelton Emily Skelton Nick Woznitza Nick Woznitza Kwun-Ye Chu Kwun-Ye Chu Spencer Goodman Jonathan McConnell Ciara Hughes Raymond Bond Sonyia McFadden Christina Malamateniou Christina Malamateniou
author_sort	Clare Rainey
title	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
title_short	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
title_full	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
title_fullStr	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
title_full_unstemmed	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
title_sort	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
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/d744b14f1bea422f8b8c2b9a73c9c126
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spelling	oai:doaj.org-article:d744b14f1bea422f8b8c2b9a73c9c1262021-11-11T05:20:25ZBeauty 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 Radiographers2673-253X10.3389/fdgth.2021.739327https://doaj.org/article/d744b14f1bea422f8b8c2b9a73c9c1262021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fdgth.2021.739327/fullhttps://doaj.org/toc/2673-253XIntroduction: 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. 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.Clare RaineyTracy O'ReganJacqueline MatthewEmily SkeltonEmily SkeltonNick WoznitzaNick WoznitzaKwun-Ye ChuKwun-Ye ChuSpencer GoodmanJonathan McConnellCiara HughesRaymond BondSonyia McFaddenChristina MalamateniouChristina MalamateniouFrontiers Media S.A.articleartificial intelligenceAIradiographyeducationworkforce trainingdigital healthMedicineRPublic aspects of medicineRA1-1270Electronic computers. Computer scienceQA75.5-76.95ENFrontiers in Digital Health, Vol 3 (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

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