Knowledge, perceptions, and expectations of Artificial intelligence in radiography practice: A global radiography workforce survey

Akudjedu, Theophilus N., Torre, Sofia, Khine, Ricardo, Katsifarakis, Dimitris, Newman, Donna and Malamateniou, Christina (2022) Knowledge, perceptions, and expectations of Artificial intelligence in radiography practice: A global radiography workforce survey. Journal of Medical Imaging and Radiation Sciences. ISSN 19398654 (In Press)

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Access this via: https://doi.org/10.1016/j.jmir.2022.11.016

Abstract

ABSTRACT Background: Artificial Intelligence (AI) technologies have already started impacting clinical practice across various settings worldwide, including the radiography profession. This study is aimed at exploring a world-wide view on AI technologies in relation to knowledge, perceptions, and expectations of radiography professionals. Methods: An online survey (hosted on Qualtrics) on key AI concepts was open to radiography professionals worldwide (August 1st to December 31st 2020). The survey sought both quantitative and qualitative data on topical issues relating to knowledge, perceptions, and expectations in relation to AI implementation in radiography practice. Data obtained was analysed using the Statistical Package for Social Sciences (SPSS) (v.26) and the six-phase thematic analysis approach. Results: A total of 314 valid responses were obtained with a fair geographical distribution. Of the respondents, 54.1% (157/290) were from North America and were predominantly clinical practicing radiographers (60.5%, 190/314). Our findings broadly relate to different perceived benefits and misgivings/shortcomings of AI implementation in radiography practice. The benefits relate to enhanced workflows and optimised workstreams while the misgivings/shortcomings revolve around de-skilling and impact on patient-centred care due to over-reliance on advanced technology following AI implementation. Discussion: Artificial intelligence is a tool but to operate optimally it requires human input and validation. Radiographers working at the interface between technology and the patient are key stakeholders in AI implementation. Lack of training and of transparency of AI tools create a mixed response of radiographers when they discuss their perceived benefits and challenges. It is also possible that their responses are nuanced by different regional and geographical contexts when it comes to AI deployment. Irrespective of geography, there is still a lot to be done about formalised AI training for radiographers worldwide. This is a vital step to ensure safe and effective AI implementation, adoption, and faster integration into clinical practice by healthcare workers including radiographers. Conclusion: Advancement of AI technologies and implementation should be accompanied by proportional training of end-users in radiography and beyond. There are many benefits of AI-enabled radiography workflows and improvement on efficiencies but equally there will be widespread disruption of traditional roles and patient-centred care, which can be managed by a well-educated and well-informed workforce.

Item Type:	Article
Keywords:	Artificial intelligence, Radiography, Global Surveys, Workforce Development, Education
Depositing User:	RED Unit Admin
Date Deposited:	21 Dec 2022 09:58
Last Modified:	21 Dec 2022 09:58
URI:	https://bnu.repository.guildhe.ac.uk/id/eprint/18635

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