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Radiologic Technology Students’ Perceptions on Adoption of Artificial Intelligence Technology in Radiology

Authors: Arif WM;

Radiologic Technology Students’ Perceptions on Adoption of Artificial Intelligence Technology in Radiology

Abstract

Wejdan M Arif King Saud University, College of Applied Medical Sciences, Department of Radiological Sciences, Riyadh, Saudi ArabiaCorrespondence: Wejdan M Arif, Email warif@ksu.edu.saStudy Purpose: This study aims to analyze radiologic technology student’s perceptions of artificial intelligence (AI) and its applications in radiology.Methods: A quantitative cross-sectional survey was conducted. A pre-validated survey questionnaire with 17 items related to students perceptions of AI and its applications was used. The sample included radiologic technology students from three universities in Saudi Arabia. The survey was conducted online for several weeks, resulting in a sample of 280 radiologic technology students.Results: Of the participants, 63.9% were aware of AI and its applications. T-tests revealed a statistically significant difference (p = 0.0471) between genders with male participants reflecting slightly higher AI awareness than female participants. Regarding the choice of radiology as specialization, 35% of the participants stated that they would not choose radiology, whereas 65% preferred it. Approximately 56% of the participants expressed concerns about the potential replacement of radiology technologists with AI, and 62.1% strongly agreed on the necessity of incorporating known ethical principles into AI.Conclusion: The findings reflect a positive evaluation of the applications of this technology, which is attributed to its essential support role. However, tailored education and training programs are necessary to prepare future healthcare professionals for the increasing role of AI in medical sciences.Keywords: radiologic technology students, radiology technologist, artificial intelligence, AI, perceptions, training, knowledge, awareness, radiology

Keywords

perceptions, knowledge, Medicine (General), radiologic technology students, training, R5-920, radiology technologist, ai, awareness, artificial intelligence, radiology

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold