Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

The Use of Artificial Intelligence and Deep Learning in Medical Imaging: A Nationwide Survey of Trainees in Saudi Arabia

Ahmad A. Mirza
* ORCID logo ,
Omar M. Wazgar
,
Ammar A. Almaghrabi
,
Roaa M. Ghandour
,
Sarah A. Alenizi
,
Khalid S. Alraddadi
,
Abdulrahim A. Mirza
,
Fayzah H. Al-Adwani
,
Mohammed A. Alsakkaf
,
Yahea A. Alzahrani
Version 1 : Received: 24 February 2022 / Approved: 25 February 2022 / Online: 25 February 2022 (13:52:22 CET)
Version 2 : Received: 21 May 2022 / Approved: 23 May 2022 / Online: 23 May 2022 (11:13:31 CEST)

How to cite: Mirza, A.A.; Wazgar, O.M.; Almaghrabi, A.A.; Ghandour, R.M.; Alenizi, S.A.; Alraddadi, K.S.; Mirza, A.A.; Al-Adwani, F.H.; Alsakkaf, M.A.; Alzahrani, Y.A. The Use of Artificial Intelligence and Deep Learning in Medical Imaging: A Nationwide Survey of Trainees in Saudi Arabia. Preprints 2022, 2022020338. https://doi.org/10.20944/preprints202202.0338.v1 Mirza, A.A.; Wazgar, O.M.; Almaghrabi, A.A.; Ghandour, R.M.; Alenizi, S.A.; Alraddadi, K.S.; Mirza, A.A.; Al-Adwani, F.H.; Alsakkaf, M.A.; Alzahrani, Y.A. The Use of Artificial Intelligence and Deep Learning in Medical Imaging: A Nationwide Survey of Trainees in Saudi Arabia. Preprints 2022, 2022020338. https://doi.org/10.20944/preprints202202.0338.v1

Abstract

Artificial intelligence is dramatically transforming medical imaging. We assessed the levels of artificial intelligence use among radiology trainees and explored their perceived impact of artifi-cial intelligence on the radiology workflow and radiology profession, in correlation with the perceived ease of use and behavioral intention to use artificial intelligence. This cross-sectional study enrolled radiology trainees from Saudi Arabia, and an online 5-part-structured question-naire was disseminated via online networks to trainee in July 2021. We included 98 participants (51 male; age 27.59±2.02 years). Level of use was low; few used it in routine practice (7%). The impact of artificial intelligence on the radiology workflow was positively perceived in all radi-ology workflow steps (3.64–3.97 out of 5). A positive impact on the radiology profession was more frequently perceived for technical and performance aspects (81%–85%) compared with prestige and legal aspects (64%–71%). Perceived ease of use and behavioral intention to use arti-ficial intelligence was associated with the current professional activity, level of use artificial in-telligence use, and perceived impact on the profession as well as on radiology workflow (p<0.05). Levels of artificial intelligence use in radiology are very low. The perceived positive impact of ar-tificial intelligence on radiology workflow and profession is correlated with an increase in be-havioral intention to use artificial intelligence. Thus, increasing awareness about the favorable impact can improve the behavioral use.

Keywords

Artificial intelligence; Diagnostic imaging; Education; Radiology; Saudi Arabia

Subject

Medicine and Pharmacology, Other

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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