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Cited by 48Artificial Intelligence in Healthcare and EducationRadiology practices and educationRadiomics and Machine Learning in Medical Imaging

Artificial intelligence in radiology: Are Saudi residents ready, prepared, and knowledgeable?

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Khafaji, Mawya A., Safhi, Mohammed A., Albadawi, Roia H., Al-Amoudi, Salma O., Shehata, Salah S., Toonsi, Fadi·2022·Saudi Medical Journal
DOI 10.15537/smj.2022.43.1.20210337PMC PMC9280560
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Abstract

Objectives: To assess the knowledge and perception of artificial intelligence (AI) among radiology residents across Saudi Arabia and assess their interest in learning about AI. Methods: An observational cross-sectional study carried out among radiology residents enrolled in the Saudi Board of Radiology, Saudi Arabia. An anonymized, self-administered questionnaire was distributed in April 2020 and responses were collected until July 2020. Results: A total of 154 residents filled the questionnaire. The top 3 aspects of AI participants wanted to learn were: clinical use of AI applications, advantages and limitations of AI applications, and technical methods. Approximately 43.5% of participants did not expect AI to affect job positions, while 42% anticipated that job positions will decrease. Approximately 53% expected a reduction in reporting workload, while 28% expected an increase in workload. Conclusion: Currently, the exposure of radiologists to the use of AI is inadequate. It is imperative that AI is introduced to radiology trainees and that radiologists stay updated with advances in AI to be more knowledgeable on how to benefit from it.

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  • AI contributes to better clinical outcomes and ensures consistency of care by mitigating human factors like exhaustion.

    “Furthermore, AI contributes to better clinical outcomes and ensures consistency of care by mitigating human factors like exhaustion [46, 61, 66].”

    From Healthcare professionals’ perspectives on artificial intelligence in patient care: a systematic review of hindering and facilitating factors on different levels (2025) · cites this paper as [66]

    The Discussion says AI can detect early findings more accurately, enabling earlier diagnosis that can lead to better patient outcomes. The Results table lists freedom from emotional exhaustion and physical limitations as a perceived advantage of AI. Neither passage establishes that AI ensures consistency of care by mitigating exhaustion; the paper reports potential benefits and survey responses, not that assured outcome. (LLM, conf=0.84)

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References 30 · Cited by 48

Research works printed but not linked: 27

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Authors
Khafaji, M. A., Safhi, M. A., Albadawi, R. H., Al-Amoudi, S. O., Shehata, S. S., & Toonsi, F.
Affiliations
Riyadh Armed Forces Hospital (SA)
Corresponding author
Khafaji, Mawya A.
ORCID
Toonsi, Fadi
Venue
Saudi Medical Journal
Publisher
Armed Forces Medical Services
Published
2022-01
Volume
43
Issue
1
Pages
53-60
ISSN
  • 0379-5284 (ISSN-L)
  • 1658-3175
DOI
10.15537/smj.2022.43.1.20210337
PMC
PMC9280560
OpenAlex
W4288063574
Cited by
48
References
30
Citations by year
2026: 2 · 2025: 13 · 2024: 11 · 2023: 9 · 2022: 8 · 2021: 4 · 2019: 1
FWCI
3.09
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
Saudi Medical Journal (publishedVersion, cc-by-nc)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
artificial intelligence, radiology residents, radiology education, Saudi Arabia

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