PT  - JOURNAL ARTICLE
AU  - Khafaji, Mawya A.
AU  - Safhi, Mohammed A.
AU  - Albadawi, Roia H.
AU  - Al-Amoudi, Salma O.
AU  - Shehata, Salah S.
AU  - Toonsi, Fadi
TI  - Artificial intelligence in radiology
AID  - 10.15537/smj.2022.43.1.20210337
DP  - 2022 Jan 01
TA  - Saudi Medical Journal
PG  - 53--60
VI  - 43
IP  - 1
4099  - http://smj.org.sa/content/43/1/53.short
4100  - http://smj.org.sa/content/43/1/53.full
SO  - Saudi Med J2022 Jan 01; 43
AB  - 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.