Saudi Radiology Personnel's Perceptions of Artificial Intelligence Implementation: A Cross-Sectional Study

Abdulaziz A Qurashi; Rashed K Alanazi; Yasser M Alhazmi; Ahmed S Almohammadi; Walaa M Alsharif; Khalid M Alshamrani

doi:10.2147/JMDH.S340786

Saudi Radiology Personnel's Perceptions of Artificial Intelligence Implementation: A Cross-Sectional Study

J Multidiscip Healthc. 2021 Nov 23:14:3225-3231. doi: 10.2147/JMDH.S340786. eCollection 2021.

Authors

Abdulaziz A Qurashi¹, Rashed K Alanazi¹, Yasser M Alhazmi¹, Ahmed S Almohammadi¹, Walaa M Alsharif¹, Khalid M Alshamrani^{2

3}

Affiliations

¹ Diagnostic Radiology Technology Department, College of Applied Medical Sciences, Taibah University, Madinah, Saudi Arabia.
² College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Jeddah, Saudi Arabia.
³ King Abdullah International Medical Research Center, Jeddah, Saudi Arabia.

Abstract

Purpose: Artificial intelligence (AI) in radiology has been a subject of heated debate. The external perception is that algorithms and machines cannot offer better diagnosis than radiologists. Reluctance to implement AI maybe due to the opacity in how AI applications work and the challenging and lengthy validation process. In this study, Saudi radiology personnel's familiarity with AI applications and its usefulness in clinical practice were investigated.

Methods: A cross-sectional study was conducted in Saudi Arabia among radiology personnel from March to April 2021. Radiology personnel nationwide were surveyed electronically using Google form. The questionnaire included 12-questions related to AI usefulness in clinical practice and participants' knowledge about AI and their acceptance level to learn and implement this technology into clinical practice. Participants' trust level was also measured; Kruskal-Wallis test was used to examine differences between groups.

Results: A total of 224 respondents from various radiology-related occupations participated in the survey. The lowest trust level in AI applications was shown by radiologists (p = 0.033). Eighty-two percent of participants (n = 184) had never used AI in their departments. Most respondents (n = 160, 71.4%) reported lack of formal education regarding AI-based applications. Most participants (n = 214, 95.5%) showed strong interest in AI education and are willing to incorporate it into the clinical practice of radiology. Almost half of radiography students (22/46, 47.8%) believe that their job might be at risk due to AI application (p = 0.038).

Conclusion: Radiology personnel's knowledge of AI has a significant impact on their willingness to learn, use and adapt this technology in clinical practice. Participants demonstrated a positive attitude towards AI, showed a reasonable understanding and are highly motivated to learn and incorporate it into clinical practice. Some participants felt that their jobs were threatened by AI adaptation, but this belief might change with good training and education programmes.

Keywords: AI-based applications; artificial intelligence; imaging modalities; radiologists; radiology.