Perceptions of artificial intelligence among computed tomography technologists in Saudi Arabia: Influence of demographics and training on AI adoption

Abstract

Objective

This study evaluates the perceptions of computed tomography (CT) technologists in Saudi Arabia regarding the integration of artificial intelligence (AI) into radiology, focusing on the influence of demographic factors and prior AI training on their attitudes toward adopting AI in radiology.

Methods

A cross-sectional study was conducted using an online questionnaire distributed among CT technologists in various Saudi health-care facilities. The survey responses captured their demographic characteristics, exposure to AI training, and perceptions of the impact of AI on their workflows and career trajectories. Descriptive statistics were used to summarize categorical variables. Pearson's chi-square test was performed to evaluate associations between demographic/professional characteristics and AI perceptions. A p-value <0.05 was considered statistically significant.

Results

A total of 396 CT technologists participated in the survey, with 82.8% employed in public hospitals and 81.3% holding a bachelor's degree. While 65% agreed that using AI would enhance their CT practices, their concerns about career disruption were minimal, with 80% disagreeing with the idea that AI would negatively impact their work roles. Limited AI training was reported, with only 9.1% receiving education during their formal studies and 19.2% from workplace initiatives. Significant associations were observed between perceptions of AI and various factors (≤0.05), such as type of hospital, years of experience, and training exposure to AI.

Conclusion

CT technologists in Saudi Arabia largely view AI as a positive addition to their radiology practices, but training gaps and resource disparities remain key challenges. Targeted educational programs and policies ensuring equitable access to AI resources are critical for fostering a well-prepared radiography workforce and facilitating seamless AI integration in radiology practices.