High Adoption, Higher Expectations: A Cross-Sectional Survey of Radiologist Engagement with Artificial Intelligence in the United Arab Emirates.
Authors
Affiliations (4)
Affiliations (4)
- College of Medicine, Gulf Medical University, Ajman, United Arab Emirates. [email protected].
- College of Medicine, Gulf Medical University, Ajman, United Arab Emirates.
- Department of Radiology, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates.
- Imaging Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates.
Abstract
Artificial intelligence (AI) is poised to transform diagnostic radiology, yet data on its adoption and the perspectives of radiologists in the Middle East remain scarce. This study provides the first comprehensive analysis of AI engagement among radiologists in the United Arab Emirates (UAE), a nation characterized by substantial investment in digital health infrastructure and artificial intelligence initiatives. We conducted a cross-sectional survey of 100 practicing radiologists in the UAE. The survey assessed professional role, practice setting, institution characteristics, attitudes toward AI, adoption patterns, perceived clinical impact, and preferences for future AI applications. The study revealed a remarkably high rate of AI adoption. Sixty-seven percent of respondents reported daily AI use. Attitudes were overwhelmingly positive, with 73% of radiologists holding a favorable or very favorable view of AI. We identified a significant gap between desired and currently available AI applications, particularly in abdominal imaging (24% gap), pediatric imaging (18% gap), and emergency/trauma imaging (16% gap) (p = 0.031). Despite high adoption, there was no statistically significant difference in attitudes or daily use across professional roles or practice settings (p > 0.05). A strong preference for a cautious, evidence-based approach was evident, with 71% of respondents favoring gradual AI implementation. Radiologists in the UAE reported high levels of AI adoption and generally favorable attitudes toward AI-assisted radiology practice, with high expectations for its future development. The findings highlight a critical need for targeted investment in underdeveloped AI subspecialty tools to meet clinical demand. These results provide a valuable benchmark for the region and underscore the importance of aligning AI development with the practical needs of clinical radiologists to ensure successful and impactful integration.