Attitudes and Readiness of Imaging Professionals Towards Artificial Intelligence: A Baseline Pre-Implementation Survey
2Imaging, Assuta Medical Center, Israel
3Social&Health Sciences, Bar-Ilan University, Israel
Background and Objective:
Artificial intelligence (AI) is entering medical imaging workflows, but successful implementation depends on staff readiness, trust, and perceived value. This study establishes a pre-implementation baseline assessing the knowledge, perceptions, and concerns of imaging professionals, with a planned post-implementation follow-up to evaluate shifts in attitudes over time.
Methods:
A baseline cross-sectional survey was conducted among 40 imaging professionals (97.5% female; mean age 40.6±12.3 years) in a large Israeli healthcare center. Respondents were primarily from mammography (75.0%) and ultrasound (22.5%), with 57.5% possessing >10 years of clinical experience. The questionnaire evaluated AI knowledge, experience, readiness, and concerns using 5-point Likert scales. Non-parametric tests (Mann-Whitney U, Spearman’s correlation) and Fisher’s exact tests were utilized. A repeated-measures follow-up is scheduled post-deployment.
Results:
Self-reported AI knowledge was moderate-to-low (mean 2.8/5), and only 30.0% reported prior practical experience with AI devices. Nevertheless, 97.5% expressed willingness to adopt AI, conditional on adequate training. Higher AI knowledge strongly correlated with positive perceptions of quality improvement ( ), and prior practical experience was associated with greater confidence in quality enhancement ( ). Ultrasound staff were significantly more optimistic than mammography staff ( ). Notably, senior professionals (>10 years of experience) were significantly less likely to expect workload reduction compared to less senior staff (17.4% vs. 58.8%; OR=0.16; ). Possible system errors were the primary concern (67.5%), and 100% required physician/radiologist oversight.
Conclusion:
Imaging professionals demonstrate high baseline readiness for AI as a supportive "co-pilot," though senior staff remain realistic/skeptical regarding workload reduction. Structured training, supervised implementation, and clear clinical governance are essential. Future follow-up will assess the actual impact of clinical deployment on these baseline perceptions.