Current State of Artificial Intelligence Adoption and Implementation in Neuroradiology Departments: Insights from a...

Artificial intelligence (AI) is increasingly integrated into medical imaging, but its adoption across neuroradiology departments remains uneven and poorly characterized. This State of Practice article reports the results of a national survey assessing how US neuroradiology departments are currently using AI - including which tools and applications are most common, how AI is perceived to affect workload and diagnostic performance, the nature of collaboration with AI vendors, and the primary barriers limiting broader adoption.

The survey was developed and distributed by the American Society of Neuroradiology (ASNR) Department Chair Working Group, a group of 18 US neuroradiologists serving as department chairs. A 19-item cross-sectional questionnaire - combining multiple-choice, multi-select, and open-ended items covering department demographics, AI usage and tools, clinical applications, perceived impact, vendor collaboration, barriers, pricing models, and future expectations - was distributed by e-mail between July 14 and August 11, 2025. Sixteen of 18 working group members (89%) completed the survey; responses were analyzed descriptively.

AI use is already widespread among academic US neuroradiology departments (81%), concentrated heavily on stroke-related applications, yet most department chairs report that it has had minimal impact on workload so far and that tool performance remains inconsistent. Cost, integration challenges, and a lack of robust efficacy evidence remain the dominant barriers - underscoring that realizing AI's potential in neuroradiology will require closer collaboration between clinicians and vendors, rather than further tool proliferation alone.