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Current State of Artificial Intelligence Adoption and Implementation in Neuroradiology Departments: Insights from a U.S. National Survey
Journal article   Open access   Peer reviewed

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

Max Wintermark, Jason W Allen, Rahul Bhala, Colin Derdeyn, Ajay Gupta, Christopher P Hess, Joseph M Hoxworth, Thierry Huisman, Mahesh V Jayaraman, Ajay Malhotra, …
American journal of neuroradiology : AJNR
03/06/2026
DOI: 10.3174/ajnr.A9279
PMID: 41791836
url
https://doi.org/10.3174/ajnr.A9279View
Published (Version of record) Open Access

Abstract

BACKGROUND AND PURPOSE: Artificial intelligence (AI) is rapidly transforming medical imaging, yet its integration into neuroradiology remains uneven. This survey-based study assesses AI usage, tools, applications, barriers, and future expectations among U.S. neuroradiology departments. MATERIALS AND METHODS: This cross-sectional survey questionnaire comprised 19 items, blending multiple-choice, multi-select, and open-ended formats. Descriptive statistics were used to identify patterns in data. RESULTS: Most departments (81%) reported AI use, primarily for stroke-related applications, with smaller numbers using tools for report generation, segmentation, and image quality enhancement. Most clinical tools were FDA-approved. AI had minimal perceived impact on workload, and performance was viewed as variable, with concerns about accuracy and false positives. Cost, integration challenges, and limited efficacy evidence were the main barriers to adoption. Despite these limitations, most respondents anticipated increased AI use over the next five years. CONCLUSIONS: Findings underscore the need for clinician-vendor collaboration to realize AI’s potential in reducing workload and improving outcomes.

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