Current utilization and impact of AI LVO detection tools in acute stroke triage: a multicenter survey analysis.

Background: Artificial intelligence (AI) tools for large vessel occlusion (LVO) detection are increasingly used in acute stroke triage to expedite diagnosis and intervention. However, variability in access and workflow integration limits their potential impact. This study assessed current usage patterns, access disparities, and integration levels across U.S. stroke programs.

Methods: Cross-sectional, web-based survey of 97 multidisciplinary stroke care providers from diverse institutions. Descriptive statistics summarized demographics, AI tool usage, access, and integration. Two-proportion Z-tests assessed differences across institutional types.

Results: Most respondents (97.9%) reported AI tool use, primarily Viz AI and Rapid AI, but only 62.1% consistently used them for triage prior to radiologist interpretation. Just 37.5% reported formal protocol integration, and 43.6% had designated personnel for AI alert response. Access varied significantly across departments, and in only 61.7% of programs did all relevant team members have access. Formal implementation of the AI detection tools did not differ based on the certification (z = -0.2;  = 0.4) or whether the program was academic or community-based (z =-0.3;  = 0.3).

Conclusions: AI-enabled LVO detection tools have the potential to improve stroke care and patient outcomes by expediting workflows and reducing treatment delays. This survey effectively evaluated current utilization of these tools and revealed widespread adoption alongside significant variability in access, integration, and workflow standardization. Larger, more diverse samples are needed to validate these findings across different hospital types, and further prospective research is essential to determine how formal integration of AI tools can enhance stroke care delivery, reduce disparities, and improve clinical outcomes.