Current utilization and impact of AI LVO detection tools in acute stroke triage: a multicenter survey analysis
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.
Authors
- Mohammad El‐Ghanem (ORCID: https://orcid.org/0000-0001-9426-0548)
- Tunmi Anwoju (ORCID: https://orcid.org/0000-0002-3413-3835)
- Yazan J. Alderazi (ORCID: https://orcid.org/0000-0001-7367-5410)
- Mohamad Ezzeldin (ORCID: https://orcid.org/0000-0001-7740-8774)
- Adam Delora (ORCID: https://orcid.org/0000-0001-5272-6557)
- Omar Kass‐Hout (ORCID: https://orcid.org/0000-0001-9738-6824)
- Ziad Darkhabani
- Zuhair Ali (ORCID: https://orcid.org/0000-0001-8740-0346)
- Thanh N. Nguyen
- Rime Ezzeldin (ORCID: https://orcid.org/0009-0009-7745-1807)
Institutions
- Boston University (US)
- Boston Medical Center (US)
- Jordan University of Science and Technology (JO)
- Harris Health System (US)
- West Health (US)
- University Medical Center (US)
- St David's Medical Center (US)
- St. David's Round Rock Medical Center (US)
- Lone Star College Kingwood (US)
- University of Houston - Clear Lake (US)
- Del Mar College (US)
- Texas A&M University – Corpus Christi (US)
Publication Details
- Journal
- Neurological Research
- Published
- 2025-06-07
- DOI
- https://doi.org/10.1080/01616412.2025.2515194
- Citations
- 2
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 2.71