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Published on: January 15, 2017
Impact of Artificial Intelligence-Based Triage on Stroke Workflow Metrics: A Systematic Review and Meta-Analysis
Juan Carlos Barrera Gutierrez1, Elaina Vivian2, Jimmy Shah2
1Methodist Digestive Institute, Methodist Dallas Medical Center, Dallas, TX, USA.
Cardiovascular and Interventional Radiology
|July 7, 2026
Summary
Artificial intelligence (AI) significantly reduces ischemic stroke workflow times, improving patient care. Further randomized trials are needed to confirm AI
Area of Science:
- Neurology
- Medical Informatics
- Artificial Intelligence
Background:
- Artificial intelligence (AI) is a growing tool in managing ischemic stroke.
- Evaluating AI's impact on stroke workflow is crucial for optimizing patient outcomes.
Purpose of the Study:
- To systematically review and meta-analyze the effect of AI implementation on stroke workflow metrics.
- To assess AI's role in improving efficiency in ischemic stroke management.
Main Methods:
- A systematic review and meta-analysis of studies published between 2015-2025.
- Searched databases: PubMed, EMBASE, OpenEvidence, Cochrane Central Register.
- Included studies evaluated automated large vessel occlusion detection using AI.
- Calculated pooled mean differences for key workflow times: door-to-groin puncture, door-to-first pass, door-to-revascularization, door-to-needle, and door-in-door-out.
Main Results:
- Twelve studies (1 RCT, 11 observational) met inclusion criteria.
- AI implementation was associated with significant reductions in workflow times.
- Specific reductions observed: door-to-groin puncture (-17.12 min), door-to-first pass (-26.55 min), door-to-revascularization (-14.55 min), door-to-needle (-4.44 min), door-in-door-out (-36.8 min).
Conclusions:
- AI-based platforms show potential for optimizing stroke workflow.
- AI contributes meaningfully to reducing critical time intervals in stroke care.
- Further randomized controlled trials are necessary to definitively confirm AI's effectiveness in stroke management.