Artificial intelligence for the detection of large vessel occlusion stroke and workflow optimization: a systematic review and meta-analysis
In brief
AI stroke workflows were linked to 55% more 90-day independence
In a meta-analysis of 13 observational studies, AI-supported workflows were associated with 40 minutes less from CT angiography to arterial puncture and 23 minutes less from hospital arrival to puncture. Patients were also more likely to be functionally independent at 90 days, but treatment access, safety and mortality showed no significant differences; whether AI itself drove the gains remains uncertain.
- Journal
- Journal of neurosurgical sciences (Q2)
- Published
- 1 October 2026
- Study design
- Systematic review of cohort studies
- Evidence level
- Level 2, Moderate (CEBM 2a)
- Authors
- João V Fernandes, Maria A Pereira, Ocílio R Gonçalves, Kenzo O Donato, Mariana L Maximiano, Arlindo B Silva Jr, et al.
- PMID
- 42974828
- DOI
- 10.23736/S0390-5616.26.06694-4
Why clinicians should know about it
- Picked for Health Informatics (top studies of the week, 8 November 2026): Deployed AI effects on stroke workflow, safety, and outcomes
Abstract
BACKGROUND: To evaluate the impact of artificial intelligence (AI) tools on workflow efficiency, treatment times, and clinical outcomes in patients with large vessel occlusion (LVO) stroke. METHODS: We included observational studies that assessed the implementation of AI-based tools in the management of patients with suspected or confirmed LVO stroke. Eligible studies reported workflow, treatment, safety, or functional outcomes. Data were extracted on thrombolysis rates, CTA-to-arterial puncture time, door-to-puncture time, endovascular treatment (EVT) access, reperfusion success, symptomatic intracerebral hemorrhage (sICH), mortality, and functional independence (mRS 0-2) at 90 days. Random-effects models were applied to calculate pooled mean differences (MD) or risk ratios (RR) with 95% confidence intervals (CIs). RESULTS: Thirteen studies were included. AI implementation significantly reduced workflow delays: CTA-to-puncture time decreased by -40.20 minutes (95% CI -66.41 to -13.99; P<0.01) and door-to-puncture time by -23.17 minutes (95% CI -38.71 to -7.63; P<0.01). Thrombolysis administration showed no difference (RR 0.79; 95% CI 0.57-1.09; P=0.15). Similarly, no significant effects were observed for EVT access (RR 0.97; 95% CI 0.66-1.44; P=0.89), reperfusion success (RR 1.06; 95% CI 1.00-1.12; P=0.06), sICH (RR 0.63; 95% CI 0.30-1.35; P=0.23), or mortality (RR 0.72; 95% CI 0.38-1.37; P=0.32). However, AI-based workflows significantly improved functional independence at 90 days (RR 1.55; 95% CI 1.12-2.15; P=0.008). CONCLUSIONS: AI tools significantly improve treatment times and functional independence in acute LVO stroke without increasing adverse events.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.