ArticleFrontiers in stroke2024
VALIDATE-Utilization of the Viz.ai mobile stroke care coordination platform to limit delays in LVO stroke diagnosis and endovascular treatment.
Article in Frontiers in stroke, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated Emergent Large Vessel Occlusion Detection Using Viz.ai Software and Its Impact on Stroke Workflow Metrics and Patient Outcomes in Stroke Centers: A Systematic Review and Meta-analysis.Translational stroke research · 2025Pooled it
- Transforming Stroke Diagnosis with Artificial Intelligence: A Scoping Review of Brainomix e-Stroke, Aidoc, RapidAI, and Viz.ai.Medicina (Kaunas, Lithuania) · 2026Article
- Current State of the Clinical Applications of Artificial Intelligence in Stroke: A Literature Review.Brain sciences · 2026Review
- Localized AI for stroke care in LMICs: a framework to overcome structural and diagnostic barriers.Frontiers in public health · 2026Review
- Artificial intelligence in neurovascular surgery: advancing diagnosis, treatment, and outcomes.Frontiers in surgery · 2026Review
- Artificial Intelligence in Stroke Care: A Narrative Review of Diagnostic, Predictive, and Workflow Applications.Cureus · 2025Review
- Predictive Models to Identify Individuals With HIV at Risk of Unsuppressed Viral Load Using Routine Public Health Data.Journal of acquired immune deficiency syndromes (1999) · 2025Article
- Core-Penumbra Hyperacute Ischemic Stroke Dataset.Scientific data · 2025Article
- The Role of Artificial Intelligence in Acute Ischemic Stroke Care: A Narrative Review.Missouri medicineReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Thousands of hospitals worldwide have adopted mobile artificial intelligence (AI)-based stroke care coordination platforms. Studies exploring the benefit of these platforms have been scrutinized due to small sample size, serial cohort design, and measurement of metrics with multiple determinants. In this large multi-center study, we evaluated the ability of an AI-based stroke care coordination platform to expedite contact with the interventionalist (NIR) for potential thrombectomy. Methods: Acute stroke consultations seen by TeleSpecialists, LLC physicians at 166 facilities (17 states) utilizing Viz.ai software (AI) vs. no AI software (non-AI) were extracted from the TeleCare by TeleSpecialists™ database from December 1, 2021, through March 31, 2022. The primary outcome was time from patient arrival to first contact with the interventionalist to discuss need for potential thrombectomy (Arrival-to-NIR notification). Results: A total of 14,116 cases were analyzed. Compared to the non-AI cohort, Arrival-to-NIR notification in the AI cohort was: (1) 39.5 min faster (44.13% reduction, Conclusions: While this study was limited by our inability to capture detailed neuroimaging timelines and patient outcomes, it suggests a potential significant benefit of AI-based stroke care coordination platforms and underscores the critical need to development robust "big data" systems to study the effects of AI, and other emerging technologies, on stroke systems of care.
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Registered trials
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