SynthesisCardiovascular and interventional radiology2026
Impact of Artificial Intelligence-Based Triage on Stroke Workflow Metrics: A Systematic Review and Meta-Analysis.
Synthesis in Cardiovascular and interventional radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Artificial Intelligence from Flow Metrics Improvement Toward Clinical Value.Cardiovascular and interventional radiology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence is an increasingly valuable tool in ischemic stroke management. This systematic review and meta-analysis evaluated the effect of artificial intelligence implementation on stroke workflow metrics.
methodsPubMed, EMBASE, OpenEvidence, and the Cochrane Central Register of Controlled Trials were searched for studies (2015-2025) evaluating automated large vessel occlusion detection. Pooled mean differences with 95% CIs were calculated for door-to-groin puncture, door-to-first pass, door-to-revascularization, door-to-needle, and door-in-door-out times.
resultsTwelve studies met the inclusion criteria: one clinical trial and eleven observational studies. Artificial intelligence was associated with reductions in multiple workflow intervals, including door-to-groin puncture (-17.12 minutes), door-to-first pass (-26.55 minutes), door-to-revascularizationon (-14.55 minutes), door-to-needle (-4.44 minutes), and door-in-door-out time (-36.8 minutes).
conclusionOverall, AI-based platforms appear to contribute meaningfully to stroke workflow optimization, although randomized controlled trials are still needed to confirm their effectiveness. LEVEL OF EVIDENCE: Level 2c, Systematic review of randomized clinical trials, and observational studies.
Indexed as
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.