The implementation of artificial intelligence (AI), particularly Viz.ai software in stroke care, has emerged as a promising tool to enhance the detection of large vessel occlusion (LVO) and to improve stroke workflow metrics and patient outcomes. The aim of this systematic review and meta‐analysis is to evaluate the impact of Viz.ai on stroke workflow efficiency in hospitals and on patients' outcomes. Following the PRISMA guidelines, we conducted a comprehensive search on electronic databases, including PubMed, Web of Science, and Scopus databases, to obtain relevant studies until 25 October 2024. Our primary outcomes were door‐to‐groin puncture (DTG) time, CT scan‐to‐start of endovascular treatment (EVT) time, CT scan‐to‐recanalization time, and door‐in‐door‐out time. Secondary outcomes included symptomatic intracranial hemorrhage (ICH), any ICH, mortality, mRS score 0.05). Our results suggest that the integration of the Viz.ai platform in stroke care holds significant potential for reducing EVT delays in patients with LVO and optimizing stroke flow metrics in comprehensive stroke centers. Further studies are required to validate its efficacy in improving clinical outcomes in patients with LVO
Sarhan et al. (Sat,) studied this question.