Introduction Management of acute ischemic stroke due to large‐vessel occlusion (LVO) is highly time‐sensitive. Artificial‐intelligence tools such as Rapid AI and Viz.ai are increasingly used to facilitate rapid detection and prioritization of LVO on brain CT angiography (CTA). We conducted a systematic review and meta‐analysis comparing the diagnostic performance of Rapid AI and Viz.ai for LVO detection. Methods A PRISMA‐guided search of PubMed and the Cochrane Library from inception to August 28, 2025, was performed. Analyses were conducted in R (meta, metafor) using random‐effects models. The primary outcomes were pooled sensitivity and specificity for LVO detection. Secondary outcomes included LVO detection rate (n/N), positive predictive value (PPV), negative predictive value (NPV), and false‐positive rate (FPR). Results Eighteen studies included 14,387patients, met inclusion criteria (13 Rapid AI; 5 Viz.ai). Rapid AI showed higher sensitivity (0.85; 95% CI, 0.80–0.90) and a higher detection rate (0.88; 0.75–0.95) versus Viz.ai (sensitivity 0.75; 0.66–0.83; detection rate 0.74; 0.66–0.81). Viz.ai demonstrated higher specificity (0.95; 0.92–0.98) than Rapid AI (0.87; 0.83–0.91). PPV was 0.57 (0.48–0.65) for Rapid AI and 0.65 (0.52–0.77) for Viz.ai; NPV remained high for both (Rapid AI 0.98 0.97–0.98; Viz.ai 0.97 0.96–0.99). FPR was lower for Viz.ai (0.05; 0.02–0.08) than Rapid AI (0.10; 0.05–0.19). All pooled estimates were statistically significant (p<0.001). Conclusions Rapid AI prioritized case capture (higher sensitivity/detection), whereas Viz.ai prioritized precision (higher specificity/lower FPR). Choice of tool may be tailored to institutional priorities. Centers prioritizing maximal LVO capture may favor Rapid AI, whereas those aiming to limit false alerts and downstream evaluations may prefer Viz.ai. image image
Alkhaldi et al. (Sat,) studied this question.