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Objectives This study aims to explore the ability to identify high-grade intracranial arterial stenosis (ICAS) by an artificial intelligence (AI) designed to detect large vessel occlusions (LVO) and the clinical relevance of these ‘false positive’ findings.Design We are presenting a retrospective cohort study.Setting The study was conducted at a supraregional stroke centre of an urban tertiary care provider.Participants Consecutive stroke cases treated between January 2023 and December 2023 of patients >18 years of both sexes and any ethnicity were eligible for inclusion. 934 patients (52.7% male) with a mean age of 71.7±13.6 years (25–101 years) were included.Interventions CT angiographies were analysed by a deep learning algorithm for LVO detection of the anterior circulation. AI results were compared with radiology reports and secondary focused evaluation.Primary and secondary outcome measures Diagnostic accuracies for ICAS detection by the AI were calculated.Results Primary reports identified 30 ICAS and nine additional ICAS were detected during secondary evaluation (incidence 4.2%). The sensitivity of radiology reports was 77% (95% CI 0.61 to 0.89), the specificity 99% (95% CI 0.98 to 1.00), negative predictive value (NPV) 99% (95% CI 0.98 to 0.99) and positive predictive value (PPV) 79% (95% CI 0.65 to 0.88). The AI identified 13 of 39 ICAS correctly. 18 false positive cases (neither LVO nor ICAS) were flagged by the AI. The sensitivity of the algorithm was 33% (95% CI 0.19 to 0.50), the specificity 98% (95% CI 0.97 to 0.99), the NPV 97% (95% CI 0.96 to 0.98) and PPV 42% (95% CI 0.28 to 0.58).Conclusion Detection of high-grade ICAS by an algorithm trained to identify LVO is per se a false positive finding but occurred in 13 of 39 cases. Dedicated training for ICAS might lead to a beneficial tool during the diagnostic work-up for ischaemic stroke.Trial registration German Register for Clinical Trials (DRKS: DRKS00034019 https://drks.de/search/de/trial/DRKS00034019).