ICH requires rapid and accurate diagnosis on non-contrast CT to prevent morbidity and mortality. Artificial intelligence (AI) algorithms offer potential to enhance detection speed and consistency. This meta-analysis evaluates the overall diagnostic accuracy and clinical performance of AI models for automated ICH detection across diverse clinical settings.

Nineteen studies encompassing 90 514 CT scans were included. AI models comprised convolutional neural networks, hybrid CNN-RNN architectures, and commercial triage systems (Aidoc, Annalise CTB, qER.ai, VeriScout™, Siemens AI). The pooled sensitivity was 0.91 (95 % CI 0.89–0.93) and specificity 0.93 (95 % CI 0.90–0.95), with an overall AUC of 0.95 (95 % CI 0.93–0.96) and DOR ≈ 130, indicating excellent discrimination. Heterogeneity was moderate (I² ≈ 60 %), largely explained by study design and validation type. Externally validated and prospective cohorts showed comparable performance to internal test sets, confirming real-world generalizability. AI integration shortened reporting or notification times by 1–3 minutes per scan in emergency workflows.

Artificial intelligence achieves radiologist-level accuracy for intracranial hemorrhage detection on non-contrast CT, supporting its safe, rapid, and generalizable integration into clinical triage workflows.