Predictive Performance of Artificial Intelligence Models for Stroke Risk Stratification: A Systematic Review and Meta-Analysis

Abstract

Background Stroke remains a leading cause of mortality, long-term disability, and healthcare expenditure worldwide, placing substantial strain on healthcare systems, particularly in low- and middle-income countries. Effective risk stratification can facilitate targeted prevention strategies, optimize resource allocation, and reduce avoidable hospitalizations. This study synthesizes existing evidence on the predictive performance of artificial intelligence (AI)-based models for stroke risk assessment through meta-analysis and explores their potential implications for healthcare system planning. Methods Studies were systematically retrieved from Web of Science (WoS), PubMed, and Scopus until 31 January 2025. The review followed the PRISMA 2020 guidelines. Area Under the Receiver Operating Characteristic Curve (AUC) values were extracted for each algorithm type and pooled using meta-analytic methods. Results Deep learning (DL) algorithms demonstrated favorable pooled discriminative performance (AUC: 0.955; 95% CI: 0.906–1.00, I2 = 85.75%), especially for imaging-based models. Sensitivity analysis modestly reduced heterogeneity (I2 from 85.75% to 61.77%). Substantial heterogeneity remained across study populations, healthcare settings, predictor characteristics, and validation strategies, limiting the generalizability of findings. Conclusions AI-based models, particularly DL approaches, demonstrate favorable predictive performance for stroke risk stratification. However, considerable methodological heterogeneity, limited external validation, and risk of bias reduce confidence in widespread clinical implementation. Future research should follow standardized reporting and validation frameworks, such as TRIPOD, to improve methodological rigor, transparency, and clinical applicability.