Artificial Intelligence for Cardiovascular Risk Prediction: An Umbrella Review of Applications and Translational...

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide. Conventional risk prediction models often demonstrate suboptimal calibration and limited generalizability across populations. Artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL), enable integration of multimodal clinical and imaging data for individualized cardiovascular risk estimation.

To evaluate the applications, predictive performance, and translational limitations of AI models for cardiovascular risk prediction within an umbrella review framework.

PubMed, Scopus, and Web of Science were systematically searched for studies published between January 2015 and October 2025 investigating AI-based prediction of cardiovascular outcomes. Eligible designs included randomized controlled trials (RCTs), cohort studies, systematic reviews, and meta-analyses. Predictive performance was the primary outcome, mainly assessed using the area under the receiver operating characteristic curve (AUC). Methodological quality was evaluated using established risk-of-bias tools. From 3500 identified records, 48 studies (8 RCTs, 28 cohort studies, and 12 systematic reviews or meta-analyses) were included in the final analysis.

AI models achieved AUC values greater than 0.90 in more than 70% of imaging-based studies. Evidence synthesis showed predominant reliance on internal validation, inconsistent calibration reporting, and limited evaluation of algorithmic fairness. Multimodal data integration improved detection of coronary artery disease (CAD) and heart failure (HF). Wearable monitoring was associated with 18-25% lower hospitalization rates compared with usual care.

AI improves predictive accuracy in cardiovascular risk assessment. Despite strong discrimination performance (AUC), methodological heterogeneity, insufficient calibration assessment, algorithmic bias, limited external validation, and regulatory uncertainty remain major barriers to implementation. Clinical translation requires multicenter RCTs, explainable AI frameworks, and standardized reporting guidelines such as Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis Artificial Intelligence (TRIPOD-AI).