What question did this study set out to answer?

This study aims to evaluate the applications, predictive performance, and translational limitations of AI models for cardiovascular risk prediction.

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

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An umbrella review evaluates AI's predictive performance in cardiovascular risk, indicating significant potential but highlighting challenges.

This study aims to evaluate the applications, predictive performance, and translational limitations of AI models for cardiovascular risk prediction.
Systematic search of PubMed, Scopus, and Web of Science.
Inclusion of various study designs including RCTs and cohort studies.
Primary outcome assessed using the area under the receiver operating characteristic curve (AUC).
Evaluation of methodological quality using risk-of-bias tools.
AI models achieved AUC values greater than 0.90 in over 70% of imaging-based studies.
Evidence synthesis revealed reliance on internal validation and inconsistent calibration reporting.
Multimodal data integration improved detection of coronary artery disease (CAD) and heart failure (HF).
Wearable technology reduced hospitalization rates by 18–25% compared to usual care.

Parizad et al. (2026) studied this question.

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An umbrella review evaluates AI's predictive performance in cardiovascular risk, indicating significant potential but highlighting challenges.