Artificial Intelligence in Cardiovascular Risk Prediction: An Up-to-Date Narrative Review on the Emerging Role of Lipid Profile-Based Models

Abstract

Introduction: Cardiovascular risk prediction remains challenging, particularly in patients with intermediate risk, mixed dyslipidemia, elevated lipoprotein(a), or variable lipid profiles. Conventional risk calculators may not fully capture nonlinear relationships among lipid, clinical, imaging, and longitudinal data. Objectives: This narrative review summarizes evidence on artificial intelligence (AI)-based cardiovascular risk assessment, focusing on lipid profile-based and multimodal models incorporating lipid-related variables. Methods: PubMed/MEDLINE, Scopus, and Google Scholar were searched for English-language articles published up to January 2026. Original studies, reviews, and relevant clinical guidelines addressing AI-based cardiovascular risk models, lipid-related predictors, and clinically applicable approaches were considered. Results: Lipid profile-based AI models may identify lipid phenotypes and lipid-related patterns associated with increased cardiovascular risk, while multimodal models have shown improved performance in selected datasets. However, the reviewed studies address heterogeneous tasks, including phenotype classification, cardiovascular event prediction, mortality prediction, patient trajectory modeling, and absolute risk estimation. Most evidence remains retrospective, with limited external validation, calibration assessment, and clinical utility data. Conclusions: AI-based models may support cardiovascular risk assessment, but routine implementation requires prospective validation, standardized evaluation, calibration, explainability, and clinical impact studies.