Artificial intelligence and machine learning in cardiovascular risk assessment: A practical clinical framework

Cardiovascular disease remains the leading cause of death globally, yet traditional risk prediction models often fail to capture the complexity of individual patient risk. Artificial intelligence (AI) and machine learning (ML) offer approaches that integrate multiple data types and learn non-linear relationships, but the growing number of available methods makes model selection a practical challenge. This review provides a clinician-oriented framework for selecting AI methods in cardiovascular risk assessment across three model classes. Traditional ML approaches—logistic regression, decision trees, and support vector machines—offer interpretability and reliability with small, structured datasets. Ensemble methods, particularly Random Survival Forest and Gradient Boosting Machines, improve predictive performance by capturing complex variable interactions and handling time-to-event outcomes. Deep learning models, including DeepSurv and convolutional neural networks applied to ECG and cardiac imaging, achieve superior discrimination in large, high-dimensional datasets but require substantial data and computational resources, and remain vulnerable to dataset shift across clinical settings. Model selection must be governed by the clinical question, data structure, interpretability requirements, and feasibility of external validation—not by performance benchmarks alone. Explainable AI and hybrid architectures offer promising paths toward reconciling predictive power with clinical transparency. Prospective evidence that AI-guided decision-making improves cardiovascular outcomes remains the field's essential next step.