A Review on the Methods and Clinical Hurdles for the Use of Artificial Intelligence in Forecasting Cardiovascular Risk

Review article

Abstract

Cardiovascular disease is the leading contributor to global mortality, calling for more sensitive, patient-specific risk evaluation to guide early interventions. While traditional risk calculators rely on a small set of linear clinical metrics and generate static, point-in-time assessments, modern computational models offer a paradigm shift. This review discusses how machine learning and deep learning algorithms process high-dimensional clinical data, raw electrocardiographic waveforms, continuous physiological streams from wearable devices, multi-modal diagnostic imaging, and comprehensive genomic assays to provide precise and individualized risk assessment. Deep neural networks automatically extract subclinical risk markers and discover novel phenotypes, frequently outperforming conventional calculators. However, the translation of these algorithms into clinical environments is hindered by persistent methodological limitations, including inadequate external validation, poor model calibration, inconsistent reporting, data heterogeneity, and a lack of transparency in "black-box" models. Ethical concerns regarding algorithmic bias and clinical utility must be addressed to ensure safe clinical deployment. Resolving these technical barriers requires establishing standardized reporting, prospective multi-center validation, and transparent interpretability frameworks. Shifting from traditional calculators to dynamic, continuously updated risk estimation platforms utilizing privacy-preserving collaborative frameworks such as federated learning represents the future of cardiovascular risk assessment.