Beyond traditional scores: a critical look at machine learning approaches in cardiovascular risk assessment.

Risk scores for incident cardiovascular disease (CVD) are recommended by various clinical guidelines to inform decisions about preventive interventions1. Traditionally, these risk scores have been developed using regression-based methods2,3. Recent years have seen increasing numbers of models developed using machine learning (ML) or artificial intelligence (AI) techniques, but few are currently recommended by clinical guidelines for routine use.