Cardiovascular diseases remain the leading cause of global morbidity and mortality. The paper reviews the role of ML in advancing cardiovascular risk assessment and discuss its potential to identify novel therapeutic targets and to improve prevention strategies. It also discusses key challenges inherent to ML, such as data quality, standardized reporting, model transparency and validation, and discuss barriers in its clinical translation. 

Read full article (Eur J Clin Invest. 2025;55(Suppl. 1):e70017) here!