Advances in machine learning for cardiac event prediction: where can we still improve?

Artificial intelligence (AI) is playing a transformative role in cardiovascular care by enabling more precise prediction of adverse clinical events across prevention, imaging, electrophysiology, and interventional practice. AI models integrate high-dimensional clinical data and imaging-derived phenotypes to improve risk stratification beyond traditional scoring systems, identifying patients who may benefit from earlier, targeted interventions.

This review examines recent advances in AI-based cardiovascular event prediction, focusing on coronary artery disease, heart failure, cardiac arrhythmias, and valvular heart disease. In these domains, AI transcends conventional markers-such as ejection fraction and stenosis severity-to extract prognostically relevant insights from complex data streams. Despite this potential, clinical translation remains limited. Many models are retrospective, rely heavily on discrimination metrics, and lack consistent validation across diverse health systems and patient subgroups. Furthermore, risk estimates are rarely linked to explicit management pathways.

To facilitate adoption, we prioritize: rigorous external and prospective validation with a focus on calibration and fairness; coupling predictions with actionable care algorithms; and developing interpretable, workflow-integrated tools. Overcoming these barriers is essential to establishing AI-based risk prediction as a reliable clinical standard.