Despite significant advances, the prevention and management of cardiovascular disease remain challenging, especially for ischemic heart disease (IHD). Current clinical decision-making relies heavily on physician expertise, guideline-directed therapies, and static risk scores, which often inadequately accommodate individual patient complexity. Machine learning (ML) and artificial intelligence (AI), particularly reinforcement learning (RL), may augment current physician-driven approaches and provide enhanced cardiovascular disease prevention and management. Indeed, offline RL refers to a class of ML algorithms that learn optimal decision-making policies from a fixed dataset of previously collected experiences—such as electronic health records or registries—without the need for active, real-time interaction with the clinical environment. This approach enables the safe development of treatment strategies in high-stakes domains where experimentation on live patients could be unethical or impractical. Notably, offline RL models hold the promise of optimizing decision-making in complex clinical settings, such as revascularization strategies for coronary artery disease. However, challenges remain in integrating AI into practice, ensuring interpretability, maintaining performance, and proving cost-effectiveness. Ultimately, validation, integration, and collaboration among clinicians, researchers, and policymakers are crucial for transforming AI-driven solutions into practical, patient-centered cardiovascular care improvements, pending prospective (and hopefully randomized) validation.
- Giuseppe Biondi-Zoccai
- Arjun Mahajan
- Giacomo Frati
