I am always ready to learn although I do not always like being taught Winston Churchill
Despite significant advances in the prevention and treatment of cardiovascular disease (CVD), refined risk-stratification and personalized treatment strategies remain a challenge, particularly for ischemic heart disease (IHD)1. Current decision-making frameworks rely on a combination of physician expertise, risk scores, and guideline-directed therapies, yet these approaches often fail to accommodate the complexity of individual patient profiles, and remain fraught with subjectivity and imprecision2. Traditional predictive models, such as the Systematic Coronary Risk Evaluation (SCORE), based on a patient age, sex, smoking, blood pressure and cholesterol, can estimate risk and guide broad treatment recommendations, but remain static and lack the adaptability required for individualized care3.
The increasing availability of extensive real-world data from electronic health records (EHRs) and administrative databases, imaging studies, and wearable devices, presents a unique opportunity to enhance decision-making by deploying and harnessing machine learning (ML) and artificial intelligence (AI)4. Many promising algorithms are available, but reinforcement learning (RL) offers a uniquely powerful, dynamic, and data-driven approach to treatment optimization by continuously refining strategies based on evolving clinical conditions (Table 1; Fig. 1)5. However, while ML and AI models have demonstrated impressive predictive capabilities, their successful implementation in routine cardiovascular care has remained limited with challenges in being able to translate into improved outcomes in real-world practice6. Additionally, the complexity of integrating AI into everyday clinical practice requires careful consideration of ethical, logistical, and regulatory aspects, all of which will influence its long-term viability and acceptance.
This diagram uses a balance scale to conceptually contrast traditional clinical decision-making (left) with reinforcement learning-enabled decision support (right). Traditional decision-making is rooted in physician expertise, clinical culture, and guidelines, typically characterized by static decisions, slower adaptation to new evidence, and broadly applicable treatment pathways. In contrast, reinforcement learning-based approaches are driven by data-derived policies and enable adaptive, sequential decision-making that continuously refines treatment strategies based on historical outcomes and patient-specific characteristics. Key attributes of each approach—such as familiarity and expert judgment on one side, versus transparency, personalization, and dynamic refinement on the other—highlight the theoretical and operational shift represented by reinforcement in cardiovascular care.
In their article in npj Digital Medicine, Ghasemi et al. begin to address several of these critical challenges with their offline RL model, demonstrating a practical approach to translating AI advances into meaningful clinical applications7. Here, we offer commentary on this seminal work—not with the intent to validate the clinical efficacy of offline reinforcement learning approaches, but rather to contextualize and interpret this important contribution within the broader landscape of medical AI.
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