Reinforcement learning for optimizing cardiovascular treatment strategies: A narrative review

Cardiovascular diseases require individualized and often sequential treatment decisions because patient responses, comorbidities, and physiological status change over time. Reinforcement learning (RL) is a machine-learning paradigm in which an agent learns a policy by selecting actions in an environment and receiving rewards that represent desired outcomes. This narrative review examined RL applications in cardiovascular treatment optimization, personalized therapy, clinical decision support, and implementation. A structured search of PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar covered January 2010 through March 2026. Eligibility was defined using a PICOS-informed framework, and direct RL evidence was distinguished from contextual artificial intelligence and machine-learning literature. Findings were synthesized thematically by cardiovascular condition, RL architecture, outcome domain, and implementation challenge, with design-appropriate critical appraisal. The direct clinical evidence was concentrated in retrospective offline analyses, simulations, and proof-of-concept studies. Q-learning and deep Q-networks, actor-critic and policy-gradient methods, model-based RL, and offline RL were applied to coronary disease, heart failure, hypertension, medication dosing, and cardiovascular critical care. Reported benefits included more individualized treatment sequences, hemodynamic support, and decision support; however, clinical endpoints were not directly comparable, and prospective and external validation remained limited. Data quality, explainability, fairness, clinician oversight, workflow integration, and regulation were persistent barriers. RL may support adaptive cardiovascular care, but current evidence does not establish routine clinical effectiveness or deployment readiness. Prospective multicenter evaluation, transparent governance, and post-deployment monitoring are required.