Abstract
Reinforcement learning (RL), a subset of artificial intelligence, is gaining momentum in personalized medicine due to its ability to model dynamic, sequential decision-making. Unlike traditional machine learning approaches, RL systems adapt treatment protocols based on patient-specific responses and evolving health states, offering a robust strategy for optimizing individualized care. This review explores the integration of RL into personalized medicine across diverse clinical domains, including oncology, chronic disease management, psychiatry, infectious diseases, and rehabilitation. Applications such as chemotherapy scheduling, insulin dosing, personalized antidepressant treatment, and ICU management illustrate RL's capacity to improve therapeutic outcomes by maximizing long-term clinical benefits. Key methodological components, including data integration, reward signal engineering, and interpretability challenges, are discussed alongside solutions such as explainable AI tools, surrogate models, and federated learning. Ethical and regulatory considerations are also examined, highlighting issues such as patient consent, algorithmic bias, and evolving guidelines from regulatory bodies like the Food and Drug Administration and the European Medicines Agency. The review emphasizes the importance of interdisciplinary collaboration and clinician engagement for the successful deployment of RL in healthcare settings. RL presents a transformative framework for delivering adaptive, equitable, and patient-centered treatment strategies. Future research should focus on implementing it safely, scalably, and transparently to fully harness its potential.