Reinforcement learning (RL), as a potent computational framework, has the capacity to transform the optimization of dynamic treatment regimens (DTRs) for intricate diseases such as sepsis and diabetes. This method, by acquiring effective treatment strategies through engagement with clinical data, offers potential for the personalization of healthcare. Nevertheless, the extensive implementation of deep reinforcement learning (DRL) models in clinical practice encounters a significant barrier: the absence of transparency and the “black box” characteristic of these models, which diminishes physicians’ confidence. This chapter of the book thoroughly analyzes the applications, methodology, and principal obstacles of reinforcement learning in the healthcare sector. We conduct a thorough examination of algorithms and their implementation in sepsis and diabetes case studies, emphasizing appropriate methodologies for medicine, including offline reinforcement learning and the utilization of simulated environments (in silico). Furthermore, we investigate the burgeoning function of Explainable AI (XAI) and Large Language Models (LLMs) as supportive instruments to tackle the issue of explainability. We illustrate the application of LLMs in incentive design, policy advising, and the generation of comprehensible explanations for RL model decisions. Ultimately, upon scrutinizing the technological and ethical problems, we ascertain that the future of reinforcement learning in medicine hinges not alone on algorithmic progress but also on the creation of dependable and transparent ways to reconcile computational capabilities with clinical requirements.