Reinforcement Learning in Optimizing Personalized Treatment Plans: A Review

Abstract

Personalized medicine represents a crucial direction in modern healthcare. Reinforcement learning, an artificial intelligence technique that learns optimal decision strategies through interaction with its environment, demonstrates significant potential in optimizing personalized treatment plans. This paper provides a comprehensive review of research progress in applying reinforcement learning to personalized treatment plan optimization. First, the background knowledge of personalized treatment and reinforcement learning is introduced, elucidating the significance and value of researching this topic. Subsequently, reinforcement learning modeling for personalized treatment problems is explored in detail. An in-depth analysis of algorithmic advancements and technical approaches in reinforcement learning follows. The application domain analysis section examines personalized cancer treatment and chronic disease management perspectives. Challenges faced by reinforcement learning in personalized treatment are discussed, and future development directions are projected. Finally, the paper summarizes the role and significance of reinforcement learning in optimizing personalized treatment plans. Research indicates that reinforcement learning holds great promise in providing robust support for optimizing personalized treatment strategies, thereby advancing progress in the medical field.