Reinforcement Learning As A Tool For Healthcare

Abstract

Reinforcement Learning (RL) has emerged as a powerful machine learning paradigm capable of making sequential decisions under uncertainty. In healthcare, RL offers transformative potential for optimizing treatment strategies, managing chronic conditions, automating diagnosis, and personalizing care. This paper surveys recent developments in RL for healthcare, categorizes major applications, discusses unique challenges including safety and interpretability, and outlines future research directions to bridge the gap between laboratory models and clinical practice.