Reinforcement Learning and Its Clinical Applications Within Healthcare: A Systematic Review of Precision Medicine and Dynamic Treatment Regimes

Abstract

<b>Background/Objectives</b>: Reinforcement learning (RL), a subset of machine learning, has emerged as a promising tool for supporting precision medicine and dynamic treatment regimes by enabling adaptive, data-driven clinical decision making. Despite its potential, challenges such as interpretability, reward definition, data limitations, and clinician adoption remain. This review aims to evaluate the recent advancements in RL in precision medicine and dynamic treatment regimes, highlight clinical fields of application, and propose practical frameworks for future integration into medical practice. <b>Methods</b>: A systematic review was conducted following PRISMA guidelines across PubMed, MEDLINE, and Web of Science databases, focusing on studies from January 2014 to December 2024. Articles were included based on their relevance to RL applications in precision medicine and dynamic treatment regime within healthcare. Data extraction captured study characteristics, algorithms used, specialty area, and outcomes. <b>Results</b>: Forty-six studies met the inclusion criteria. RL applications were concentrated in endocrinology, critical care, oncology, and behavioral health, with a focus on dynamic and personalized treatment planning. Hybrid and value-based RL methods were the most utilized. Since 2020, there has been a sharp increase in RL research in healthcare, driven by advances in computational power, digital health technologies, and increased use of wearable devices. <b>Conclusions</b>: RL offers a powerful opportunity to augment clinical decision making by enabling dynamic and individualized patient care. Addressing key barriers related to transparency, data availability, and alignment with clinical workflows will be critical to translating RL into everyday medical practice.

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