K Banumathi, Venkatesan Latha, Benjamin Lizy Sonia, K Vijayalakshmi, Satchi Nesa Sathya
Community Health Nursing, Apollo College of Nursing, The Tamil Nadu Dr. Maruthur Gopalan Ramachandran (MGR) Medical University, Chennai, IND.
Obstetrics and Gynecology, All India Institute of Medical Sciences, New Delhi, New Delhi, IND.
Cureus. 2025 Apr 21;17(4):e82756. doi: 10.7759/cureus.82756. eCollection 2025 Apr.
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.
强化学习（RL）作为人工智能的一个子集，因其能够对动态的、顺序性的决策进行建模，在个性化医疗中越来越受到关注。与传统机器学习方法不同，强化学习系统能够根据患者的特定反应和不断变化的健康状况调整治疗方案，为优化个性化护理提供了一个强大的策略。本文综述探讨了强化学习在肿瘤学、慢性病管理、精神病学、传染病和康复等不同临床领域融入个性化医疗的情况。化疗方案安排、胰岛素剂量调整、个性化抗抑郁治疗以及重症监护病房管理等应用实例展示了强化学习通过最大化长期临床效益来改善治疗效果的能力。本文还讨论了关键的方法学组成部分，包括数据整合、奖励信号设计以及可解释性挑战，同时介绍了诸如可解释人工智能工具、替代模型和联邦学习等解决方案。文中还审视了伦理和监管方面的考量，强调了患者同意、算法偏差以及食品药品监督管理局和欧洲药品管理局等监管机构不断演变的指导方针等问题。本文综述强调了跨学科合作和临床医生参与对于在医疗环境中成功部署强化学习的重要性。强化学习为提供适应性强、公平且以患者为中心的治疗策略提供了一个变革性框架。未来的研究应专注于安全、可扩展且透明地实施强化学习，以充分发挥其潜力。
