Reinforcement Learning for Personalized Medicine: Optimizing Treatment Strategies Through AI-Enabled Decision Support Systems

Abstract

Reinforcement learning in healthcare has drawn much interest because personalized medicine can optimize treatment strategies based on unique patient characteristics, and RL can afford dynamic decision-making. In this context, this study proposes an explainable AI-based decision support system that performs the treatment personalization by using Proximal Policy Optimization (PPO) and provides a set of recommended drugs. The RL model can learn optimal treatment policies through patient data interaction, dynamic dosage adjustment, producing improvement in clinical outcomes. We demonstrate through simulations that the proposed approach reliably outperforms classical planning methods on success rate, adaptability, and robustness against noisy data. The study illustrates RL's promise in precision medicine using real-world datasets, such as MIMIC-III. These findings underscore the transformative potential of RL in the healthcare domain, providing a scalable abiotic framework for intelligent, patient-specific treatment optimization.

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