Personalized Healthcare Recommendations with Q-Learning Reinforcement Learning

Abstract

The transforming potential of Q-Learning in customized medical recommendations is examined in this study. Its performance in comparison to conventional methods, strategic data utilization, and flexibility in a variety of scenarios all show great promise. However, a significant gap in the literature emphasizes how important it is to take ethics into account when using patient data. The findings show that Q-learning improves patient outcomes, but its ethical implications are still mostly unknown. This study offers ethical guidelines for future research that address the identified gap. In short, even though Q-Learning has many advantages, ethical issues must be bridged in order to promote responsible integration into healthcare, striking a balance between technical advancements as well as moral principles to enhance patient outcomes.