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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.

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Personalized Healthcare Recommendations with Q-Learning Reinforcement Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Poi Tamrakar,
  • Ganesh R. Pathak,
  • Mily Lal,
  • Akanksha Goel,
  • Manisha Bhende

摘要

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.