Reinforcement learning for intelligent healthcare applications: A survey

Area of Science:

• Healthcare technology
• Medical informatics
• Computational biology

Background:

• Modern clinical research aims to discover novel treatments and personalize existing therapies.
• Artificial Intelligence (AI) has advanced healthcare by creating systems that learn from vast datasets.
• Reinforcement Learning (RL), a Machine Learning (ML) subset, is gaining traction for its potential in precision medicine.

Purpose of the Study:

• To review the application of Reinforcement Learning (RL) in healthcare.
• To examine past research on RL in medical contexts.
• To identify limitations and future directions for RL in healthcare.

Main Methods:

• Literature review of Reinforcement Learning (RL) applications in healthcare.
• Analysis of AI and ML contributions to clinical research.
• Synthesis of findings on personalized medicine and RL.

Main Results:

• AI, particularly RL, shows significant promise for developing personalized treatment strategies.
• RL can analyze complex patient data to inform treatment decisions.
• The review identifies key areas where RL has been applied and its impact.

Conclusions:

• Reinforcement Learning (RL) is a key technology for advancing precision medicine.
• Further research is needed to overcome limitations and fully realize RL's potential in healthcare.
• RL offers a powerful approach to discovering new medical knowledge and optimizing patient care.