Reinforcement Learning Applications in Health Informatics

Abstract

Reinforcement learning (RL) is a branch of Artificial intelligence (AI) that makes complex decisions all by itself. Unlike traditional AI systems that passively absorb knowledge provided by humans, the RL technology actively teaches itself through trial and error by interacting with a simulated environment. RL is used in various domains including video games, robotics, natural language processing, and financial analysis. This chapter discusses the opportunities that RL provides in the healthcare field, along with the challenges and limitations associated with each of its applications. Specifically, the adoption of RL in the Internet of Things healthcare devices, medication dosing, drug design, treatment recommendation, lung radiotherapy, personal health, and sepsis treatment has overcome a number of challenges. For example, RL helps in determining the dosage for patients, designing drugs, and guiding patients towards a healthier lifestyle. However, the use of RL in the healthcare field is still limited by the availability and accuracy of relevant medical datasets, requires further validation, and takes time to adapt to changes in the environment.