The rise of chronic disease patients and the pandemic pose immediate threats to healthcare
expenditure and mortality rates. This calls for transforming healthcare systems away from …

The rapid increase in the percentage of chronic disease patients along with the recent
pandemic pose immediate threats on healthcare expenditure and elevate causes of death …

Discovering new treatments and personalizing existing ones is one of the major goals of
modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the …

As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision
making by using interaction samples of an agent with its environment and the potentially …

Reinforcement learning (RL) has emerged as a dynamic and transformative paradigm in
artificial intelligence, offering the promise of intelligent decision-making in complex and …

The paper provides the readers with the knowledge of how reinforcement learning (RL)
applications can be applied in medical diagnosis and healthcare. RL is a powerful and …

Many healthcare decisions involve navigating through a multitude of treatment options in a
sequential and iterative manner to find an optimal treatment pathway with the goal of an …

Many reinforcement learning (RL) applications have combinatorial action spaces, where
each action is a composition of sub-actions. A standard RL approach ignores this inherent …

Reinforcement learning (RL) can be used to learn treatment policies and aid decision
making in healthcare. However, given the need for generalization over complex state/action …

With the advancements in reinforcement learning (RL), new variants of this artificial
intelligence approach have been introduced in the literature. This has led to increased …