  Owe to the recent advancements in Artificial Intelligence especially deep
learning, many data-driven decision support systems have been implemented to
facilitate medical doctors in delivering personalized care. We focus on the
deep reinforcement learning (DRL) models in this paper. DRL models have
demonstrated human-level or even superior performance in the tasks of computer
vision and game playings, such as Go and Atari game. However, the adoption of
deep reinforcement learning techniques in clinical decision optimization is
still rare. We present the first survey that summarizes reinforcement learning
algorithms with Deep Neural Networks (DNN) on clinical decision support. We
also discuss some case studies, where different DRL algorithms were applied to
address various clinical challenges. We further compare and contrast the
advantages and limitations of various DRL algorithms and present a preliminary
guide on how to choose the appropriate DRL algorithm for particular clinical
applications.
