# Deep Reinforcement Learning for Clinical Decision Support: A Brief Survey

arXiv: https://arxiv.org/abs/1907.09475 · HTML: https://mlsanity.com/paper/1907.09475/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2019.

## Built on by (most cited first, 4 of 4)

- [The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model](https://mlsanity.com/paper/2305.16589/) (2023)
- [Explainability in reinforcement learning: perspective and position](https://mlsanity.com/paper/2203.11547/) (2022)
- [Explaining Reinforcement Learning: A Counterfactual Shapley Values Approach](https://mlsanity.com/paper/2408.02529/) (2024)
- [Using AI to Optimize Patient Transfer and Resource Utilization During Mass-Casualty Incidents: A Simulation Platform](https://mlsanity.com/paper/2509.08756/) (2025)

## Abstract (arXiv)

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.