Reinforcement learning for intelligent healthcare applications: A survey

By A. Coronato, Muddasar Naeem, G. Pietro, Giovanni Paragliola. Artif. Intell. Medicine, 2020

299 citations recorded by the source corpus.

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 realization of advanced intelligent systems able to learn about clinical treatments and discover new medical knowledge from the huge amount of data collected. Reinforcement Learning (RL), which is a branch of Machine Learning (ML), has received significant attention in the medical community since it has the potentiality to support the development of personalized treatments in accordance with the more general precision medicine vision. This report presents a review of the role of RL in healthcare by investigating past work, and highlighting any limitations and possible future contributions.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/2bed17dc270c5c185278c262b2424295b6a21fce

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mohamad Forouzanfar
2. Shiva Maleki Varnosfaderani

Citations in the corpus

• The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century (2024, background): “Reinforcement learning, where algorithms learn to make sequences of decisions by trial and error, has potential in personalized treatment optimization [21].”