A Systematic Mapping on Applications of Deep Reinforcement Learning in Medical Imaging using Bibliometric Analysis

Abstract

In recent years, the use of deep learning in health care sector has been increased tremendously. However, the researchers are mainly focused on supervised and unsupervised learning than reinforcement learning. This study aims to analyze the research trends in healthcare and deep reinforcement learning over a specified period of time. The biblioshiny, a bibliometric analysis tool and x-stat of MS-Excel has been used to analyse the 1678 articles in scopus database published between 2007 and 2024. The main contribution of this work is to offer insights into the research trends in healthcare and deep reinforcement learning, emphasizing the significance of exploring new pathways in medical imaging through bibliometric analysis. This study aims to bridge the gap between traditional machine learning techniques and the potential benefits of reinforcement learning in medical imaging applications. To provide insights into the current research landscape in medical imaging and highlight the potential for further exploration of deep reinforcement learning in healthcare applications. Additionally, this study aims to guide future research directions in leveraging advanced machine learning techniques for improving medical imaging practices.

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