A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues

In the last few years, the application of Machine Learning approaches like Deep Neural Network (DNN) models have become more attractive in the healthcare system given the rising complexity of the healthcare data. Machine Learning (ML) algorithms provide efficient and effective data analysis models to uncover hidden patterns and other meaningful information from the considerable amount of health data that conventional analytics are not able to discover in a reasonable time. In particular, Deep Learning (DL) techniques have been shown as promising methods in pattern recognition in the healthcare systems. Motivated by this consideration, the contribution of this paper is to investigate the deep learning approaches applied to healthcare systems by reviewing the cutting-edge network architectures, applications, and industrial trends. The goal is first to provide extensive insight into the application of deep learning models in healthcare solutions to bridge deep learning techniques and human healthcare interpretability. And then, to present the existing open challenges and future directions.

Automated summary

This paper investigates the application of deep learning approaches in healthcare systems, focusing on advanced network architectures, applications, and industry trends. The goal is to provide in-depth insights into the application of deep learning models in healthcare solutions, bridging deep learning techniques and human healthcare interpretability, and presenting existing challenges and future directions.