  Given the growing complexity of healthcare data over the last several years,
using machine learning techniques like Deep Neural Network (DNN) models has
gained increased appeal. In order to extract hidden patterns and other valuable
information from the huge quantity of health data, which traditional analytics
are unable to do in a reasonable length of time, machine learning (ML)
techniques are used. Deep Learning (DL) algorithms in particular have been
shown as potential approaches to pattern identification in healthcare systems.
This thought has led to the contribution of this research, which examines deep
learning methods used in healthcare systems via an examination of cutting-edge
network designs, applications, and market trends. To connect deep learning
methodologies and human healthcare interpretability, the initial objective is
to provide in-depth insight into the deployment of deep learning models in
healthcare solutions. And last, to outline the current unresolved issues and
potential directions.
