Review of deep learning in healthcare

TL;DR

This paper reviews how deep learning techniques are applied in healthcare, focusing on network designs, applications, interpretability, and future challenges to improve health data analysis.

Contribution

It provides a comprehensive overview of deep learning methods in healthcare, highlighting recent advancements, applications, and unresolved issues.

Findings

Deep learning models enhance pattern recognition in healthcare data.

Current challenges include interpretability and data privacy.

Market trends show increasing adoption of deep learning in healthcare.

Abstract

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.