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.

在过去的几年中，鉴于医疗保健数据的复杂性不断提高，诸如深度神经网络（DNN）模型之类的机器学习方法的应用在医疗保健系统中变得越来越有吸引力。机器学习（ML）算法提供了有效的数据分析模型，以从传统分析无法在合理的时间内发现的大量健康数据中发现隐藏的模式和其他有意义的信息。特别是，深度学习（DL）技术已显示为医疗系统中模式识别的有前途的方法。出于这种考虑，本文的贡献是通过审查尖端的网络体系结构，应用程序和行业趋势来研究应用于医疗保健系统的深度学习方法。目标是首先深入了解深度学习模型在医疗保健解决方案中的应用，以桥接深度学习技术和人类医疗保健的可解释性。然后，提出现有的挑战和未来的方向。