A review of deep learning approaches in clinical and healthcare systems based on medical image analysis

Healthcare is a high-priority sector where people expect the highest levels of care and service, regardless of cost. That makes it distinct from other sectors. Due to the promising results of deep learning in other practical applications, many deep learning algorithms have been proposed for use in healthcare and to solve traditional artificial intelligence issues. The main objective of this study is to review and analyze current deep learning algorithms in healthcare systems. In addition, it highlights the contributions and limitations of recent research papers. It combines deep learning methods with the interpretability of human healthcare by providing insights into deep learning applications in healthcare solutions. It first provides an overview of several deep learning models and their most recent developments. It then briefly examines how these models are applied in several medical practices. Finally, it summarizes current trends and issues in the design and training of deep neural networks besides the future direction in this field.

Automated summary

This study reviews and analyzes current deep learning algorithms in healthcare systems, highlighting their contributions and limitations. By combining deep learning methods with the interpretability of human healthcare, it provides insights into deep learning applications in healthcare solutions.