Deep Learning in Healthcare System for Quality of Service.

Area of Science:

• Computer Science
• Medical Informatics
• Artificial Intelligence

Background:

• Machine learning (ML) and deep learning (DL) demonstrate significant success in supply chain and manufacturing.
• Their capacity for handling complex data with minimal human input has led to widespread adoption in healthcare.
• Current healthcare systems extensively utilize ML and DL to enhance service and patient health quality.

Purpose of the Study:

• To present a state-of-the-art review of recent advancements in ML and DL.
• To examine the implementation of ML and DL in healthcare systems for multi-objective goals.
• To provide reference data for future ML and DL applications in healthcare.

Main Methods:

• A thorough review of 10 selected research papers was conducted.
• Papers focused on novel integrations of ML and DL within healthcare systems.
• The review synthesized findings on achieving diverse healthcare targets using these technologies.

Main Results:

• ML and DL have proven effective in disease diagnosis, acute condition detection, and medical image analysis.
• These technologies show promise in drug discovery, drug delivery systems, and smart health monitoring.
• The reviewed works demonstrate successful integration for achieving various healthcare objectives.

Conclusions:

• ML and DL are pivotal in advancing healthcare quality and efficiency.
• The reviewed literature provides a foundation for future ML and DL implementations across healthcare sectors.
• This work serves as a valuable reference for researchers and practitioners integrating AI in medicine.