Applications of Deep Learning Techniques in Healthcare Systems: A Review

Area of Science:

• Healthcare Technology
• Medical Informatics
• Artificial Intelligence

Background:

• Artificial intelligence (AI) simulates human intelligence in machines.
• AI is increasingly applied in healthcare for diagnosis, treatment, and drug discovery.
• Deep learning, a subset of AI, utilizes methods like CNNs, SAEs, and RNNs.

Purpose of the Study:

• To review the application of deep learning methods in healthcare.
• To highlight the use of CNNs, SAEs, and RNNs in medical contexts.
• To present studies on AI in medical imaging, drug discovery, and remote monitoring.

Main Methods:

• Review of existing literature on deep learning in healthcare.
• Focus on convolutional neural networks (CNNs) for image analysis.
• Inclusion of stacked autoencoders (SAEs) for feature learning and recurrent neural networks (RNNs) for sequential data analysis.

Main Results:

• Deep learning methods show significant utility in medical image analysis.
• AI applications accelerate drug discovery and development processes.
• Remote patient monitoring is enhanced through AI-driven data analysis.

Conclusions:

• Deep learning methods are versatile tools in modern healthcare.
• AI integration promises advancements in diagnostics, therapeutics, and patient care.
• Further research into AI applications can revolutionize medical practices.