Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and...

Area of Science:

• Artificial Intelligence in Medicine
• Computational Health Informatics

Background:

• Machine learning (ML) and deep learning (DL) are pivotal in modern intelligent healthcare.
• These technologies address challenges in disease prediction, drug discovery, and medical imaging.

Purpose of the Study:

• To provide a comprehensive survey of ML and DL in healthcare systems.
• To highlight state-of-the-art features, integration benefits, applications, and future guidelines.

Main Methods:

• Conducted a literature review using prominent journal and conference databases.
• Identified and analyzed scholarly works using specific keywords related to ML and DL in healthcare.

Main Results:

• Summarized current advancements in ML-DL-based analysis for smart healthcare.
• Detailed the integration of ML-healthcare, DL-healthcare, and ML-DL-healthcare services.
• Presented diverse ML and DL applications within the healthcare industry.

Conclusions:

• Identified key research challenges and provided recommendations for future studies.
• Emphasized the transformative potential of ML and DL in advancing healthcare.
• Highlighted the need for continued research and development in this interdisciplinary field.