Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective.

Area of Science:

• Computational Biology
• Medical Informatics
• Artificial Intelligence in Medicine

Background:

• Personalized medicine tailors treatments using patient genetics, lifestyle, and inheritance.
• Advancements in analyzing complex, high-volume data drive the use of learning methods.
• Deep learning shows significant promise in advancing personalized medicine.

Purpose of the Study:

• To provide a comprehensive overview of learning methods, focusing on deep learning, in personalized medicine.
• To review applications in drug development, disease characteristic identification, and therapeutic effect prediction.
• To analyze the advantages and disadvantages of applied learning methods.

Main Methods:

• Review of current research on learning methods in personalized medicine.
• Focus on deep learning applications across three key domains.
• Detailed examination of applied methods, their pros, and cons.

Main Results:

• Learning methods excel at handling data complexity, nonlinearity, and feature recognition.
• Deep learning demonstrates utility in drug development, disease identification, and treatment prediction.
• Despite advantages, learning methods are not universally applicable to all medical challenges.

Conclusions:

• Learning methods, particularly deep learning, offer powerful tools for personalized medicine.
• Further research is needed to address existing challenges and explore future directions.
• The review provides insights into the current state and future potential of AI in personalized healthcare.