
Machine Learning for Medical Applications
Computational Drug Discovery, Bioimaging, Smart Biomaterials
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Machine Learning for Medical Applications
Computational Drug Discovery, Bioimaging, Smart Biomaterials
About this book
Machine Learning for Medical Applications – Volume I provides an in-depth look into the frontier of artificial intelligence in healthcare, bringing together contributions from leading researchers and innovators. This volume focuses on three critical areas: computational drug discovery, advanced bioimaging techniques, and the development of smart biomaterials for medical use. Readers will discover how machine learning is revolutionizing personalized medicine, improving diagnostic accuracy, and enabling the design of AI-driven biomedical sensors and therapeutic systems. With practical insights into algorithmic modeling, drug toxicity prediction, and materials screening, this book bridges the gap between data science and clinical applications. Ideal for professionals, academics, and students in biomedical engineering, computer science, and medical informatics, this book highlights the synergistic potential of machine learning and modern medicine in shaping the future of healthcare.
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Information
Table of contents
- Title Page
- Copyright
- Contents
- Frontmatter
- Contents
- List of contributors
- Blockchain technology to secure medical data sharing in machine learning applications ensure privacy and integrity
- AI-powered sensors and devices for sustained health tracking
- Development of AI-driven biomedical sensors and devices optimization for continuous health monitoring
- Design and development of AI-driven biomedical sensors and devices and their optimization for continuous health monitoring
- Machine learning-driven personalized medicine: customized drug delivery systems and patient-specific material applications
- Personalized medicine using customized drug delivery systems and patient-specific material solutions, enabled by machine learning algorithms
- AI-driven drug design exploring molecular docking and lead optimization using machine learning algorithms
- Machine learning models for predicting drug toxicity and side effects
- Machine learning innovations in biomedical materials from drug discovery to personalized medicine
- High-throughput screening for novel medical materials: machine learning-enabled approaches
- Automated materials characterization using machine learning for screening biocompatible materials
- Machine learning algorithms for enhanced medical image analysis and diagnostics
- Transforming healthcare with machine learning
- Revolutionizing healthcare
- Index
- Index