,Table of Contents
Cover image
Title page
Copyright
Endorsement
List of contributors
About the editors
Biographies
Preface
About this book
Intended audience
How is this book organized
Introduction
The promise of an intelligent machine
Current applications and challenges in healthcare
Chapter 1. Current healthcare, big data, and machine learning
Abstract
1.1 Current healthcare practice
1.2 Value-based treatments and healthcare services
1.3 Increasing data volumes in healthcare
1.4 Analytics of healthcare data (machine learning and deep learning)
1.5 Conclusions/summary
,References
Chapter 2. The rise of artificial intelligence in healthcare applications
Abstract
2.1 The new age of healthcare
2.2 Precision medicine
2.3 Artificial intelligence and medical visualization
2.4 Intelligent personal health records
2.5 Robotics and artificial intelligence-powered devices
2.6 Ambient assisted living
2.7 The artificial intelligence can see you now
References
Chapter 3. Drug discovery and molecular modeling using artificial intelligence
Abstract
3.1 Introduction. The scope of artificial intelligence in drug discovery
3.2 Various types of machine learning in artificial intelligence
3.3 Molecular modeling and databases in artificial intelligence for drug molecules
3.4 Computational mechanics ML methods in molecular modeling
3.5 Drug characterization using isopotential surfaces
3.6 Drug design for neuroreceptors using artificial neural network techniques
3.7 Specific use of deep learning in drug design
3.8 Possible future artificial intelligence development in drug design and development
References
Chapter 4. Applications of artificial intelligence in drug delivery and pharmaceutical development
, Abstract
4.1 The evolving pharmaceutical field
4.2 Drug delivery and nanotechnology
4.3 Quality-by-design R&D
4.4 Artificial intelligence in drug delivery modeling
4.5 Artificial intelligence application in pharmaceutical product R&D
4.6 Landscape of AI implementation in the drug delivery industry
4.7 Conclusion: the way forward
References
Chapter 5. Cancer diagnostics and treatment decisions using artificial intelligence
Abstract
5.1 Background
5.2 Artificial intelligence, machine learning, and deep learning in cancer
5.3 Artificial intelligence to determine cancer susceptibility
5.4 Artificial intelligence for enhanced cancer diagnosis and staging
5.5 Artificial intelligence to predict cancer treatment response
5.6 Artificial intelligence to predict cancer recurrence and survival
5.7 Artificial intelligence for personalized cancer pharmacotherapy
5.8 How will artificial intelligence affect ethical practices and patients?
5.9 Concluding remarks
References
Chapter 6. Artificial intelligence for medical imaging
Abstract
Cover image
Title page
Copyright
Endorsement
List of contributors
About the editors
Biographies
Preface
About this book
Intended audience
How is this book organized
Introduction
The promise of an intelligent machine
Current applications and challenges in healthcare
Chapter 1. Current healthcare, big data, and machine learning
Abstract
1.1 Current healthcare practice
1.2 Value-based treatments and healthcare services
1.3 Increasing data volumes in healthcare
1.4 Analytics of healthcare data (machine learning and deep learning)
1.5 Conclusions/summary
,References
Chapter 2. The rise of artificial intelligence in healthcare applications
Abstract
2.1 The new age of healthcare
2.2 Precision medicine
2.3 Artificial intelligence and medical visualization
2.4 Intelligent personal health records
2.5 Robotics and artificial intelligence-powered devices
2.6 Ambient assisted living
2.7 The artificial intelligence can see you now
References
Chapter 3. Drug discovery and molecular modeling using artificial intelligence
Abstract
3.1 Introduction. The scope of artificial intelligence in drug discovery
3.2 Various types of machine learning in artificial intelligence
3.3 Molecular modeling and databases in artificial intelligence for drug molecules
3.4 Computational mechanics ML methods in molecular modeling
3.5 Drug characterization using isopotential surfaces
3.6 Drug design for neuroreceptors using artificial neural network techniques
3.7 Specific use of deep learning in drug design
3.8 Possible future artificial intelligence development in drug design and development
References
Chapter 4. Applications of artificial intelligence in drug delivery and pharmaceutical development
, Abstract
4.1 The evolving pharmaceutical field
4.2 Drug delivery and nanotechnology
4.3 Quality-by-design R&D
4.4 Artificial intelligence in drug delivery modeling
4.5 Artificial intelligence application in pharmaceutical product R&D
4.6 Landscape of AI implementation in the drug delivery industry
4.7 Conclusion: the way forward
References
Chapter 5. Cancer diagnostics and treatment decisions using artificial intelligence
Abstract
5.1 Background
5.2 Artificial intelligence, machine learning, and deep learning in cancer
5.3 Artificial intelligence to determine cancer susceptibility
5.4 Artificial intelligence for enhanced cancer diagnosis and staging
5.5 Artificial intelligence to predict cancer treatment response
5.6 Artificial intelligence to predict cancer recurrence and survival
5.7 Artificial intelligence for personalized cancer pharmacotherapy
5.8 How will artificial intelligence affect ethical practices and patients?
5.9 Concluding remarks
References
Chapter 6. Artificial intelligence for medical imaging
Abstract
