PRECISION HEALTH AND ARTIFICIAL INTELLIGENCE : with privacy, ethics, bias, health equity, best... practices, and case studies

This book provides a comprehensive explanation of precision (i.e., personalized) healthcare and explores how it can be advanced through artificial intelligence (AI) and other data-driven technologies. From improving the diagnosis, treatment, and monitoring of many medical conditions to the effective implementation of precise patient care, this book will help you understand datasets produced from digital health technologies and IoT and teach you how to employ analytical methods such as convolutional neural networks and deep learning to analyze that data. You’ll also see how this data-driven approach can enhance and democratize value-based healthcare delivery. Additionally, you’ll learn how the convergence of AI and precision health is revolutionizing healthcare, including some of the most difficult challenges facing precision medicine, such as ethics, bias, privacy, and health equity. Precision Health and Artificial Intelligence provides the groundwork for clinicians, engineers, bioinformaticians, and healthcare enthusiasts to apply AI to healthcare. What You Will Learn• Understand the components required to facilitate precision health and personalized care• Apply and implement precision health systems• Overcome the challenges of delivering precision healthcare at scale• Reconcile ethical and moral implications of delivering precision healthcare• Gain insight into the hurdles providers face while implementing precision healthcare Who This Book Is ForHealthcare professionals, clinicians, engineers, bioinformaticians, chief information officers (CIOs), and students Table of Contents 5 About the Author 11 About the Technical Reviewer 12 Acknowledgments 13 Introduction 14 Chapter 1: Introduction 17 From Personalized Medicine to Precision Health 19 Why Precision Health? Why Now? 25 Shifting Paradigms from Volume to Value 26 Social Determinants of Health 28 Why Diversity Is Essential Within Precision Health 31 Summary 33 Chapter 2: What Is Precision Health? 34 The Five Ps of Precision Health 37 Prediction and Prevention 37 Personalization of Treatment 40 Participation 42 Population 44 Considerations of Precision Health 45 Cost 45 Genes Are Just the Beginning 46 Health Equality 47 Unfulfilled Power of Data 48 Engagement 49 High Touch Means High Tech 50 Phenomics 51 Digital Transformation 51 Applying Precision Health: The P5H Precision Healthcare Continuum 53 Health Stages 55 Stage A 55 Stage B 56 Stage C 56 Stage D 57 Optimization Across Stages 57 Intervention Levels 58 Level 1 58 Level 2 58 Level 3 59 Level 4 59 Summary 60 Chapter 3: Data and the Digital Phenotype 61 Data Forms and Types 62 Forms 62 Types 63 Sources of Data 64 Sensors 66 Digital Phenotyping 68 Digital Twin 70 Data Challenges 71 Measurement and Completeness 72 Lack of Data on Social Determinants of Health 72 Privacy and Security 73 Cost 75 Disconnected from Data 75 Limited Adoption of Common Data Models 75 Expanding Beyond Qualitative Data 76 A Paradigm for Acting on Data 76 Turning Data into Information, Knowledge, and Wisdom 77 Summary 79 Chapter 4: Artificial Intelligence and Machine Learning in Precision Health 80 The Three Types of AI 82 Artificial Narrow Intelligence 82 Artificial General Intelligence 83 Artificial Superintelligence 83 A Brief Introduction to Machine Learning 83 Framework for Machine Learning 84 Software and Toolkits 86 Explainable AI 87 Applications of AI Assisted Precision Health in Practice 88 Clinical Decision Support 88 Behavioral Change Interventions and Lifestyle Medicine 89 New Treatments, Definitions of Disease, and Points of Intervention 92 Digital Twins 94 Health Promoting Chatbots 95 Voice Recognition 96 Summary 98 Chapter 5: Risks and Ethical Challenges of Precision Health 99 Responsible Development and Ethical AI Principles 100 Epistemic Principles 101 Interpretability 101 Reliability and Safety 102 General Ethical AI Principles 105 Bias, Inclusivity, and Fairness 105 Transparency and Accountability 107 Lawfulness 108 Data Privacy and Security 109 Human Agency 111 Beneficence 112 Redesigning Care and the Patient-Clinician Relationship 112 Health Inequalities 114 Theology 115 Preparing the Profession 115 Summary 116 Chapter 6: Future of Precision Healthcare 117 Precision Care from Birth to Death 118 Nanotechnology 119 DNA Manipulation and Gene Therapy 119 Smart Sensors 120 Bioprinting 120 Brain Computer Interfacing 121 Smart Habitats 122 Digital Health Education 124 Literacy 125 Changing Roles 127 Quality 128 Ability 128 Accessibility and Equity 129 New Forms of Training 129 Collaboration Between Academia and Industry 131 Summary 132 Chapter 7: Precision Healthcare in Practice 133 Delivery of Specialist Multidisciplinary Weight Management to Hospital-Based Patients Through a Digital Tool 135 Objective 138 Methods 138 How Does Personalization Appear? 139 Results 140 Discussion 142 Conclusion 144 Building on Our Evidence 145 Understanding People’s Attitudes Toward Data for the Optimization of a Specialist Podiatry Service for People with Long-Term Health Conditions 146 Objective 149 Methods 149 Results 150 Discussion 151 Conclusion 154 Impact of the Findings 155 Evaluation of a Digital Intervention for the Self-Management of Type 2 Diabetes and Prediabetes 156 Objectives 159 Methods 160 Results 161 Discussion 162 Conclusion 163 Impact of the Findings 163 Voice-Based Symptom Monitoring and AI-Based Rehabilitation for Patients with Long COVID 163 Background 164 Objective 166 Implementation Plan 167 Risks 168 Evaluation 168 Potential Impact 168 Developing a Digital Tool to Support Daily Behavioral Change for Children and Young People to Support Healthier Lives 170 Objective 172 Methods 172 Milestones 173 Evaluation 174 Impact of the Project 174 Summary 175 Index 176 This book provides a comprehensive explanation of precision (i.e., personalized) healthcare and explores how it can be advanced through artificial intelligence (AI) and other data-driven technologies. From improving the diagnosis, treatment, and monitoring of many medical conditions to the effective implementation of precise patient care, this book will help you understand datasets produced from digital health technologies and IoT and teach you how to employ analytical methods such as convolutional neural networks and deep learning to analyze that data. You'll also see how this data-driven approach can enhance and democratize value-based healthcare delivery. Additionally, you'll learn how the convergence of AI and precision health is revolutionizing healthcare, including some of the most difficult challenges facing precision medicine, such as ethics, bias, privacy, and health equity. Precision Health and Artificial Intelligence provides the groundwork for clinicians, engineers, bioinformaticians, and healthcare enthusiasts to apply AI to healthcare. What You Will Learn Understand the components required to facilitate precision health and personalized care Apply and implement precision health systems Overcome the challenges of delivering precision healthcare at scale Reconcile ethical and moral implications of delivering precision healthcare Gain insight into the hurdles providers face while implementing precision healthcare Who This Book Is For Healthcare professionals, clinicians, engineers, bioinformaticians, chief information officers (CIOs), and students This book provides a comprehensive explanation of precision (i.e., personalized) healthcare and explores how it can be advanced through Artificial Intelligence (AI) and other data-driven technologies. From improving the diagnosis, treatment, and monitoring of many medical conditions to the effective implementation of precise patient care, this book will help you understand datasets produced from digital health technologies and IoT and teach you how to employ analytical methods such as convolutional neural networks and Deep Learning to analyze that data. Open-source toolkits support Machine Learning by providing accessible and ready-to-use code for common algorithms. Most are available for Python, the programming language favored for developing Machine Learning algorithms. Scikit-learn is a Python module containing image processing and Machine Learning techniques built on SciPy and enables algorithms for clustering, classification, and regression, such as naïve Bayes, decision trees, random forests, k-means, and support vector machines. NLTK, or Natural Language Toolkit, is a collection of libraries used in natural language processing (NLP).