AI Doctor: The Rise of Artificial Intelligence in Healthcare - A Guide for Users, Buyers, Builders, and Investors

Explores the transformative impact of artificial intelligence (AI) on the healthcare industry AI Doctor: The Rise of Artificial Intelligence in Healthcare provides a timely and authoritative overview of the current impact and future potential of AI technology in healthcare. With a reader-friendly narrative style, this comprehensive guide traces the evolution of AI in healthcare, describes methodological breakthroughs, drivers and barriers of its adoption, discusses use cases across clinical medicine, administration and operations, and life sciences, and examines the business models for the entrepreneurs, investors, and customers. Detailed yet accessible chapters help those in the business and practice of healthcare recognize the remarkable potential of AI in areas such as drug discovery and development, diagnostics, therapeutics, clinical workflows, personalized medicine, early disease prediction, population health management, and healthcare administration and operations. Throughout the text, author Ronald M. Razmi, MD offers valuable insights on harnessing AI to improve health of the world population, develop more efficient business models, accelerate long-term economic growth, and optimize healthcare budgets. Addressing the potential impact of AI on the clinical practice of medicine, the business of healthcare, and opportunities for investors, AI Doctor: The Rise of Artificial Intelligence in Healthcare: Discusses what AI is currently doing in healthcare and its direction in the next decade Examines the development and challenges for medical algorithms Identifies the applications of AI in diagnostics, therapeutics, population health, clinical workflows, administration and operations, discovery and development of new clinical paradigms and more Presents timely and relevant information on rapidly expanding generative AI technologies, such as Chat GPT Describes the analysis that needs to be made by entrepreneurs and investors as they evaluate building or investing in health AI solutions Features a wealth of relatable real-world examples that bring technical concepts to life Explains the role of AI in the development of vaccines, diagnostics, and therapeutics during the COVID-19 pandemic AI Doctor: The Rise of Artificial Intelligence in Healthcare. A Guide for Users, Buyers, Builders, and Investors is a must-read for healthcare professionals, researchers, investors, entrepreneurs, medical and nursing students, and those building or designing systems for the commercial marketplace. The book's non-technical and reader-friendly narrative style also makes it an ideal read for everyone interested in learning about how AI will improve health and healthcare in the coming decades. Cover Title Page Copyright Page Dedication Page Contents About the Author Foreword Preface Acknowledgments Part I Roadmap of AI in Healthcare Chapter 1 History of AI and Its Promise in Healthcare 1.1 What is AI? 1.2 A Classification System for Underlying AI/ML Algorithms 1.3 AI and Deep Learning in Medicine 1.4 The Emergence of Multimodal and Multipurpose Models in Healthcare References Chapter 2 Building Robust Medical Algorithms 2.1 Obtaining Datasets That are Big Enough and Detailed Enough for Training 2.2 Data Access Laws and Regulatory Issues 2.3 Data Standardization and Its Integration into Clinical Workflows 2.4 Federated AI as a Possible Solution 2.5 Synthetic Data 2.6 Data Labeling and Transparency 2.7 Model Explainability 2.8 Model Performance in the Real World 2.9 Training on Local Data 2.10 Bias in Algorithms 2.11 Responsible AI References Chapter 3 Barriers to AI Adoption in Healthcare 3.1 Evidence Generation 3.2 Regulatory Issues 3.3 Reimbursement 3.4 Workflow Issues with Providers and Payers 3.5 Medical-Legal Barriers 3.6 Governance 3.7 Cost and Scale of Implementation 3.8 Shortage of Talent References Chapter 4 Drivers of AI Adoption in Healthcare 4.1 Availability of Data 4.2 Powerful Computers, Cloud Computing, and Open Source Infrastructure 4.3 Increase in Investments 4.4 Improvements in Methodology 4.5 Policy and Regulatory 4.5.1 FDA 4.5.2 Other Bodies 4.6 Reimbursement 4.7 Shortage of Healthcare Resources 4.8 Issues with Mistakes, Inefficient Care Pathways, and Non-personalized Care References Part II Applications of AI in Healthcare Chapter 5 Diagnostics 5.1 Radiology 5.2 Pathology 5.3 Dermatology 5.4 Ophthalmology 5.5 Cardiology 5.6 Neurology 5.7 Musculoskeletal 5.8 Oncology 5.8.1 Diagnosis and Treatment of Cancer 5.8.2 Histopathological Cancer Diagnosis 5.8.3 Tracking Tumor Development 5.8.4 Prognosis Detection 5.9 GI 5.10 COVID-19 5.11 Genomics 5.12 Mental Health 5.13 Diagnostic Bots 5.14 At Home Diagnostics/Remote Monitoring 5.15 Sound AI 5.16 AI in Democratizing Care References Chapter 6 Therapeutics 6.1 Robotics 6.2 Mental Health 6.3 Precision Medicine 6.4 Chronic Disease Management 6.5 Medication Supply and Adherence 6.6 VR References Chapter 7 Clinical Decision Support 7.1 AI in Decision Support 7.2 Initial Use Cases 7.3 Primary Care 7.4 Specialty Care 7.4.1 Cancer Care 7.4.2 Neurology 7.4.3 Cardiology 7.4.4 Infectious Diseases 7.4.5 COVID-19 7.5 Devices 7.6 End-of-Life AI 7.7 Patient Decision Support References Chapter 8 Population Health and Wellness 8.1 Nutrition 8.2 Fitness 8.3 Stress and Sleep 8.4 Population Health and Management 8.5 Risk Assessment 8.6 Use of Real World Data 8.7 Medication Adherence 8.8 Remote Engagement and Automation 8.9 SDOH 8.10 Aging in Place References Chapter 9 Clinical Workflows 9.1 Documentation Assistants 9.2 Quality Measurement 9.3 Nursing and Clinical Assistants 9.4 Virtual Assistants References Chapter 10 Administration and Operations 10.1 Providers 10.1.1 Documentation, Coding, and Billing 10.1.2 Practice Management and Operations 10.1.3 Hospital Operations 10.2 Payers 10.2.1 Payer Administrative Functions 10.2.2 Fraud 10.2.3 Personalized Communications References Chapter 11 AI Applications in Life Sciences 11.1 Drug Discovery 11.2 Clinical Trials 11.2.1 Information Engines 11.2.2 Patient Stratification 11.2.3 Clinical Trial Operations 11.3 Medical Affairs and Commercial References Part III The Business Case for AI in Healthcare Chapter 12 Which Health AI Applications Are Ready for Their Moment? 12.1 Methodology 12.2 Clinical Care 12.3 Administrative and Operations 12.4 Life Sciences References Chapter 13 The Business Model for Buyers of Health AI Solutions 13.1 Clinical Care 13.2 Administrative and Operations 13.3 Life Sciences 13.4 Guide for Buyer Assessment of Health AI Solutions References Chapter 14 How to Build and Invest in the Best Health AI Companies 14.1 Barriers to Entry and Intellectual Property (IP) 14.1.1 Creating Defensible Products 14.2 Startups Versus Large Companies 14.3 Sales and Marketing 14.4 Initial Customers 14.5 Direct-to-Consumer (D2C) 14.6 Planning Your Entrepreneurial Health AI Journey 14.7 Assessment of Companies by Investors 14.7.1 Key Areas to Explore for a Health AI Company for Investment References Index EULA