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.
Sprache
Verlagsort
Zielgruppe
Für Beruf und Forschung
Für höhere Schule und Studium
Maße
Höhe: 218 mm
Breite: 157 mm
Dicke: 28 mm
Gewicht
ISBN-13
978-1-394-24016-6 (9781394240166)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
RONALD M. RAZMI, MD is a cardiologist and the co-founder and Managing Director of Zoi Capital, a venture capital firm that invests in the applications of AI in healthcare. Dr. Razmi completed his medical training at the Mayo Clinic and holds an MBA from Northwestern University's Kellogg School of Management. He was a McKinsey consultant before launching a population health management software company at the dawn of digital health. He saw firsthand the confluence of clinical, technical, and business factors that need to come together for new technologies to gain a foothold in healthcare. He is a co-author of the Handbook of Cardiovascular Magnetic Resonance Imaging.
Autor*in
Zoi Capital; Northwestern University's Kellogg School of Management
About the Author xi
Foreword xiii
Preface xix
Acknowledgments xxiii
Part I Roadmap of AI in Healthcare 1
1 History of AI and Its Promise in Healthcare 3
1.1 What is AI? 5
1.2 A Classification System for Underlying AI/ML Algorithms 14
1.3 AI and Deep Learning in Medicine 17
1.4 The Emergence of Multimodal and Multipurpose Models in Healthcare 20
References 23
2 Building Robust Medical Algorithms 27
2.1 Obtaining Datasets That are Big Enough and Detailed Enough for Training 30
2.2 Data Access Laws and Regulatory Issues 33
2.3 Data Standardization and Its Integration into Clinical Workflows 34
2.4 Federated AI as a Possible Solution 36
2.5 Synthetic Data 40
2.6 Data Labeling and Transparency 43
2.7 Model Explainability 45
2.8 Model Performance in the Real World 50
2.9 Training on Local Data 52
2.10 Bias in Algorithms 53
2.11 Responsible AI 60
References 62
3 Barriers to AI Adoption in Healthcare 67
3.1 Evidence Generation 71
3.2 Regulatory Issues 74
3.3 Reimbursement 76
3.4 Workflow Issues with Providers and Payers 78
3.5 Medical- Legal Barriers 81
3.6 Governance 83
3.7 Cost and Scale of Implementation 85
3.8 Shortage of Talent 86
References 86
4 Drivers of AI Adoption in Healthcare 91
4.1 Availability of Data 92
4.2 Powerful Computers, Cloud Computing, and Open Source Infrastructure 93
4.3 Increase in Investments 94
4.4 Improvements in Methodology 95
4.5 Policy and Regulatory 95
4.5.1 Fda 95
4.5.2 Other Bodies 100
4.6 Reimbursement 102
4.7 Shortage of Healthcare Resources 105
4.8 Issues with Mistakes, Inefficient Care Pathways, and Non- personalized Care 106
References 110
Part II Applications of AI in Healthcare 113
5 Diagnostics 115
5.1 Radiology 115
5.2 Pathology 122
5.3 Dermatology 124
5.4 Ophthalmology 125
5.5 Cardiology 127
5.6 Neurology 132
5.7 Musculoskeletal 133
5.8 Oncology 134
5.8.1 Diagnosis and Treatment of Cancer 136
5.8.2 Histopathological Cancer Diagnosis 136
5.8.3 Tracking Tumor Development 136
5.8.4 Prognosis Detection 137
5.9 Gi 139
5.10 Covid- 19 139
5.11 Genomics 140
5.12 Mental Health 141
5.13 Diagnostic Bots 142
5.14 At Home Diagnostics/Remote Monitoring 144
5.15 Sound AI 148
5.16 AI in Democratizing Care 149
References 150
6 Therapeutics 157
6.1 Robotics 158
6.2 Mental Health 159
6.3 Precision Medicine 161
6.4 Chronic Disease Management 164
6.5 Medication Supply and Adherence 167
6.6 Vr 168
References 169
7 Clinical Decision Support 171
7.1 AI in Decision Support 176
7.2 Initial Use Cases 180
7.3 Primary Care 182
7.4 Specialty Care 185
7.4.1 Cancer Care 185
7.4.2 Neurology 185
7.4.3 Cardiology 186
7.4.4 Infectious Diseases 187
7.4.5 Covid- 19 187
7.5 Devices 188
7.6 End- of- Life AI 189
7.7 Patient Decision Support 190
References 191
8 Population Health and Wellness 195
8.1 Nutrition 196
8.2 Fitness 200
8.3 Stress and Sleep 201
8.4 Population Health and Management 204
8.5 Risk Assessment 206
8.6 Use of Real World Data 208
8.7 Medication Adherence 208
8.8 Remote Engagement and Automation 209
8.9 Sdoh 211
8.10 Aging in Place 212
References 214
9 Clinical Workflows 217
9.1 Documentation Assistants 218
9.2 Quality Measurement 225
9.3 Nursing and Clinical Assistants 225
9.4 Virtual Assistants 227
References 230
10 Administration and Operations 233
10.1 Providers 234
10.1.1 Documentation, Coding, and Billing 234
10.1.2 Practice Management and Operations 238
10.1.3 Hospital Operations 240
10.2 Payers 243
10.2.1 Payer Administrative Functions 244
10.2.2 Fraud 246
10.2.3 Personalized Communications 247
References 248
11 AI Applications in Life Sciences 251
11.1 Drug Discovery 252
11.2 Clinical Trials 261
11.2.1 Information Engines 264
11.2.2 Patient Stratification 267
11.2.3 Clinical Trial Operations 268
11.3 Medical Affairs and Commercial 271
References 272
Part III the Business Case for Ai in Healthcare 275
12 Which Health AI Applications Are Ready for Their Moment? 277
12.1 Methodology 278
12.2 Clinical Care 281
12.3 Administrative and Operations 289
12.4 Life Sciences 291
References 293
13 The Business Model for Buyers of Health AI Solutions 295
13.1 Clinical Care 298
13.2 Administrative and Operations 305
13.3 Life Sciences 309
13.4 Guide for Buyer Assessment of Health AI Solutions 312
References 313
14 How to Build and Invest in the Best Health AI Companies 315
14.1 Barriers to Entry and Intellectual Property (IP) 316
14.1.1 Creating Defensible Products 318
14.2 Startups Versus Large Companies 319
14.3 Sales and Marketing 321
14.4 Initial Customers 324
14.5 Direct- to- Consumer (D2C) 325
14.6 Planning Your Entrepreneurial Health AI Journey 327
14.7 Assessment of Companies by Investors 329
14.7.1 Key Areas to Explore for a Health AI Company for Investment 329
References 330
Index 333
