AI Doctor - The Rise of Artificial Intelligence in Healthcare - A Guide for Users, Buyers, Builders, and Investors (Feb 6, 2024)_(1394240163)_(Wiley)

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