AI doctor : the rise of artificial intelligence in healthcare : a guide for users, buyers, builders, and investors

AI doctor the rise of artificial intelligence in healthcare : a guide for users, buyers, builders, and investors

"Over the last decade, Artificial intelligence (AI) has permeated every sector of the economy and is in the process of transforming every aspect of our lives. Its applications in healthcare, however, will be the most impactful of all. In fact, it's estimated that the dollar value of AI app...

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Detalles Bibliográficos
Otros Autores: Razmi, Ronald M., author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated 2024.
Edición:1st ed
Materias:
Artificial intelligence > Medical applications.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009811331706719
Tabla de Contenidos:
  • 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.

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