AI doctor : the rise of artificial intelligence in healthcare : a guide for users, buyers, builders, and investors / Ronald M. Razmi.

Format:

Book

Author/Creator:

Razmi, Ronald M., author.

Language:

English

Subjects (All):

Artificial intelligence--Medical applications.

Artificial intelligence.

Physical Description:

1 online resource (371 pages) : color illustrations

Edition:

1st ed.

Other Title:

Artificial intelligence doctor

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2024.

Summary:

"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 applications in healthcare will be larger than that across all other sectors of the economy combined. AI is revolutionizing healthcare by enabling unprecedented advancements in diagnostics, treatments, and patient care. With the ability to analyse vast amounts of medical data quickly and accurately, AI systems can assist in early disease detection, predict patient outcomes, and personalize treatment plans. AI-powered algorithms are enhancing medical imaging, allowing for more precise and efficient diagnosis of conditions like cancer and cardiovascular diseases. Additionally, AI chatbots and virtual assistants are providing accessible and immediate support for mental health concerns. By automating administrative tasks and streamlining workflows, AI is reducing healthcare costs and improving efficiency, allowing healthcare professionals to focus more on patient care. As AI continues to evolve and integrate with healthcare systems, its potential to transform the industry and improve patient outcomes is truly remarkable"-- Provided by publisher.

Contents:

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

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

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

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

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

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

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

Chapter 9 Clinical Workflows

9.1 Documentation Assistants

9.2 Quality Measurement

9.3 Nursing and Clinical Assistants

9.4 Virtual Assistants

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

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

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

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

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

Index

EULA.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

ISBN:

9781394240173

1394240171

9781394240180

139424018X

9781394240197

1394240198

OCLC:

1416876881