| Description |
1 online resource (371 p.) |
| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
| 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 |
| Notes |
Description based upon print version of record |
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8.7 Medication Adherence |
| Subject |
Artificial intelligence -- Medical applications.
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| Form |
Electronic book
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| ISBN |
9781394240180 |
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139424018X |
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1394240171 |
|
9781394240173 |
|
1394240198 |
|
9781394240197 |
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