Description
Book Synopsis
This book takes an in-depth look at the emerging technologies that are transforming the way clinicians manage patients, while at the same time emphasizing that the best practitioners use both artificial and human intelligence to make decisions.
AI and machine learning are explored at length, with plain clinical English explanations of convolutional neural networks, back propagation, and digital image analysis. Real-world examples of how these tools are being employed are also discussed, including their value in diagnosing diabetic retinopathy, melanoma, breast cancer, cancer metastasis, and colorectal cancer, as well as in managing severe sepsis.
With all the enthusiasm about AI and machine learning, it was also necessary to outline some of criticisms, obstacles, and limitations of these new tools. Among the criticisms discussed: the relative lack of hard scientific evidence supporting some of the latest algorithms and the so-called black box problem. A ch
Table of Contents
Dedication
Contents
Preface
About the Authors
Chapter 1: Clinical Reasoning and Diagnostic Errors
Chapter 2: The Promise of Artificial Intelligence and Machine Learning
Chapter 3: AI Criticisms, Obstacles, and Limitations
Chapter 4: CDS Systems: Past, Present, and Future
Chapter 5: Reengineering Data Analytics
Chapter 6: Will Systems Biology Transform Clinical Decision Support?
Chapter 7: Precision Medicine
Chapter 8: Reinventing Clinical Decision Support: Case Studies
Index
