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Artificial Intelligence in Medical Imaging

ARTIFICIAL INTELLIGENCE IN MED IMAGING: OPPS APPS RISKS

Medical Imaging

Health, Medicine & Nursing

Gt Product Group

Binding Format

Marketing Imprint Name

B2B GT

CL Carton Count

Imprint Name

B2B & B2C

<P>This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals. It provides a thorough overview of the impact of artificial intelligence (AI) on medical imaging; includes contributions from radiologists and IT professionals, ensuring a multidisciplinary approach; and makes practical recommendations for the use of AI technology for both clinical and nonclinical applications.</P>

COLFR

COLSR

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
I&#x2019;ve Seen the Future &#x2026;.
Preface.
Contents.
Introduction.
1: Introduction: Game Changers in Radiology.
2: Technology: Getting Started.
3: The Role of Medical Image Computing and Machine Learning in Healthcare.
4: A Deeper Understanding of Deep Learning.
5: Deep Learning and Machine Learning in Imaging: Basic Principles.
6: Technology: Developing A.I. Applications.
7: How to Develop Artificial Intelligence Applications.
8: A Standardised Approach for Preparing Imaging Data for Machine Learning Tasks in Radiology.
9: The Value of Structured Reporting for AI.
10: Artificial Intelligence in Medicine: Validation and Study Design.
11: Big Data in Medicine.
12: Enterprise Imaging.
13: Imaging Biomarkers and Imaging Biobanks.
14: Practical Use Cases of A.I. in Radiology.
15: Applications of AI Beyond Image Interpretation.
16: Artificial Intelligence and Computer-Assisted Evaluation of Chest Pathology.
17: Cardiovascular Diseases.
18: Deep Learning in Breast Cancer Screening.
19: Neurological Diseases.
20: The Role of AI in Clinical Trials.
21: Quality, Regulatory and Ethical Issues.
22: Quality and Curation of Medical Images and Data.
23: Does Future Society Need Legal Personhood for Robots and AI?.
24: The Role of an Artificial Intelligence Ecosystem in Radiology.
25: Advantages, Challenges, and Risks of Artificial Intelligence for Radiologists.
AI: A Glossary of Terms.
Glossary.
Index.

Artificial Intelligence in Medical Imaging.css-1p8rvzy{position:absolute;padding-left:8px;}

Artificial Intelligence in Medical Imaging