AI for Radiology

Artificial Intelligence has revolutionised areas of medicine. This book focuses on the integral role of AI in radiology, shedding light on how this technology can enhance patient care and streamline professional workflows.

Artificial intelligence (AI) has revolutionized many areas of medicine and is increasingly being embraced. This book focuses on the integral role of AI in radiology, shedding light on how this technology can enhance patient care and streamline professional workflows.

This book reviews, explains, and contextualizes some of the most current, practical, and relevant developments in artificial intelligence and deep learning in radiology and medical image analysis. AI for Radiology presents a balanced viewpoint of the impact of AI in these fields, underscoring that AI technologies are not intended to replace radiologists but rather to augment their capabilities, freeing professionals to focus on more complex cases. This book guides readers from the basic principles of AI to their practical applications in radiology, moving from the role of data in AI to the ethical and regulatory considerations of using AI in radiology and concluding with a selection of resources for further exploration.

This book has been crafted with a diverse readership in mind. It is a valuable asset for medical professionals eager to stay up to date with AI developments, computer scientists curious about AI's clinical applications, and anyone interested in the intersection of healthcare and technology.

Autorentext

Oge Marques, PhD, is Professor of Computer Science and Engineering in the College of Engineering and Computer Science, Professor of Biomedical Science (Secondary) in the Charles E. Schmidt College of Medicine, and Professor of Information Technology (by courtesy), in the College of Business at Florida Atlantic University (Boca Raton, FL - USA).

He is the author of 12 technical books, one patent, and more than 130 refereed scientific articles on image processing, medical image analysis, computer vision, artificial intelligence, and machine learning. He is a senior member of both the Institute of Electrical and Electronics Engineers and the Association for Computing Machinery, Fellow of the National Institutes of Health AIM-AHEAD Consortium, Fellow of the Leshner Leadership Institute of the American Association for the Advancement of Science, Tau Beta Pi Eminent Engineer, and member of the honor societies of Sigma Xi, Phi Kappa Phi, and Upsilon Pi Epsilon.

Inhalt

1 Artificial Intelligence and Medicine: The Big Picture

2 AI in Radiology: From Fear to Leadership

3 Fundamentals of Machine Learning and Deep Learning

4 Fundamentals of Medical Image Analysis

5 Data: The Essential Ingredient in AI Solutions

6 Clinical Applications of AI in Radiology

7 Harnessing AI in Radiology Education and Training

8 Getting Started with Deep Learning in Medical Imaging

9 The Future of AI in Radiology

10 Resources for Further Learning