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.

“The book is not just about the present state of affairs. It offers a vision, exploring the future trajectories of AI in radiology, addressing challenges, controversies, and the endless possibilities on the horizon.Having witnessed Oge’s dedication and forward-thinking approach firsthand, I am confident that this book will serve as an invaluable resource. For those stepping into the realm of AI in radiology or seeking to deepen their knowledge, this book provides a holistic, scientifically rigorous, and practical guide…I wholeheartedly believe that it will stand as a cornerstone for all enthusiasts eager to delve into the world of AI in Radiology.”--Felipe Kitamura, MD, PhDDirector of Applied Innovation and AI at DasaAffiliated Professor of Radiology at Universidade Federal de São Paulo

• 1 Artificial Intelligence and Medicine: The Big Picture2 AI in Radiology: From Fear to Leadership3 Fundamentals of Machine Learning and Deep Learning4 Fundamentals of Medical Image Analysis5 Data: The Essential Ingredient in AI Solutions6 Clinical Applications of AI in Radiology7 Harnessing AI in Radiology Education and Training8 Getting Started with Deep Learning in Medical Imaging9 The Future of AI in Radiology10 Resources for Further Learning

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.

“The book is not just about the present state of affairs. It offers a vision, exploring the future trajectories of AI in radiology, addressing challenges, controversies, and the endless possibilities on the horizon.Having witnessed Oge’s dedication and forward-thinking approach firsthand, I am confident that this book will serve as an invaluable resource. For those stepping into the realm of AI in radiology or seeking to deepen their knowledge, this book provides a holistic, scientifically rigorous, and practical guide…I wholeheartedly believe that it will stand as a cornerstone for all enthusiasts eager to delve into the world of AI in Radiology.”--Felipe Kitamura, MD, PhDDirector of Applied Innovation and AI at DasaAffiliated Professor of Radiology at Universidade Federal de São Paulo

• 1 Artificial Intelligence and Medicine: The Big Picture2 AI in Radiology: From Fear to Leadership3 Fundamentals of Machine Learning and Deep Learning4 Fundamentals of Medical Image Analysis5 Data: The Essential Ingredient in AI Solutions6 Clinical Applications of AI in Radiology7 Harnessing AI in Radiology Education and Training8 Getting Started with Deep Learning in Medical Imaging9 The Future of AI in Radiology10 Resources for Further Learning