Description
Book SynopsisThis 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.
Trade Review“The book seems practical and interesting for newcomers to the feld and also experts. This book covers a range of introductory to advanced issues of AI and can respond well to the concerns of researchers. The presented examples … prepare the ground for familiarity with the research process and future research trends in this feld. Based on the reviews, we can recommend this book to researchers as a desirable book as a gateway to enter this feld.” (Shahabedin Nabavi and Mohammad Mohammadi, Physical and Engineering Sciences in Medicine, Vol. 44, 2021)
Table of ContentsPART I: INTRODUCTION
1 Introduction: Game changers in radiology
PART II: TECHNIQUES
2 The role of medical imaging computing, informatics and machine learning in healthcare
2 History and evolution of A.I. in medical imaging
3 Deep Learning and Neural Networks in imaging: basic principles
PART III DEVELOPMENT of AI APPLICATIONS
4 Imaging biomarkers
5 How to develop A.I. applications
6 Validation of A.I. applications
PART IV: BIG DATA IN RADIOLOGY
7 The value of enterprise imaging
8 Data mining in radiology
9 Image biobanks
10 The quest for medical images and data
11 Clearance of medical images and data
12 Legal and ethical issues in AI
PART V: CLINICAL USE OF A.I. IN RADIOLOGY
13 Pulmonary diseases
14 Cardiac diseases
15 Breast cancer
16 Neurological diseases
PART VI: IMPACT of A.I. on RADIOLOGY
17 Applications of A.I. beyond image analysis
18 Value of structured reporting for A.I.
19 The role of A.I. for clinical trials
20 Market and economy of A.I.: evolution
21 The role of an A.I. ecosystem for radiology
22 Advantages and risks of A.I. for radiologists
23 Re-thinking radiology
