Main description:

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.

Contents:

PART 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