Artificial Intelligence in Medical Imaging: Opportunities, Applications and Risks

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.

Table of contents:

Part I. Introduction

1. Introduction: Game Changers in Radiology

Part II. Technology: Getting Started

2. The Role of Medical Image Computing and Machine Learning in Healthcare

3. A Deeper Understanding of Deep Learning

4. Deep Learning and Machine Learning in Imaging: Basic Principles

Part III. Technology: Developing A.I. Applications

5. How to Develop Artificial Intelligence Applications

6. A Standardised Approach for Preparing Imaging Data for Machine Learning Tasks in Radiology

7. The Value of Structured Reporting for AI

8. Artificial Intelligence in Medicine: Validation and Study Design

Part IV. Big Data in Medicine

9. Enterprise Imaging

10. Imaging Biomarkers and Imaging Biobanks

Part V. Practical Use Cases of A.I. in Radiology

11. Applications of AI Beyond Image Interpretation

12. Artificial Intelligence and Computer-Assisted Evaluation of Chest Pathology

13. Cardiovascular Diseases

14. Deep Learning in Breast Cancer Screening

15. Neurological Diseases

16. The Role of AI in Clinical Trials

Part VI. Quality, Regulatory and Ethical Issues

17. Quality and Curation of Medical Images and Data

18. Does Future Society Need Legal Personhood for Robots and AI?

19. The Role of an Artificial Intelligence Ecosystem in Radiology

20. Advantages, Challenges, and Risks of Artificial Intelligence for Radiologists