The Radiology AI Handbook, 1st Edition

• Offers an indispensable introduction to this emerging field, with expert coverage of how AI can best be used in radiology.
• Provides clear explanations of fundamental concepts in AI and machine learning; current and future applications of AI that may affect the practice of radiology; and how to develop commercially viable AI applications in radiology.
• Discusses both interpretive and non-interpretive applications, and includes multiple case studies throughout.
• Serves as both an introduction to AI in radiology for students, trainees, and professionals, as well as a how-to guide for getting started on identifying, developing, testing, and commercializing AI applications.
• An eBook version is included with purchase. The eBook allows you to access all of the text, figures, and references, with the ability to search, customize your content, make notes and highlights, and have content read aloud. Additional digital ancillary content may publish up to 6 weeks following the publication date.

PART I Background
1. AI in Radiology—Past and Present
2. AI in Radiology—Future
3. Technical Principles

PART II Interpretive Applications
4. Interpretive Applications of Artificial Intelligence in Breast Radiology
5. Artificial Intelligence in Cardiovascular Imaging
6. Interpretive Applications: Chest
7. Artificial Intelligence in Emergency Radiology
8. Artificial Intelligence in Gastrointestinal Imaging
9. Genitourinary
10. ArtificiaI Intelligence in Head and Neck Radiology: Current Innovations, Challenges, and Future Directions
11. Interpretive Applications: Musculoskeletal
12. Neuroradiology
13. Interpretive Applications of Artificial Intelligence in Interventional Radiology
14. Artificial Intelligence in Nuclear Radiology: Unlocking the Potential for Enhanced Patient

PART III Noninterpretive Applications
15. Patient Facing Noninterpretive Artificial Intelligence Applications
16. Navigating the Radiologic Technologist’s Landscape: Current Innovations and Future Directions of Artificial Intelligence in Radiology
17. Business-Facing Approaches
18. Noninterpretive Application of Artificial Intelligence in Radiology:
Population Health

PART IV Develop Your Application
19. Data Curation
20. Artificial Intelligence Network Training and Validation in Radiology: Recent Developments and Real-World Examples
21. Regulatory Considerations for Radiology Artificial Intelligence/Machine Learning Devices

PART V Case Studies
22. Response to COVID With Artificial Intelligence—Assisted Radiologic Diagnosis
23. Arterys Artificial Intelligence: Inception, Development, Growth
24. Viz.ai—Pioneering Artificial Intelligence in Healthcare