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Artificial intelligence has the potential to transform many areas of medicine and is already a growing factor in the field of radiology.
The Radiology AI Handbook offers the
current, authoritative information you need in order to better understand AI and how to incorporate it into your daily practice. Written by clinical and computer science experts in AI, this book provides a comprehensive overview of the
fundamental concepts, technology, research/development/validation, and regulatory considerations for current and emerging radiology AI applications in each subspecialty.
- 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.
List of contents
PART I Background1. AI in Radiology-Past and Present
2. AI in Radiology-Future
3. Technical Principles
PART II Interpretive Applications4. 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 Applications15. 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 Application19. 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 Studies22. Response to COVID With Artificial Intelligence-Assisted Radiologic Diagnosis
23. Arterys Artificial Intelligence: Inception, Development, Growth
24. Viz.ai-Pioneering Artificial Intelligence in Healthcare
About the author
Dr Adam E. M. Eltorai, MD, PhD completed his graduate studies in Biomedical Engineering and Biotechnology along with his medical degree from Brown University. His work has spanned the translational spectrum with a focus on medical technology innovation and development. Dr. Eltorai has published numerous articles and books.
