The Radiology AI Handbook

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.Dr. Rajat Chand is a board-certified adult and pediatric interventional radiologist.

"This slim 256-page book... authoritatively provid[es] the key current information needed to understand the role and application of artificial intelligence (AI) and machine learning in radiology.... Written by both clinical and data science experts with the aim of providing a comprehensive overview for application in clinical practice.... [the] contents are general and related to fundamental concepts, including technology and regulatory considerations, using a teaching path that also takes into account research, development and validation.... [T]he book discusses both interpretive and non-interpretive applications, and includes multiple case studies throughout.... [T]his publication may represent a good introduction to AI in radiology either for clinical radiologists and for students and trainees in the discipline, also having the ability to arouse the interest of other professionals interested in the subject." Review by Luigi Mansi, European Journal of Nuclear Medicine and Molecular Imaging, June 2026

• PART I Background1. AI in Radiology-Past and Present2. AI in Radiology-Future3. Technical PrinciplesPART II Interpretive Applications4. Interpretive Applications of Artificial Intelligence in Breast Radiology5. Artificial Intelligence in Cardiovascular Imaging6. Interpretive Applications: Chest7. Artificial Intelligence in Emergency Radiology8. Artificial Intelligence in Gastrointestinal Imaging9. Genitourinary10. ArtificiaI Intelligence in Head and Neck Radiology: Current Innovations, Challenges, and Future Directions11. Interpretive Applications: Musculoskeletal12. Neuroradiology13. Interpretive Applications of Artificial Intelligence in Interventional Radiology14. Artificial Intelligence in Nuclear Radiology: Unlocking the Potential for Enhanced PatientPART III Noninterpretive Applications15. Patient Facing Noninterpretive Artificial Intelligence Applications16. Navigating the Radiologic Technologist’s Landscape: Current Innovations and Future Directions of Artificial Intelligence in Radiology17. Business-Facing Approaches18. Noninterpretive Application of Artificial Intelligence in Radiology:Population HealthPART IV Develop Your Application19. Data Curation20. Artificial Intelligence Network Training and Validation in Radiology: Recent Developments and Real-World Examples21. Regulatory Considerations for Radiology Artificial Intelligence/Machine Learning DevicesPART V Case Studies22. Response to COVID With Artificial Intelligence-Assisted Radiologic Diagnosis23. Arterys Artificial Intelligence: Inception, Development, Growth24. Viz.ai-Pioneering Artificial Intelligence in Healthcare