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.
1. Cover image - p. c 2. Title page - p. iii 3. Copyright - p. iv 4. Contributors - p. v 5. Preface - p. viii 6. Acknowledgments - p. ix 7. Table of Contents - p. x 8. PART I Background - p. 1 9. Chapter 1 AI in Radiology—Past and Present - p. 3 10. Chapter 2 AI in Radiology—Future - p. 9 11. Chapter 3 Technical Principles - p. 19 12. PART II Interpretive Applications - p. 29 13. Chapter 4 Interpretive Applications of Artificial Intelligence in Breast Radiology - p. 31 14. Chapter 5 Artificial Intelligence in Cardiovascular Imaging - p. 45 15. Chapter 6 Interpretive Applications: Chest - p. 51 16. Chapter 7 ArtificiaI Intelligence in Emergency Radiology - p. 59 17. Chapter 8 Artificial Intelligence in Gastrointestinal Imaging - p. 63 18. Chapter 9 Genitourinary - p. 71 19. Chapter 10 ArtificiaI Intelligence In Head and Neck Radiology: Current Innovations, Challenges, and Future Directions - p. 77 20. Chapter 11 Interpretive Applications: Musculoskeletal - p. 85 21. Chapter 12 Neuroradiology - p. 93 22. Chapter 13 Interpretive Applications of Artificial Intelligence in Interventional Radiology - p. 101 23. Chapter 14 Artificial Intelligence in Nuclear Radiology: Unlocking the Potential for Enhanced Patient - p. 111 24. PART III Noninterpretive Applications - p. 125 25. Chapter 15 Patient Facing Noninterpretive Artificial Intelligence Applications - p. 127 26. Chapter 16 Navigating the Radiologic Technologist’s Landscape: Current Innovations and Future Directions of Artificial Intelligence in Radiology - p. 133 27. Chapter 17 Business-Facing Approaches - p. 145 28. Chapter 18 Non-interpretive Application of Artificial Intelligence in Radiology: Population Health - p. 153 29. PART IV Develop Your Application - p. 161 30. Chapter 19 Data Curation - p. 163 31. Chapter 20 Artificial Intelligence Network Training and Validation in Radiology: Recent Developments and Real-World Examples - p. 187 32. Chapter 21 Regulatory Considerations for Radiology Artificial Intelligence/Machine Learning Devices - p. 197 33. PART V Case Studies - p. 209 34. Chapter 22 Response to COVID With Artificial Intelligence–Assisted Radiologic Diagnosis - p. 211 35. Chapter 23 Arterys Artificial Intelligence: Inception, Development, Growth - p. 219 36. Chapter 24 Viz.ai—Pioneering Artificial Intelligence in Healthcare - p. 227 37. Index - p. 241
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.
1. Cover image - p. c 2. Title page - p. iii 3. Copyright - p. iv 4. Contributors - p. v 5. Preface - p. viii 6. Acknowledgments - p. ix 7. Table of Contents - p. x 8. PART I Background - p. 1 9. Chapter 1 AI in Radiology—Past and Present - p. 3 10. Chapter 2 AI in Radiology—Future - p. 9 11. Chapter 3 Technical Principles - p. 19 12. PART II Interpretive Applications - p. 29 13. Chapter 4 Interpretive Applications of Artificial Intelligence in Breast Radiology - p. 31 14. Chapter 5 Artificial Intelligence in Cardiovascular Imaging - p. 45 15. Chapter 6 Interpretive Applications: Chest - p. 51 16. Chapter 7 ArtificiaI Intelligence in Emergency Radiology - p. 59 17. Chapter 8 Artificial Intelligence in Gastrointestinal Imaging - p. 63 18. Chapter 9 Genitourinary - p. 71 19. Chapter 10 ArtificiaI Intelligence In Head and Neck Radiology: Current Innovations, Challenges, and Future Directions - p. 77 20. Chapter 11 Interpretive Applications: Musculoskeletal - p. 85 21. Chapter 12 Neuroradiology - p. 93 22. Chapter 13 Interpretive Applications of Artificial Intelligence in Interventional Radiology - p. 101 23. Chapter 14 Artificial Intelligence in Nuclear Radiology: Unlocking the Potential for Enhanced Patient - p. 111 24. PART III Noninterpretive Applications - p. 125 25. Chapter 15 Patient Facing Noninterpretive Artificial Intelligence Applications - p. 127 26. Chapter 16 Navigating the Radiologic Technologist’s Landscape: Current Innovations and Future Directions of Artificial Intelligence in Radiology - p. 133 27. Chapter 17 Business-Facing Approaches - p. 145 28. Chapter 18 Non-interpretive Application of Artificial Intelligence in Radiology: Population Health - p. 153 29. PART IV Develop Your Application - p. 161 30. Chapter 19 Data Curation - p. 163 31. Chapter 20 Artificial Intelligence Network Training and Validation in Radiology: Recent Developments and Real-World Examples - p. 187 32. Chapter 21 Regulatory Considerations for Radiology Artificial Intelligence/Machine Learning Devices - p. 197 33. PART V Case Studies - p. 209 34. Chapter 22 Response to COVID With Artificial Intelligence–Assisted Radiologic Diagnosis - p. 211 35. Chapter 23 Arterys Artificial Intelligence: Inception, Development, Growth - p. 219 36. Chapter 24 Viz.ai—Pioneering Artificial Intelligence in Healthcare - p. 227 37. Index - p. 241
