Edited by Adam E.M. Eltorai, MD, PhD, James M. Hillis, Rajat Chand, MD, Sudhen B. Desai and Katherine P. Andriole
**Selected for 2026 Doody's Core Titles in Diagnostic Radiology**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, au
...view more
**Selected for 2026 Doody's Core Titles in Diagnostic Radiology**
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.
**Selected for 2026 Doody's Core Titles in Diagnostic Radiology**
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.
特長
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
Any additional digital ancillary content may publish up to 6 weeks following the publication date
著者情報
Edited by Adam E.M. Eltorai, MD, PhD, Harvard Medical School, Boston, MA, USA; James M. Hillis, Director of Clinical Operations Mass, General Brigham, AI Assistant Neurologist, Department of Neurology, Massachusetts General Hospital, Assistant Professor of Neurology, Harvard Medical School Boston, Massachusetts, USA; Rajat Chand, MD, Interventional Radiologist, Vascular and Interventional Radiology, Independent Contractor, Austin, Texas, USA; Sudhen B. Desai, Principal, MedTech Consultant, Interventional Radiologist, ASKD Medical, LLC, Paradise Valley, Arizona, USA and Katherine P. Andriole, Director of Academic Research and Education, Mass General Brigham AI, Associate Professor of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
Edited by Adam E.M. Eltorai, MD, PhD, Harvard Medical School, Boston, MA, USA; James M. Hillis, Director of Clinical Operations Mass, General Brigham, AI Assistant Neurologist, Department of Neurology, Massachusetts General Hospital, Assistant Professor of Neurology, Harvard Medical School Boston, Massachusetts, USA; Rajat Chand, MD, Interventional Radiologist, Vascular and Interventional Radiology, Independent Contractor, Austin, Texas, USA; Sudhen B. Desai, Principal, MedTech Consultant, Interventional Radiologist, ASKD Medical, LLC, Paradise Valley, Arizona, USA and Katherine P. Andriole, Director of Academic Research and Education, Mass General Brigham AI, Associate Professor of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
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
"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
https://shop.elsevier.jp/the-radiology-ai-handbook-e-book-9780323877619.html335781The Radiology AI Handbook - E-Bookhttps://secure-ecsd.elsevier.com/covers/80/Tango2/large/9780323877619.jpg2018320183JPYInStock/Medicine/Radiology/Product Format/E-Book/Medicine/Radiology/Product Format/E-Book/eBooks/Medicine & Surgery/Radiology/Product Format/E-Book/Medicine/Radiology/Product Format/E-Book/New Titles/New Titles/New Titles/New Titles50545925936827525987559368325255040433511959368225803540593680062823026298152629866062986621488653650545535936823514512052598335936828418269243350885936809579989258035265936799**Selected for 2026 Doody's Core Titles in Diagnostic Radiology**<BR><BR>Artificial intelligence has the potential to transform many areas of medicine and is already a growing factor in the field of radiology. <i>The Radiology AI Handbook</i> offers the <b>current, authoritative information you need</b> 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 <b>fundamental concepts, technology, research/development/validation, and regulatory considerations</b> for current and emerging radiology AI applications in each subspecialty. **Selected for 2026 Doody's Core Titles in Diagnostic Radiology**<BR><BR>Artificial intelligence has the potential to transform many areas of medicine and is already a growing factor in the field of radiology. <i>The Radiology AI Handbook</i> offers the <b>current, authoritative information you need</b> 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 <b>fundamental concepts, technology, research/development/validation, and regulatory considerations</b> for current and emerging radiology AI applications in each subspecialty.00add-to-cart97803238776192025専門医Edited by Adam E.M. Eltorai, MD, PhD, James M. Hillis, Rajat Chand, MD, Sudhen B. Desai and Katherine P. Andriole20271E-Book184w x 260h (7.25" x 10.25")Elsevier4752025/10/06IN STOCKEdited by <STRONG>Adam E.M. Eltorai</STRONG>, MD, PhD, Harvard Medical School, Boston, MA, USA; <STRONG>James M. Hillis</STRONG>, Director of Clinical Operations Mass, General Brigham, AI Assistant Neurologist, Department of Neurology, Massachusetts General Hospital, Assistant Professor of Neurology, Harvard Medical School Boston, Massachusetts, USA; <STRONG>Rajat Chand</STRONG>, MD, Interventional Radiologist, Vascular and Interventional Radiology, Independent Contractor, Austin, Texas, USA; <STRONG>Sudhen B. Desai</STRONG>, Principal, MedTech Consultant, Interventional Radiologist, ASKD Medical, LLC, Paradise Valley, Arizona, USA and <STRONG>Katherine P. Andriole</STRONG>, Director of Academic Research and Education, Mass General Brigham AI, Associate Professor of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USAE-BookE-BookS013Medicine, Radiology米国いいえYesYesいいえいいえ選択してください選択してくださいいいえいいえ選択してください
