Artificial Intelligence in Neuroradiology: A Practical Guide - Couverture rigide

Couverture rigide

ISBN 10 : 3032245672 ISBN 13 : 9783032245670

Editeur : Springer, 2026

Synopsis

This book offers an approachable guide to the use of artificial intelligence in neuroradiology. Artificial intelligence is evolving rapidly and has begun to have an impact on how neuroradiology is conducted. Thus, being familiar with artificial intelligence and its utility is essential for the modern neuroradiologist.

This text includes an overview of artificial intelligence in neuroradiology, including how it's conducted and how it's applied in clinical practice. In particular, machine learning and deep learning algorithms are reviewed as pertains to neuroimaging applications, such as optimizing workflow, quality assurance, including noise reduction and reduction in scan time, image segmentation and volumetric measurements, diagnosis, and treatment response prediction. This is based on the current research literature with a consideration for future directions. Finally, ethical and legal issues related to AI for medical imaging are discussed, as well as regulatory and HIPAA compliance issues. Illustrative examples are included throughout.

This is an ideal guide for neuroradiologists and neurologists.

A propos du livre

À propos de l'auteur

Daniel Thomas Ginat, MD is an Associate Professor of Radiology at the University of Chicago. He has edited several volumes for Springer, including Neuroimaging Pharmacopoeia, Second Edition.

À propos de la quatrième de couverture

This book offers an approachable guide to the use of artificial intelligence in neuroradiology. Artificial intelligence is evolving rapidly and has begun to have an impact on how neuroradiology is conducted. Thus, being familiar with artificial intelligence and its utility is essential for the modern neuroradiologist.

This text includes an overview of artificial intelligence in neuroradiology, including how it's conducted and how it's applied in clinical practice. In particular, machine learning and deep learning algorithms are reviewed as pertains to neuroimaging applications, such as optimizing workflow, quality assurance, including noise reduction and reduction in scan time, image segmentation and volumetric measurements, diagnosis, and treatment response prediction. This is based on the current research literature with a consideration for future directions. Finally, ethical and legal issues related to AI for medical imaging are discussed, as well as regulatory and HIPAA compliance issues. Illustrative examples are included throughout.

This is an ideal guide for neuroradiologists and neurologists.

Détails de l'édition

• Éditeur: Springer
• Date d'édition: 2026
• Langue: anglais
• ISBN 10: 3032245672
• ISBN 13: 9783032245670
• Reliure: Hardcover
• Nombre de pages: 308
• Éditeur: Ginat Daniel Thomas
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible