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Artificial intelligence in medical i...

Ranschaert, Erik R.

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Artificial intelligence in medical imaging = opportunities, applications and risks /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial intelligence in medical imaging/ edited by Erik R. Ranschaert, Sergey Morozov, Paul R. Algra.
Reminder of title:	opportunities, applications and risks /
other author:	Ranschaert, Erik R.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	xv, 373 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	PART I: INTRODUCTION: Introduction: Game changers in radiology -- PART II: TECHNIQUES: The role of medical imaging computing, informatics and machine learning in healthcare -- History and evolution of A.I. in medical imaging -- Deep Learning and Neural Networks in imaging: basic principles -- PART III DEVELOPMENT of AI APPLICATIONS: Imaging biomarkers -- How to develop A.I. applications -- Validation of A.I. applications -- PART IV: BIG DATA IN RADIOLOGY: The value of enterprise imaging -- Data mining in radiology -- Image biobanks -- The quest for medical images and data -- Clearance of medical images and data -- Legal and ethical issues in AI -- PART V: CLINICAL USE OF A.I. IN RADIOLOGY: Pulmonary diseases -- Cardiac diseases -- Breast cancer -- Neurological diseases -- PART VI: IMPACT of A.I. on RADIOLOGY: Applications of A.I. beyond image analysis -- Value of structured reporting for A.I. -- The role of A.I. for clinical trials -- Market and economy of A.I.: evolution -- The role of an A.I. ecosystem for radiology -- Advantages and risks of A.I. for radiologists -- Re-thinking radiology.
Contained By:	Springer eBooks
Subject:	Diagnostic imaging - Data processing. -
Online resource:	https://doi.org/10.1007/978-3-319-94878-2
ISBN:	9783319948782

Artificial intelligence in medical imaging = opportunities, applications and risks /
Artificial intelligence in medical imagingopportunities, applications and risks /[electronic resource] :edited by Erik R. Ranschaert, Sergey Morozov, Paul R. Algra. - Cham :Springer International Publishing :2019. - xv, 373 p. :ill. (some col.), digital ;24 cm.

PART I: INTRODUCTION: Introduction: Game changers in radiology -- PART II: TECHNIQUES: The role of medical imaging computing, informatics and machine learning in healthcare -- History and evolution of A.I. in medical imaging -- Deep Learning and Neural Networks in imaging: basic principles -- PART III DEVELOPMENT of AI APPLICATIONS: Imaging biomarkers -- How to develop A.I. applications -- Validation of A.I. applications -- PART IV: BIG DATA IN RADIOLOGY: The value of enterprise imaging -- Data mining in radiology -- Image biobanks -- The quest for medical images and data -- Clearance of medical images and data -- Legal and ethical issues in AI -- PART V: CLINICAL USE OF A.I. IN RADIOLOGY: Pulmonary diseases -- Cardiac diseases -- Breast cancer -- Neurological diseases -- PART VI: IMPACT of A.I. on RADIOLOGY: Applications of A.I. beyond image analysis -- Value of structured reporting for A.I. -- The role of A.I. for clinical trials -- Market and economy of A.I.: evolution -- The role of an A.I. ecosystem for radiology -- Advantages and risks of A.I. for radiologists -- Re-thinking radiology.

This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.

ISBN: 9783319948782

Standard No.: 10.1007/978-3-319-94878-2doiSubjects--Topical Terms:

817500
Diagnostic imaging
--Data processing.

LC Class. No.: RC78.7.D53 / A785 2019

Dewey Class. No.: 616.0754

Artificial intelligence in medical imaging = opportunities, applications and risks /
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245 0 0 $a Artificial intelligence in medical imaging $h [electronic resource] : $b opportunities, applications and risks / $c edited by Erik R. Ranschaert, Sergey Morozov, Paul R. Algra.
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300 $a xv, 373 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a PART I: INTRODUCTION: Introduction: Game changers in radiology -- PART II: TECHNIQUES: The role of medical imaging computing, informatics and machine learning in healthcare -- History and evolution of A.I. in medical imaging -- Deep Learning and Neural Networks in imaging: basic principles -- PART III DEVELOPMENT of AI APPLICATIONS: Imaging biomarkers -- How to develop A.I. applications -- Validation of A.I. applications -- PART IV: BIG DATA IN RADIOLOGY: The value of enterprise imaging -- Data mining in radiology -- Image biobanks -- The quest for medical images and data -- Clearance of medical images and data -- Legal and ethical issues in AI -- PART V: CLINICAL USE OF A.I. IN RADIOLOGY: Pulmonary diseases -- Cardiac diseases -- Breast cancer -- Neurological diseases -- PART VI: IMPACT of A.I. on RADIOLOGY: Applications of A.I. beyond image analysis -- Value of structured reporting for A.I. -- The role of A.I. for clinical trials -- Market and economy of A.I.: evolution -- The role of an A.I. ecosystem for radiology -- Advantages and risks of A.I. for radiologists -- Re-thinking radiology.
520 $a This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
650 0 $a Diagnostic imaging $x Data processing. $3 817500
650 0 $a Artificial intelligence $x Medical applications. $3 900591
650 1 4 $a Imaging / Radiology. $3 891022
650 2 4 $a Information Systems and Communication Service. $3 891044
650 2 4 $a Health Informatics. $3 892928
700 1 $a Ranschaert, Erik R. $3 3384837
700 1 $a Morozov, Sergey. $3 3384838
700 1 $a Algra, Paul R. $3 3384839
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-3-319-94878-2
950 $a Medicine (Springer-11650)