Artificial Intelligence in Medical Imaging Opportunities, Applications and Risks /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Ranschaert, Erik R. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Morozov, Sergey (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Algra, Paul R. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Θέματα: Radiology.; Computers.; Health informatics.; Imaging / Radiology.; Information Systems and Communication Service.; Health Informatics.

 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. 

 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.