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Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging/ edited by Patrick Veit-Haibach, Ken Herrmann.
其他作者:	Herrmann, Ken.
面頁冊數:	XIV, 210 p. 72 illus., 65 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Health Informatics. -
電子資源:	https://doi.org/10.1007/978-3-031-00119-2
ISBN:	9783031001192

Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging
Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging[electronic resource] /edited by Patrick Veit-Haibach, Ken Herrmann. - 1st ed. 2022. - XIV, 210 p. 72 illus., 65 illus. in color.online resource.

This book includes detailed explanations of the underlying technologies and concepts used in Artificial Intelligence (AI) and Machine Learning (ML) in the context of nuclear medicine and hybrid imaging. A diverse team of authors, including pioneers in the field and respected experts from leading international institutions, share their insights, opinions and outlooks on this exciting topic. A wide range of clinical applications are discussed, from brain applications to body indications, as well as the applicability of AI and ML for cardio-vascular conditions. The book also considers the potential impact of theranostics. To balance the technology-heavy and disease-specific applications, it also discusses ethical / legal issues, economic realities and the human factor, the physician. Though this discussion is not based on research and outcomes, it provides important insights into the ramifications of how AI and ML could transform Nuclear Medicine and Hybrid Imaging practice. As the first work highlighting the role of these concepts specifically in this field, rather than for medical imaging in general, this book offers a valuable resource for Nuclear Medicine Physicians, Radiologists, Physicists, Medical Imaging Administrators and Nuclear Medicine Technologists alike.

ISBN: 9783031001192

Standard No.: 10.1007/978-3-031-00119-2doiSubjects--Topical Terms:

593963
Health Informatics.

LC Class. No.: R895-920

Dewey Class. No.: 616.0757

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