Titlebook: Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging; Patrick Veit-Haibach,Ken Herrmann Book 2022 The Editor(s)

Between

Single Transistor Configurations,hine learning in particular, within the field of healthcare. We argue that, going forward, the deliberation and further development of ethics of AI and machine learning should be grounded more strongly in the field of data ethics than it is the case today. This is because of the specific nature of t

遺忘

Frequency Compensation Techniques,vice organization, improve image quality while reducing patient exposure, and dramatically improve the amount and quality of diagnostic information in our studies. In this chapter, we adopt the point of view of the nuclear medicine physician. We discuss the biggest and most predictable benefits of A

languid

丑惡

Dissonance

Legal and Ethical Aspects of Machine Learning: Who Owns the Data?he digital data that enable machine learning and artificial intelligence. We then turn to the question of ownership, discussing what ownership means, and can mean, in the context of digital data, and who can legitimately own digital data used in and for imaging.

假

Implementing Digital Real-Time Servos,haracteristics of the development process and validation to finally detail how the process can be applied in hybrid modalities where it is highly relevant to combine the spatial information with the functional one.

spondylosis

notion

叢林

Talkative

Introduction to Feedback Control,ing and to predict clinical prognosis. Like with other advance statistical methods, the accuracy and generalizability of AI/DL methods is enhanced using large and heterogenous datasets to develop robust AI/DL models and applications that can transform the field of healthcare, hybrid and molecular imaging.