NDLI: Artificial intelligence and machine learning for medical imaging: A technology review

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Artificial intelligence and machine learning for medical imaging: A technology review

Content Provider	Scilit
Author	Barragán-Montero, Ana Javaid, Umair Valdés, Gilmer Nguyen, Dan Desbordes, Paul Macq, Benoit Willems, Siri Vandewinckele, Liesbeth Holmström, Mats Löfman, Fredrik Michiels, Steven Souris, Kevin Sterpin, Edmond Lee, John A.
Copyright Year	2021
Description	Journal: Physica Medica Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners. The aim of this review is to present the basic technological pillars of AI, together with the state-of-the-art machine learning methods and their application to medical imaging. In addition, we discuss the new trends and future research directions. This will help the reader to understand how AI methods are now becoming an ubiquitous tool in any medical image analysis workflow and pave the way for the clinical implementation of AI-based solutions.
Related Links	http://www.physicamedica.com/article/S1120179721001733/pdf
Ending Page	256
Page Count	15
Starting Page	242
ISSN	11201797
DOI	10.1016/j.ejmp.2021.04.016
Journal	Physica Medica
Volume Number	83
Language	English
Publisher	Elsevier BV
Publisher Date	2021-03-01
Access Restriction	Open
Subject Keyword	Journal: Physica Medica Medical Informatics Artificial Intelligence Medical Imaging Machine Learning Deep Learning
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Physics and Astronomy Biophysics

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