Artificial intelligence in radiology

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forw...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Nature reviews. Cancer Jg. 18; H. 8; S. 500 - 510
Hauptverfasser: Hosny, Ahmed, Parmar, Chintan, Quackenbush, John, Schwartz, Lawrence H, Aerts, Hugo J W L
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Nature Publishing Group 01.08.2018
Schlagworte:
Algorithms
Artificial Intelligence
Deep Learning
Humans
Image processing
Image Processing, Computer-Assisted
Machine Learning
Neoplasms - diagnostic imaging
Neural networks
Pattern Recognition, Automated
Radiology
ISSN:1474-175X, 1474-1768, 1474-1768
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Literature Review-3
ObjectType-Review-3
content type line 23
ISSN:1474-175X
1474-1768
1474-1768
DOI:10.1038/s41568-018-0016-5