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Continuous learning AI in radiology: implementation principles and early applications

Continuous learning artificial intelligence algorithms learn and retrain continually, making them less prone to error and bias.

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Bibliographic Details
Main Authors: Pianykh, Oleg (Author) , Langs, Georg (Author) , Dewey, Marc (Author) , Enzmann, Dieter R. (Author) , Herold, Christian J. (Author) , Schönberg, Stefan (Author) , Brink, James A. (Author)
Format: Article (Journal)
Language:English
Published: October 2020
In: Radiology
Year: 2020, Volume: 297, Issue: 1, Pages: 6-14
ISSN:1527-1315
DOI:10.1148/radiol.2020200038
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1148/radiol.2020200038
Verlag, lizenzpflichtig, Volltext: https://pubs.rsna.org/doi/10.1148/radiol.2020200038
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Author Notes:Oleg S. Pianykh, Georg Langs, Marc Dewey, Dieter R. Enzmann, Christian J. Herold, Stefan O. Schoenberg, James A. Brink
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Summary:Continuous learning artificial intelligence algorithms learn and retrain continually, making them less prone to error and bias.
Item Description:Online publiziert: 25. August 2020
Gesehen am 06.08.2025
Physical Description:Online Resource
ISSN:1527-1315
DOI:10.1148/radiol.2020200038

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