Big data, artificial intelligence, and structured reporting

Abstract The past few years have seen a considerable rise in interest towards artificial intelligence and machine learning applications in radiology. However, in order for such systems to perform adequately, large amounts of training data are required. These data should ideally be standardised and o...

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Bibliographic Details
Main Authors: Daniel Pinto dos Santos, Bettina Baeßler
Format: Article
Language:English
Published: SpringerOpen 2018-12-01
Series:European Radiology Experimental
Subjects:
Online Access:http://link.springer.com/article/10.1186/s41747-018-0071-4
Description
Summary:Abstract The past few years have seen a considerable rise in interest towards artificial intelligence and machine learning applications in radiology. However, in order for such systems to perform adequately, large amounts of training data are required. These data should ideally be standardised and of adequate quality to allow for further usage in training of artificial intelligence algorithms. Unfortunately, in many current clinical and radiological information technology ecosystems, access to relevant pieces of information is difficult. This is mostly because a significant portion of information is handled as a collection of narrative texts and interoperability is still lacking. This review aims at giving a brief overview on how structured reporting can help to facilitate research in artificial intelligence and the context of big data.
ISSN:2509-9280