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...

Full description

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
id doaj-eb4372e284584ccfa7cbc97aea35eb73
record_format Article
spelling doaj-eb4372e284584ccfa7cbc97aea35eb732020-11-25T01:27:48ZengSpringerOpenEuropean Radiology Experimental2509-92802018-12-01211510.1186/s41747-018-0071-4Big data, artificial intelligence, and structured reportingDaniel Pinto dos Santos0Bettina Baeßler1Department of Radiology, University Hospital of CologneDepartment of Radiology, University Hospital of CologneAbstract 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.http://link.springer.com/article/10.1186/s41747-018-0071-4Artificial intelligenceInformation technologyMachine learningRadiology
collection DOAJ
language English
format Article
sources DOAJ
author Daniel Pinto dos Santos
Bettina Baeßler
spellingShingle Daniel Pinto dos Santos
Bettina Baeßler
Big data, artificial intelligence, and structured reporting
European Radiology Experimental
Artificial intelligence
Information technology
Machine learning
Radiology
author_facet Daniel Pinto dos Santos
Bettina Baeßler
author_sort Daniel Pinto dos Santos
title Big data, artificial intelligence, and structured reporting
title_short Big data, artificial intelligence, and structured reporting
title_full Big data, artificial intelligence, and structured reporting
title_fullStr Big data, artificial intelligence, and structured reporting
title_full_unstemmed Big data, artificial intelligence, and structured reporting
title_sort big data, artificial intelligence, and structured reporting
publisher SpringerOpen
series European Radiology Experimental
issn 2509-9280
publishDate 2018-12-01
description 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.
topic Artificial intelligence
Information technology
Machine learning
Radiology
url http://link.springer.com/article/10.1186/s41747-018-0071-4
work_keys_str_mv AT danielpintodossantos bigdataartificialintelligenceandstructuredreporting
AT bettinabaeßler bigdataartificialintelligenceandstructuredreporting
_version_ 1725103201126973440