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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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 |
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1725103201126973440 |