Cargando…
Big data, artificial intelligence, and structured reporting
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 adequat...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6279752/ https://www.ncbi.nlm.nih.gov/pubmed/30515717 http://dx.doi.org/10.1186/s41747-018-0071-4 |
_version_ | 1783378530247114752 |
---|---|
author | Pinto dos Santos, Daniel Baeßler, Bettina |
author_facet | Pinto dos Santos, Daniel Baeßler, Bettina |
author_sort | Pinto dos Santos, Daniel |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6279752 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-62797522018-12-26 Big data, artificial intelligence, and structured reporting Pinto dos Santos, Daniel Baeßler, Bettina Eur Radiol Exp Narrative Review 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. Springer International Publishing 2018-12-05 /pmc/articles/PMC6279752/ /pubmed/30515717 http://dx.doi.org/10.1186/s41747-018-0071-4 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Narrative Review Pinto dos Santos, Daniel Baeßler, Bettina Big data, artificial intelligence, and structured reporting |
title | 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_short | Big data, artificial intelligence, and structured reporting |
title_sort | big data, artificial intelligence, and structured reporting |
topic | Narrative Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6279752/ https://www.ncbi.nlm.nih.gov/pubmed/30515717 http://dx.doi.org/10.1186/s41747-018-0071-4 |
work_keys_str_mv | AT pintodossantosdaniel bigdataartificialintelligenceandstructuredreporting AT baeßlerbettina bigdataartificialintelligenceandstructuredreporting |