Applications and challenges of artificial intelligence in diagnostic and interventional radiology

2022
journal article
review article
dc.abstract.enPurpose: Machine learning (ML) and deep learning (DL) can be utilized in radiology to help diagnosis and for predicting management and outcomes based on certain image findings. DL utilizes convolutional neural networks (CNN) and may be used to classify imaging features. The objective of this literature review is to summarize recent publications highlighting the key ways in which ML and DL may be applied in radiology, along with solutions to the problems that this implementation may face. Material and methods: Twenty-one publications were selected from the primary literature through a PubMed search. The articles included in our review studied a range of applications of artificial intelligence in radiology. Results: The implementation of artificial intelligence in diagnostic and interventional radiology may improve image analysis, aid in diagnosis, as well as suggest appropriate interventions, clinical predictive modelling, and trainee education. Potential challenges include ethical concerns and the need for appropriate datasets with accurate labels and large sample sizes to train from. Additionally, the training data should be representative of the population to which the future ML platform will be applicable. Finally, machines do not disclose a statistical rationale when expounding on the task purpose, making them difficult to apply in medical imaging. Conclusions: As radiologists report increased workload, utilization of artificial intelligence may provide improved outcomes in medical imaging by assisting, rather than guiding or replacing, radiologists.pl
dc.contributor.authorWaller, Josephpl
dc.contributor.authorO'Connor, Aislingpl
dc.contributor.authorRafaat, Eleezapl
dc.contributor.authorAmireh, Ahmadpl
dc.contributor.authorDempsey, Johnpl
dc.contributor.authorMartin, Clarissapl
dc.contributor.authorUmair, Muhammadpl
dc.date.accessioned2022-03-29T08:25:52Z
dc.date.available2022-03-29T08:25:52Z
dc.date.issued2022pl
dc.date.openaccess0
dc.description.accesstimew momencie opublikowania
dc.description.additionalBibliogr. s. e117pl
dc.description.physicale113-e117pl
dc.description.versionostateczna wersja wydawcy
dc.description.volume87pl
dc.identifier.doi10.5114/pjr.2022.113531pl
dc.identifier.eissn1899-0967pl
dc.identifier.issn1733-134Xpl
dc.identifier.urihttps://ruj.uj.edu.pl/xmlui/handle/item/289567
dc.languageengpl
dc.language.containerengpl
dc.rightsUdzielam licencji. Uznanie autorstwa - Użycie niekomercyjne - Bez utworów zależnych 4.0 Międzynarodowa*
dc.rights.licenceCC-BY-NC-ND
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.pl*
dc.share.typeotwarte czasopismo
dc.subject.enartificial intelligencepl
dc.subject.enmachine learningpl
dc.subject.enradiologypl
dc.subject.enimagingpl
dc.subtypeReviewArticlepl
dc.titleApplications and challenges of artificial intelligence in diagnostic and interventional radiologypl
dc.title.journalPolish Journal of Radiologypl
dc.typeJournalArticlepl
dspace.entity.typePublication
dc.abstract.enpl
Purpose: Machine learning (ML) and deep learning (DL) can be utilized in radiology to help diagnosis and for predicting management and outcomes based on certain image findings. DL utilizes convolutional neural networks (CNN) and may be used to classify imaging features. The objective of this literature review is to summarize recent publications highlighting the key ways in which ML and DL may be applied in radiology, along with solutions to the problems that this implementation may face. Material and methods: Twenty-one publications were selected from the primary literature through a PubMed search. The articles included in our review studied a range of applications of artificial intelligence in radiology. Results: The implementation of artificial intelligence in diagnostic and interventional radiology may improve image analysis, aid in diagnosis, as well as suggest appropriate interventions, clinical predictive modelling, and trainee education. Potential challenges include ethical concerns and the need for appropriate datasets with accurate labels and large sample sizes to train from. Additionally, the training data should be representative of the population to which the future ML platform will be applicable. Finally, machines do not disclose a statistical rationale when expounding on the task purpose, making them difficult to apply in medical imaging. Conclusions: As radiologists report increased workload, utilization of artificial intelligence may provide improved outcomes in medical imaging by assisting, rather than guiding or replacing, radiologists.
dc.contributor.authorpl
Waller, Joseph
dc.contributor.authorpl
O'Connor, Aisling
dc.contributor.authorpl
Rafaat, Eleeza
dc.contributor.authorpl
Amireh, Ahmad
dc.contributor.authorpl
Dempsey, John
dc.contributor.authorpl
Martin, Clarissa
dc.contributor.authorpl
Umair, Muhammad
dc.date.accessioned
2022-03-29T08:25:52Z
dc.date.available
2022-03-29T08:25:52Z
dc.date.issuedpl
2022
dc.date.openaccess
0
dc.description.accesstime
w momencie opublikowania
dc.description.additionalpl
Bibliogr. s. e117
dc.description.physicalpl
e113-e117
dc.description.version
ostateczna wersja wydawcy
dc.description.volumepl
87
dc.identifier.doipl
10.5114/pjr.2022.113531
dc.identifier.eissnpl
1899-0967
dc.identifier.issnpl
1733-134X
dc.identifier.uri
https://ruj.uj.edu.pl/xmlui/handle/item/289567
dc.languagepl
eng
dc.language.containerpl
eng
dc.rights*
Udzielam licencji. Uznanie autorstwa - Użycie niekomercyjne - Bez utworów zależnych 4.0 Międzynarodowa
dc.rights.licence
CC-BY-NC-ND
dc.rights.uri*
http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.pl
dc.share.type
otwarte czasopismo
dc.subject.enpl
artificial intelligence
dc.subject.enpl
machine learning
dc.subject.enpl
radiology
dc.subject.enpl
imaging
dc.subtypepl
ReviewArticle
dc.titlepl
Applications and challenges of artificial intelligence in diagnostic and interventional radiology
dc.title.journalpl
Polish Journal of Radiology
dc.typepl
JournalArticle
dspace.entity.type
Publication
Affiliations

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