Ready for testing artificial intelligence in radiology clinical practice: We would do well to be in the front line leveraging their strengths but also highlighting today weaknesses.
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| Title: | Ready for testing artificial intelligence in radiology clinical practice: We would do well to be in the front line leveraging their strengths but also highlighting today weaknesses. |
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| Authors: | Bender, Benjamin1 (AUTHOR) Benjamin.bender@med.uni-tuebingen.de |
| Source: | European Radiology. Feb2024, Vol. 34 Issue 2, p808-809. 2p. |
| Subjects: | Radiology, Artificial intelligence, Intelligence tests, Artificial neural networks |
| Abstract: | The article discusses the potential use of artificial intelligence (AI) in radiology practice. It explains that deep learning, a specific strategy of machine learning, has shown promising results in tasks related to radiology. The article highlights the importance of training AI models with large, well-labeled datasets and discusses a study that compared the performance of radiologists with and without the assistance of an AI model. The study found that the AI model performed better than the average radiologist in detecting certain findings, but there were still limitations and risks associated with using AI tools. The article concludes by suggesting that AI tools could improve the quality of radiological reports and calls for further research to validate their benefits. [Extracted from the article] |
| Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: egs DbLabel: Engineering Source An: 175341113 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Ready for testing artificial intelligence in radiology clinical practice: We would do well to be in the front line leveraging their strengths but also highlighting today weaknesses. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bender%2C+Benjamin%22">Bender, Benjamin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> Benjamin.bender@med.uni-tuebingen.de</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22European+Radiology%22">European Radiology</searchLink>. Feb2024, Vol. 34 Issue 2, p808-809. 2p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Radiology%22">Radiology</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligence+tests%22">Intelligence tests</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The article discusses the potential use of artificial intelligence (AI) in radiology practice. It explains that deep learning, a specific strategy of machine learning, has shown promising results in tasks related to radiology. The article highlights the importance of training AI models with large, well-labeled datasets and discusses a study that compared the performance of radiologists with and without the assistance of an AI model. The study found that the AI model performed better than the average radiologist in detecting certain findings, but there were still limitations and risks associated with using AI tools. The article concludes by suggesting that AI tools could improve the quality of radiological reports and calls for further research to validate their benefits. [Extracted from the article] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=175341113 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00330-023-10240-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 2 StartPage: 808 Subjects: – SubjectFull: Radiology Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Intelligence tests Type: general – SubjectFull: Artificial neural networks Type: general Titles: – TitleFull: Ready for testing artificial intelligence in radiology clinical practice: We would do well to be in the front line leveraging their strengths but also highlighting today weaknesses. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bender, Benjamin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 09387994 Numbering: – Type: volume Value: 34 – Type: issue Value: 2 Titles: – TitleFull: European Radiology Type: main |
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