Artificial intelligence in medical ultrasonography: driving on an unpaved road

Kim, Young H

dc.contributor.author	Kim, Young H
dc.date	2022-08-11T08:10:50.000
dc.date.accessioned	2022-08-23T17:21:30Z
dc.date.available	2022-08-23T17:21:30Z
dc.date.issued	2021-07-01
dc.date.submitted	2021-06-30
dc.identifier.citation	<p>Kim YH. Artificial intelligence in medical ultrasonography: driving on an unpaved road. Ultrasonography. 2021 Jul;40(3):313-317. doi: 10.14366/usg.21031. Epub 2021 May 10. PMID: 34053212; PMCID: PMC8217795. <a href="https://doi.org/10.14366/usg.21031">Link to article on publisher's site</a></p>
dc.identifier.issn	2288-5919 (Linking)
dc.identifier.doi	10.14366/usg.21031
dc.identifier.pmid	34053212
dc.identifier.uri	http://hdl.handle.net/20.500.14038/48535
dc.description.abstract	Recent advances in deep-learning technology have brought revolutionary changes to artificial intelligence (AI) research and application across industries, yielding major innovations such as facial recognition and self-driving cars. Medicine is no exception, and radiology, which is based on the interpretation of image data obtained through various methods-and has often been compared with computer vision using pattern analysis-is anticipated to experience a major revolution. Despite expectations for increasing research and development of AI-empowered ultrasonography, the clinical implementation of AI in medical ultrasonography faces unique obstacles. It will be necessary to standardize image acquisition, regulate operator and interpreter qualification and performance, integrate clinical information, and provide performance feedback to maximize benefits for patient care.
dc.language.iso	en_US
dc.relation	<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=34053212&dopt=Abstract">Link to Article in PubMed</a></p>
dc.rights	Copyright © 2021 Korean Society of Ultrasound in Medicine (KSUM). This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.uri	http://creativecommons.org/licenses/by-nc/4.0/
dc.subject	deep learning
dc.subject	artificial intelligence
dc.subject	AI
dc.subject	radiology
dc.subject	medical ultrasonography
dc.subject	images
dc.subject	Artificial Intelligence and Robotics
dc.subject	Radiology
dc.title	Artificial intelligence in medical ultrasonography: driving on an unpaved road
dc.type	Journal Article
dc.source.journaltitle	Ultrasonography (Seoul, Korea)
dc.source.volume	40
dc.source.issue	3
dc.identifier.legacyfulltext	https://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=1652&context=radiology_pubs&unstamped=1
dc.identifier.legacycoverpage	https://escholarship.umassmed.edu/radiology_pubs/635
dc.identifier.contextkey	23604529
refterms.dateFOA	2022-08-23T17:21:30Z
html.description.abstract	<p>Recent advances in deep-learning technology have brought revolutionary changes to artificial intelligence (AI) research and application across industries, yielding major innovations such as facial recognition and self-driving cars. Medicine is no exception, and radiology, which is based on the interpretation of image data obtained through various methods-and has often been compared with computer vision using pattern analysis-is anticipated to experience a major revolution. Despite expectations for increasing research and development of AI-empowered ultrasonography, the clinical implementation of AI in medical ultrasonography faces unique obstacles. It will be necessary to standardize image acquisition, regulate operator and interpreter qualification and performance, integrate clinical information, and provide performance feedback to maximize benefits for patient care.</p>
dc.identifier.submissionpath	radiology_pubs/635
dc.contributor.department	Department of Radiology
dc.source.pages	313-317

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Copyright © 2021 Korean Society of Ultrasound in Medicine (KSUM). This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Except where otherwise noted, this item's license is described as Copyright © 2021 Korean Society of Ultrasound in Medicine (KSUM). This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.