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dc.contributor.authorAisu, Naoen
dc.contributor.authorMiyake, Masahiroen
dc.contributor.authorTakeshita, Koheien
dc.contributor.authorAkiyama, Masatoen
dc.contributor.authorKawasaki, Ryoen
dc.contributor.authorKashiwagi, Kenjien
dc.contributor.authorSakamoto, Taijien
dc.contributor.authorOshika, Tetsuroen
dc.contributor.authorTsujikawa, Akitakaen
dc.contributor.alternative愛須, 奈央ja
dc.contributor.alternative三宅, 正裕ja
dc.contributor.alternative辻川, 明孝ja
dc.date.accessioned2023-06-21T04:54:29Z-
dc.date.available2023-06-21T04:54:29Z-
dc.date.issued2022-01-18-
dc.identifier.urihttp://hdl.handle.net/2433/283372-
dc.description.abstractMachine learning (ML) and deep learning (DL) are changing the world and reshaping the medical field. Thus, we conducted a systematic review to determine the status of regulatory-approved ML/DL-based medical devices in Japan, a leading stakeholder in international regulatory harmonization. Information about the medical devices were obtained from the Japan Association for the Advancement of Medical Equipment search service. The usage of ML/DL methodology in the medical devices was confirmed using public announcements or by contacting the marketing authorization holders via e-mail when the public announcements were insufficient for confirmation. Among the 114, 150 medical devices found, 11 were regulatory-approved ML/DL-based Software as a Medical Device, with 6 products (54.5%) related to radiology and 5 products (45.5%) related to gastroenterology. The domestic ML/DL-based Software as a Medical Device were mostly related to health check-ups, which are common in Japan. Our review can help understanding the global overview that can foster international competitiveness and further tailored advancements.en
dc.language.isoeng-
dc.publisherPublic Library of Science (PLoS)en
dc.rights© 2022 Aisu et al.en
dc.rightsThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/-
dc.subjectMedical devices and equipmenten
dc.subjectJapanen
dc.subjectComputed axial tomographyen
dc.subjectComputer softwareen
dc.subjectRadiology and imagingen
dc.subjectMachine learningen
dc.subjectMachine learning algorithmsen
dc.subjectGastroenterology and hepatologyen
dc.titleRegulatory-approved deep learning/machine learning-based medical devices in Japan as of 2020: A systematic reviewen
dc.typejournal article-
dc.type.niitypeJournal Article-
dc.identifier.jtitlePLOS Digital Healthen
dc.identifier.volume1-
dc.identifier.issue1-
dc.relation.doi10.1371/journal.pdig.0000001-
dc.textversionpublisher-
dc.identifier.artnume0000001-
dc.identifier.pmid36812514-
dcterms.accessRightsopen access-
dc.identifier.eissn2767-3170-
出現コレクション:学術雑誌掲載論文等

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