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タイトル: Regulatory-approved deep learning/machine learning-based medical devices in Japan as of 2020: A systematic review
著者: Aisu, Nao
Miyake, Masahiro  kyouindb  KAKEN_id
Takeshita, Kohei
Akiyama, Masato
Kawasaki, Ryo
Kashiwagi, Kenji
Sakamoto, Taiji
Oshika, Tetsuro
Tsujikawa, Akitaka  kyouindb  KAKEN_id
著者名の別形: 愛須, 奈央
三宅, 正裕
辻川, 明孝
キーワード: Medical devices and equipment
Japan
Computed axial tomography
Computer software
Radiology and imaging
Machine learning
Machine learning algorithms
Gastroenterology and hepatology
発行日: 18-Jan-2022
出版者: Public Library of Science (PLoS)
誌名: PLOS Digital Health
巻: 1
号: 1
論文番号: e0000001
抄録: Machine 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.
著作権等: © 2022 Aisu et al.
This 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.
URI: http://hdl.handle.net/2433/283372
DOI(出版社版): 10.1371/journal.pdig.0000001
PubMed ID: 36812514
出現コレクション:学術雑誌掲載論文等

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