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journal.pdig.0000001.pdf | 1.01 MB | Adobe PDF | 見る/開く |
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 Takeshita, Kohei Akiyama, Masato Kawasaki, Ryo Kashiwagi, Kenji Sakamoto, Taiji Oshika, Tetsuro Tsujikawa, Akitaka |
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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