{"title":"理解需要考虑人工智能在眼科领域的应用。","authors":"Hitoshi Tabuchi","doi":"10.4103/2211-5056.356685","DOIUrl":null,"url":null,"abstract":"Machine learning operations (MLOps)[2] are becoming increasingly important and efforts should be made in this area. From an MLOps perspective, it is essential to have a mechanism to maintain and perpetuate the performance of artificial intelligence after its implementation in society. For example, for the intraocular lens check AI that the author and his team are developing,[3] providers must continuously replace the training data from obsolete intraocular lenses with new data.","PeriodicalId":44978,"journal":{"name":"Taiwan Journal of Ophthalmology","volume":"13 2","pages":"257-258"},"PeriodicalIF":1.0000,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/48/99/TJO-13-257.PMC10361427.pdf","citationCount":"0","resultStr":"{\"title\":\"Understanding required to consider artificial intelligence applications to the field of ophthalmology.\",\"authors\":\"Hitoshi Tabuchi\",\"doi\":\"10.4103/2211-5056.356685\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Machine learning operations (MLOps)[2] are becoming increasingly important and efforts should be made in this area. From an MLOps perspective, it is essential to have a mechanism to maintain and perpetuate the performance of artificial intelligence after its implementation in society. For example, for the intraocular lens check AI that the author and his team are developing,[3] providers must continuously replace the training data from obsolete intraocular lenses with new data.\",\"PeriodicalId\":44978,\"journal\":{\"name\":\"Taiwan Journal of Ophthalmology\",\"volume\":\"13 2\",\"pages\":\"257-258\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2023-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/48/99/TJO-13-257.PMC10361427.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Taiwan Journal of Ophthalmology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4103/2211-5056.356685\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"OPHTHALMOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Taiwan Journal of Ophthalmology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4103/2211-5056.356685","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
Understanding required to consider artificial intelligence applications to the field of ophthalmology.
Machine learning operations (MLOps)[2] are becoming increasingly important and efforts should be made in this area. From an MLOps perspective, it is essential to have a mechanism to maintain and perpetuate the performance of artificial intelligence after its implementation in society. For example, for the intraocular lens check AI that the author and his team are developing,[3] providers must continuously replace the training data from obsolete intraocular lenses with new data.