{"title":"放射学中的诊断想象:第1部分。","authors":"Rodney Sappington","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>*Machines that dream, the restless impulse for technical change that has marked radiology from its beginning and forays into deep neural networks, will no doubt unsettle long-held institu- tional practices in radiology. *A willingness to collaborate and puzzle through machine intelligence has come from those who have not accepted the status quo. A certain form of scientific curiosity has been a guiding principle in their work. *In radiology, machine intelligence has been extremely useful and built into just about every major technical innovation. But it has only been the last several years that a subfield of Al, machine learning, has begun to show remarkably fast development due to faster comput- er processing capabilities and advanced modeling and results emerging from the application of deep learning.</p>","PeriodicalId":74636,"journal":{"name":"Radiology management","volume":"38 6","pages":"39-44"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Diagnostic Imagination in Radiology: Part 1.\",\"authors\":\"Rodney Sappington\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>*Machines that dream, the restless impulse for technical change that has marked radiology from its beginning and forays into deep neural networks, will no doubt unsettle long-held institu- tional practices in radiology. *A willingness to collaborate and puzzle through machine intelligence has come from those who have not accepted the status quo. A certain form of scientific curiosity has been a guiding principle in their work. *In radiology, machine intelligence has been extremely useful and built into just about every major technical innovation. But it has only been the last several years that a subfield of Al, machine learning, has begun to show remarkably fast development due to faster comput- er processing capabilities and advanced modeling and results emerging from the application of deep learning.</p>\",\"PeriodicalId\":74636,\"journal\":{\"name\":\"Radiology management\",\"volume\":\"38 6\",\"pages\":\"39-44\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Radiology management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiology management","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
*Machines that dream, the restless impulse for technical change that has marked radiology from its beginning and forays into deep neural networks, will no doubt unsettle long-held institu- tional practices in radiology. *A willingness to collaborate and puzzle through machine intelligence has come from those who have not accepted the status quo. A certain form of scientific curiosity has been a guiding principle in their work. *In radiology, machine intelligence has been extremely useful and built into just about every major technical innovation. But it has only been the last several years that a subfield of Al, machine learning, has begun to show remarkably fast development due to faster comput- er processing capabilities and advanced modeling and results emerging from the application of deep learning.