{"title":"定量放射学的工具:自然和人工智能的结合","authors":"Stefania MONTEMEZZI, Carlo CAVEDON","doi":"10.23736/s2723-9284.23.00249-9","DOIUrl":null,"url":null,"abstract":"Artificial intelligence (AI) is a fast-moving technology that enables machines to perform tasks that could previously be done only by humans. The current debate is now whether machines will outperform humans, and therefore substitute them in critical tasks. In this paper, an attempt will be made to identify the most used AI techniques in diagnostic imaging, providing examples and identifying potential pitfalls.","PeriodicalId":369070,"journal":{"name":"Journal of Radiological Review","volume":"160 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Tools for quantitative radiology: natural and artificial intelligence together\",\"authors\":\"Stefania MONTEMEZZI, Carlo CAVEDON\",\"doi\":\"10.23736/s2723-9284.23.00249-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Artificial intelligence (AI) is a fast-moving technology that enables machines to perform tasks that could previously be done only by humans. The current debate is now whether machines will outperform humans, and therefore substitute them in critical tasks. In this paper, an attempt will be made to identify the most used AI techniques in diagnostic imaging, providing examples and identifying potential pitfalls.\",\"PeriodicalId\":369070,\"journal\":{\"name\":\"Journal of Radiological Review\",\"volume\":\"160 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Radiological Review\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23736/s2723-9284.23.00249-9\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Radiological Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23736/s2723-9284.23.00249-9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Tools for quantitative radiology: natural and artificial intelligence together
Artificial intelligence (AI) is a fast-moving technology that enables machines to perform tasks that could previously be done only by humans. The current debate is now whether machines will outperform humans, and therefore substitute them in critical tasks. In this paper, an attempt will be made to identify the most used AI techniques in diagnostic imaging, providing examples and identifying potential pitfalls.
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