{"title":"Artificial intelligence for disease diagnostics still has a long way to go","authors":"Jian-She Yang, Qiang Wang, Zhong-Wei Lv","doi":"10.4329/wjr.v16.i3.69","DOIUrl":null,"url":null,"abstract":"Artificial intelligence (AI) can sometimes resolve difficulties that other advanced technologies and humans cannot. In medical diagnostics, AI has the advantage of processing figure recognition, especially for images with similar characteristics that are difficult to distinguish with the naked eye. However, the mechanisms of this advanced technique should be well-addressed to elucidate clinical issues. In this letter, regarding an original study presented by Takayama et al , we suggest that the authors should effectively illustrate the mechanism and detailed procedure that artificial intelligence techniques processing the acquired images, including the recognition of non-obvious difference between the normal parts and pathological ones, which were impossible to be distinguished by naked eyes, such as the basic constitutional elements of pixels and grayscale, special molecules or even some metal ions which involved into the diseases occurrence.","PeriodicalId":23819,"journal":{"name":"World journal of radiology","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"World journal of radiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4329/wjr.v16.i3.69","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
Artificial intelligence (AI) can sometimes resolve difficulties that other advanced technologies and humans cannot. In medical diagnostics, AI has the advantage of processing figure recognition, especially for images with similar characteristics that are difficult to distinguish with the naked eye. However, the mechanisms of this advanced technique should be well-addressed to elucidate clinical issues. In this letter, regarding an original study presented by Takayama et al , we suggest that the authors should effectively illustrate the mechanism and detailed procedure that artificial intelligence techniques processing the acquired images, including the recognition of non-obvious difference between the normal parts and pathological ones, which were impossible to be distinguished by naked eyes, such as the basic constitutional elements of pixels and grayscale, special molecules or even some metal ions which involved into the diseases occurrence.