{"title":"The application of artificial intelligence in EUS.","authors":"Deyu Zhang, Chang Wu, Zhenghui Yang, Hua Yin, Yue Liu, Wanshun Li, Haojie Huang, Zhendong Jin","doi":"10.1097/eus.0000000000000053","DOIUrl":null,"url":null,"abstract":"<p><p>Artificial intelligence (AI) is an epoch-making technology, among which the 2 most advanced parts are machine learning and deep learning algorithms that have been further developed by machine learning, and it has been partially applied to assist EUS diagnosis. AI-assisted EUS diagnosis has been reported to have great value in the diagnosis of pancreatic tumors and chronic pancreatitis, gastrointestinal stromal tumors, esophageal early cancer, biliary tract, and liver lesions. The application of AI in EUS diagnosis still has some urgent problems to be solved. First, the development of sensitive AI diagnostic tools requires a large amount of high-quality training data. Second, there is overfitting and bias in the current AI algorithms, leading to poor diagnostic reliability. Third, the value of AI still needs to be determined in prospective studies. Fourth, the ethical risks of AI need to be considered and avoided.</p>","PeriodicalId":11577,"journal":{"name":"Endoscopic Ultrasound","volume":"13 2","pages":"65-75"},"PeriodicalIF":4.4000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11213611/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Endoscopic Ultrasound","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/eus.0000000000000053","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/4/10 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Artificial intelligence (AI) is an epoch-making technology, among which the 2 most advanced parts are machine learning and deep learning algorithms that have been further developed by machine learning, and it has been partially applied to assist EUS diagnosis. AI-assisted EUS diagnosis has been reported to have great value in the diagnosis of pancreatic tumors and chronic pancreatitis, gastrointestinal stromal tumors, esophageal early cancer, biliary tract, and liver lesions. The application of AI in EUS diagnosis still has some urgent problems to be solved. First, the development of sensitive AI diagnostic tools requires a large amount of high-quality training data. Second, there is overfitting and bias in the current AI algorithms, leading to poor diagnostic reliability. Third, the value of AI still needs to be determined in prospective studies. Fourth, the ethical risks of AI need to be considered and avoided.
人工智能（AI）是一项划时代的技术，其中最先进的两部分是机器学习和深度学习算法，并通过机器学习得到进一步发展，目前已部分应用于辅助 EUS 诊断。据报道，人工智能辅助 EUS 诊断在胰腺肿瘤和慢性胰腺炎、胃肠道间质瘤、食管早癌、胆道和肝脏病变的诊断中具有重要价值。人工智能在 EUS 诊断中的应用仍有一些亟待解决的问题。首先，开发灵敏的人工智能诊断工具需要大量高质量的训练数据。第二，目前的人工智能算法存在过度拟合和偏差，导致诊断可靠性差。第三，人工智能的价值仍需要前瞻性研究来确定。第四，需要考虑和避免人工智能的伦理风险。
期刊介绍：
Endoscopic Ultrasound, a publication of Euro-EUS Scientific Committee, Asia-Pacific EUS Task Force and Latin American Chapter of EUS, is a peer-reviewed online journal with Quarterly print on demand compilation of issues published. The journal’s full text is available online at http://www.eusjournal.com. The journal allows free access (Open Access) to its contents and permits authors to self-archive final accepted version of the articles on any OAI-compliant institutional / subject-based repository. The journal does not charge for submission, processing or publication of manuscripts and even for color reproduction of photographs.
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