Application of artificial intelligence in endoscopic gastrointestinal tumors

With an increasing number of patients with gastrointestinal cancer, effective and accurate early diagnostic clinical tools are required provide better health care for patients with gastrointestinal cancer. Recent studies have shown that artificial intelligence (AI) plays an important role in the dia...

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Bibliographic Details
Published in:	Frontiers in oncology Vol. 13; p. 1239788
Main Authors:	Xin, Yiping, Zhang, Qi, Liu, Xinyuan, Li, Bingqing, Mao, Tao, Li, Xiaoyu
Format:	Journal Article
Language:	English
Published:	Switzerland Frontiers Media S.A 2023
Subjects:	adenoma detection rate artificial intelligence colorectal cancer deep learning gastric cancer adenoma detection rate deep learning colorectal cancer gastric cancer artificial intelligence
Online Access:	Get full text
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Summary:	With an increasing number of patients with gastrointestinal cancer, effective and accurate early diagnostic clinical tools are required provide better health care for patients with gastrointestinal cancer. Recent studies have shown that artificial intelligence (AI) plays an important role in the diagnosis and treatment of patients with gastrointestinal tumors, which not only improves the efficiency of early tumor screening, but also significantly improves the survival rate of patients after treatment. With the aid of efficient learning and judgment abilities of AI, endoscopists can improve the accuracy of diagnosis and treatment through endoscopy and avoid incorrect descriptions or judgments of gastrointestinal lesions. The present article provides an overview of the application status of various artificial intelligence in gastric and colorectal cancers in recent years, and the direction of future research and clinical practice is clarified from a clinical perspective to provide a comprehensive theoretical basis for AI as a promising diagnostic and therapeutic tool for gastrointestinal cancer.
Bibliography:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-3 content type line 23 ObjectType-Review-1
ISSN:	2234-943X 2234-943X
DOI:	10.3389/fonc.2023.1239788