文献检索文档翻译深度研究
Suppr Zotero 插件Zotero 插件
邀请有礼套餐&价格历史记录

新学期,新优惠

限时优惠:9月1日-9月22日

30天高级会员仅需29元

1天体验卡首发特惠仅需5.99元

了解详情
不再提醒
插件&应用
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
高级版
套餐订阅购买积分包
AI 工具
文献检索文档翻译深度研究
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2025

人工智能在胃肠内镜中的不断发展的作用。

Evolving role of artificial intelligence in gastrointestinal endoscopy.

机构信息

Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, United States.

出版信息

World J Gastroenterol. 2020 Dec 14;26(46):7287-7298. doi: 10.3748/wjg.v26.i46.7287.


DOI:10.3748/wjg.v26.i46.7287
PMID:33362384
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7739161/
Abstract

Artificial intelligence (AI) is a combination of different technologies that enable machines to sense, comprehend, and learn with human-like levels of intelligence. AI technology will eventually enhance human capability, provide machines genuine autonomy, and reduce errors, and increase productivity and efficiency. AI seems promising, and the field is full of invention, novel applications; however, the limitation of machine learning suggests a cautious optimism as the right strategy. AI is also becoming incorporated into medicine to improve patient care by speeding up processes and achieving greater accuracy for optimal patient care. AI using deep learning technology has been used to identify, differentiate catalog images in several medical fields including gastrointestinal endoscopy. The gastrointestinal endoscopy field involves endoscopic diagnoses and prognostication of various digestive diseases using image analysis with the help of various gastrointestinal endoscopic device systems. AI-based endoscopic systems can reliably detect and provide crucial information on gastrointestinal pathology based on their training and validation. These systems can make gastroenterology practice easier, faster, more reliable, and reduce inter-observer variability in the coming years. However, the thought that these systems will replace human decision making replace gastrointestinal endoscopists does not seem plausible in the near future. In this review, we discuss AI and associated various technological terminologies, evolving role in gastrointestinal endoscopy, and future possibilities.

摘要

人工智能(AI)是多种技术的结合,使机器能够具有类似于人类的感知、理解和学习能力。AI 技术最终将增强人类的能力,为机器提供真正的自主性,并减少错误,提高生产力和效率。人工智能似乎很有前途,该领域充满了发明和新颖的应用;然而,机器学习的局限性表明,谨慎乐观是正确的策略。人工智能也被应用于医学领域,以通过加速流程和提高最佳患者护理的准确性来改善患者护理。人工智能使用深度学习技术已被用于识别和区分包括胃肠内窥镜检查在内的多个医学领域的图像目录。胃肠内窥镜检查领域涉及使用各种胃肠内窥镜设备系统进行图像分析来进行内窥镜诊断和各种消化疾病的预后。基于人工智能的内窥镜系统可以根据其训练和验证可靠地检测和提供有关胃肠道病理的重要信息。这些系统可以使胃肠病学实践在未来几年变得更加容易、快速、可靠,并减少观察者间的差异。然而,这些系统将取代人类决策的想法,似乎在不久的将来不太可能取代胃肠内窥镜医生。在这篇综述中,我们讨论了人工智能及其相关的各种技术术语,以及它在胃肠内窥镜检查中的不断发展的作用和未来的可能性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7c7/7739161/f56927a7744e/WJG-26-7287-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7c7/7739161/f56927a7744e/WJG-26-7287-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7c7/7739161/f56927a7744e/WJG-26-7287-g001.jpg

相似文献

[1]
Evolving role of artificial intelligence in gastrointestinal endoscopy.

World J Gastroenterol. 2020-12-14

[2]
Proceedings from the First Global Artificial Intelligence in Gastroenterology and Endoscopy Summit.

Gastrointest Endosc. 2020-10

[3]
Consensus statements on the current landscape of artificial intelligence applications in endoscopy, addressing roadblocks, and advancing artificial intelligence in gastroenterology.

Gastrointest Endosc. 2025-1

[4]
Artificial intelligence application in diagnostic gastrointestinal endoscopy - Deus ex machina?

World J Gastroenterol. 2021-8-28

[5]
Overview of Deep Learning in Gastrointestinal Endoscopy.

Gut Liver. 2019-1-11

[6]
Potentials of AI in medical image analysis in Gastroenterology and Hepatology.

J Gastroenterol Hepatol. 2021-1

[7]
Artificial intelligence in gastrointestinal endoscopy for inflammatory bowel disease: a systematic review and new horizons.

Therap Adv Gastroenterol. 2021-6-10

[8]
Challenges involved in the application of artificial intelligence in gastroenterology: The race is on!

World J Gastroenterol. 2023-12-28

[9]
Application of Artificial Intelligence in Gastrointestinal Endoscopy.

J Clin Gastroenterol. 2021-2-1

[10]
Artificial intelligence in gastrointestinal endoscopy.

VideoGIE. 2020-11-9

引用本文的文献

[1]
Predictive model integrating deep learning and clinical features based on ultrasound imaging data for surgical intervention in intussusception in children younger than 8 months.

BMJ Open. 2025-8-22

[2]
Enhanced gastrointestinal disease classification using a convvit hybrid model on endoscopic images.

Phys Eng Sci Med. 2025-7-21

[3]
EUS-based intratumoral and peritumoral machine learning radiomics analysis for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer.

Front Oncol. 2025-3-4

[4]
An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer.

Sci Rep. 2025-1-27

[5]
A novel endoscopic ultrasomics-based machine learning model and nomogram to predict the pathological grading of pancreatic neuroendocrine tumors.

Heliyon. 2024-7-9

[6]
Role of Artificial Intelligence in the Diagnosis of Gastroesophageal Reflux Disease.

Cureus. 2024-6-11

[7]
Endoscopic ultrasonography-based intratumoral and peritumoral machine learning radiomics analyses for distinguishing insulinomas from non-functional pancreatic neuroendocrine tumors.

Front Endocrinol (Lausanne). 2024

[8]
Construction and validation of an endoscopic ultrasonography-based ultrasomics nomogram for differentiating pancreatic neuroendocrine tumors from pancreatic cancer.

Front Oncol. 2024-5-23

[9]
The adoption of artificial intelligence assisted endoscopy in the Middle East: challenges and future potential.

Transl Gastroenterol Hepatol. 2023-10-25

[10]
A Comprehensive Guide to Artificial Intelligence in Endoscopic Ultrasound.

J Clin Med. 2023-5-30

本文引用的文献

[1]
Pancreatic Cancer Prediction Through an Artificial Neural Network.

Front Artif Intell. 2019-5-3

[2]
The importance of colonoscopy bowel preparation for the detection of colorectal lesions and colorectal cancer prevention.

Endosc Int Open. 2020-5

[3]
The impact of deep convolutional neural network-based artificial intelligence on colonoscopy outcomes: A systematic review with meta-analysis.

J Gastroenterol Hepatol. 2020-4-26

[4]
Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network.

Gastrointest Endosc. 2020-7

[5]
Deep learning algorithms for automated detection of Crohn's disease ulcers by video capsule endoscopy.

Gastrointest Endosc. 2020-3

[6]
Clinical usefulness of a deep learning-based system as the first screening on small-bowel capsule endoscopy reading.

Dig Endosc. 2020-5

[7]
Artificial intelligence using a convolutional neural network for automatic detection of small-bowel angioectasia in capsule endoscopy images.

Dig Endosc. 2020-3

[8]
Gastroenterologist-Level Identification of Small-Bowel Diseases and Normal Variants by Capsule Endoscopy Using a Deep-Learning Model.

Gastroenterology. 2019-6-25

[9]
Deep Learning to Classify Intraductal Papillary Mucinous Neoplasms Using Magnetic Resonance Imaging.

Pancreas. 2019-7

[10]
Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study.

Gut. 2019-2-27

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

推荐工具

医学文档翻译智能文献检索