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人工智能在胃肠病学和肝脏病学中的应用:现状与挑战。

Artificial intelligence in gastroenterology and hepatology: Status and challenges.

机构信息

Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China.

Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang Province, China.

出版信息

World J Gastroenterol. 2021 Apr 28;27(16):1664-1690. doi: 10.3748/wjg.v27.i16.1664.


DOI:10.3748/wjg.v27.i16.1664
PMID:33967550
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8072192/
Abstract

Originally proposed by John McCarthy in 1955, artificial intelligence (AI) has achieved a breakthrough and revolutionized the processing methods of clinical medicine with the increasing workloads of medical records and digital images. Doctors are paying attention to AI technologies for various diseases in the fields of gastroenterology and hepatology. This review will illustrate AI technology procedures for medical image analysis, including data processing, model establishment, and model validation. Furthermore, we will summarize AI applications in endoscopy, radiology, and pathology, such as detecting and evaluating lesions, facilitating treatment, and predicting treatment response and prognosis with excellent model performance. The current challenges for AI in clinical application include potential inherent bias in retrospective studies that requires larger samples for validation, ethics and legal concerns, and the incomprehensibility of the output results. Therefore, doctors and researchers should cooperate to address the current challenges and carry out further investigations to develop more accurate AI tools for improved clinical applications.

摘要

人工智能(AI)最初由约翰·麦卡锡(John McCarthy)于 1955 年提出,随着病历和数字图像工作量的增加,它在临床医学的处理方法上取得了突破和变革。医生们开始关注 AI 技术在胃肠病学和肝病学等各个领域的各种疾病。本综述将说明用于医学图像分析的 AI 技术流程,包括数据处理、模型建立和模型验证。此外,我们将总结 AI 在内镜、放射学和病理学中的应用,例如检测和评估病变、辅助治疗以及具有出色模型性能的预测治疗反应和预后。AI 在临床应用中面临的当前挑战包括回顾性研究中潜在的固有偏差,这需要更大的样本量进行验证、伦理和法律问题以及输出结果的不可理解性。因此,医生和研究人员应合作应对当前的挑战,并进行进一步的研究,开发更准确的 AI 工具,以改善临床应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e925/8072192/3643525d1a78/WJG-27-1664-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e925/8072192/500cb8c5c601/WJG-27-1664-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e925/8072192/3643525d1a78/WJG-27-1664-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e925/8072192/500cb8c5c601/WJG-27-1664-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e925/8072192/3643525d1a78/WJG-27-1664-g002.jpg

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本文引用的文献

[1]
Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis.

Nat Cancer. 2020-8

[2]
Clinical-Radiomic Analysis for Pretreatment Prediction of Objective Response to First Transarterial Chemoembolization in Hepatocellular Carcinoma.

Liver Cancer. 2021-2

[3]
Artificial intelligence in upper GI endoscopy - current status, challenges and future promise.

J Gastroenterol Hepatol. 2021-1

[4]
Radiomic Feature-Based Predictive Model for Microvascular Invasion in Patients With Hepatocellular Carcinoma.

Front Oncol. 2020-11-5

[5]
Radiomics Analysis Based on Multiparametric MRI for Predicting Early Recurrence in Hepatocellular Carcinoma After Partial Hepatectomy.

J Magn Reson Imaging. 2021-4

[6]
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Eur Radiol. 2021-6

[7]
Artificial intelligence in colonoscopy - Now on the market. What's next?

J Gastroenterol Hepatol. 2021-1

[8]
Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning.

World J Gastroenterol. 2020-10-28

[9]
Potentials of AI in medical image analysis in Gastroenterology and Hepatology.

J Gastroenterol Hepatol. 2021-1

[10]
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Front Pharmacol. 2020-10-2

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