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Artificial intelligence in gastroenterology and hepatology: Status and challenges
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 disea...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Baishideng Publishing Group Inc
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8072192/ https://www.ncbi.nlm.nih.gov/pubmed/33967550 http://dx.doi.org/10.3748/wjg.v27.i16.1664 |
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author | Cao, Jia-Sheng Lu, Zi-Yi Chen, Ming-Yu Zhang, Bin Juengpanich, Sarun Hu, Jia-Hao Li, Shi-Jie Topatana, Win Zhou, Xue-Yin Feng, Xu Shen, Ji-Liang Liu, Yu Cai, Xiu-Jun |
author_facet | Cao, Jia-Sheng Lu, Zi-Yi Chen, Ming-Yu Zhang, Bin Juengpanich, Sarun Hu, Jia-Hao Li, Shi-Jie Topatana, Win Zhou, Xue-Yin Feng, Xu Shen, Ji-Liang Liu, Yu Cai, Xiu-Jun |
author_sort | Cao, Jia-Sheng |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8072192 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Baishideng Publishing Group Inc |
record_format | MEDLINE/PubMed |
spelling | pubmed-80721922021-05-06 Artificial intelligence in gastroenterology and hepatology: Status and challenges Cao, Jia-Sheng Lu, Zi-Yi Chen, Ming-Yu Zhang, Bin Juengpanich, Sarun Hu, Jia-Hao Li, Shi-Jie Topatana, Win Zhou, Xue-Yin Feng, Xu Shen, Ji-Liang Liu, Yu Cai, Xiu-Jun World J Gastroenterol Review 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. Baishideng Publishing Group Inc 2021-04-28 2021-04-28 /pmc/articles/PMC8072192/ /pubmed/33967550 http://dx.doi.org/10.3748/wjg.v27.i16.1664 Text en ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved. https://creativecommons.org/licenses/by-nc/4.0/This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. |
spellingShingle | Review Cao, Jia-Sheng Lu, Zi-Yi Chen, Ming-Yu Zhang, Bin Juengpanich, Sarun Hu, Jia-Hao Li, Shi-Jie Topatana, Win Zhou, Xue-Yin Feng, Xu Shen, Ji-Liang Liu, Yu Cai, Xiu-Jun Artificial intelligence in gastroenterology and hepatology: Status and challenges |
title | Artificial intelligence in gastroenterology and hepatology: Status and challenges |
title_full | Artificial intelligence in gastroenterology and hepatology: Status and challenges |
title_fullStr | Artificial intelligence in gastroenterology and hepatology: Status and challenges |
title_full_unstemmed | Artificial intelligence in gastroenterology and hepatology: Status and challenges |
title_short | Artificial intelligence in gastroenterology and hepatology: Status and challenges |
title_sort | artificial intelligence in gastroenterology and hepatology: status and challenges |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8072192/ https://www.ncbi.nlm.nih.gov/pubmed/33967550 http://dx.doi.org/10.3748/wjg.v27.i16.1664 |
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