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Artificial intelligence models for the diagnosis and management of liver diseases

With the development of more advanced methods for the diagnosis and treatment of diseases, the data required for medical care are becoming complex, and misinterpretation of information due to human error may result in serious consequences. Human error can be avoided with the support of artificial in...

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Detalles Bibliográficos
Autores principales: Nishida, Naoshi, Kudo, Masatoshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Society of Ultrasound in Medicine 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9816706/
https://www.ncbi.nlm.nih.gov/pubmed/36443931
http://dx.doi.org/10.14366/usg.22110
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author Nishida, Naoshi
Kudo, Masatoshi
author_facet Nishida, Naoshi
Kudo, Masatoshi
author_sort Nishida, Naoshi
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description With the development of more advanced methods for the diagnosis and treatment of diseases, the data required for medical care are becoming complex, and misinterpretation of information due to human error may result in serious consequences. Human error can be avoided with the support of artificial intelligence (AI). AI models trained with various medical data for diagnosis and management of liver diseases have been applied to hepatitis, fatty liver disease, liver cirrhosis, and liver cancer. Some of these models have been reported to outperform human experts in terms of performance, indicating their potential for supporting clinical practice given their high-speed output. This paper summarizes the recent advances in AI for liver disease and introduces the AI-aided diagnosis of liver tumors using B-mode ultrasonography.
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spelling pubmed-98167062023-01-11 Artificial intelligence models for the diagnosis and management of liver diseases Nishida, Naoshi Kudo, Masatoshi Ultrasonography Review Article With the development of more advanced methods for the diagnosis and treatment of diseases, the data required for medical care are becoming complex, and misinterpretation of information due to human error may result in serious consequences. Human error can be avoided with the support of artificial intelligence (AI). AI models trained with various medical data for diagnosis and management of liver diseases have been applied to hepatitis, fatty liver disease, liver cirrhosis, and liver cancer. Some of these models have been reported to outperform human experts in terms of performance, indicating their potential for supporting clinical practice given their high-speed output. This paper summarizes the recent advances in AI for liver disease and introduces the AI-aided diagnosis of liver tumors using B-mode ultrasonography. Korean Society of Ultrasound in Medicine 2023-01 2022-09-06 /pmc/articles/PMC9816706/ /pubmed/36443931 http://dx.doi.org/10.14366/usg.22110 Text en Copyright © 2023 Korean Society of Ultrasound in Medicine (KSUM) https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review Article
Nishida, Naoshi
Kudo, Masatoshi
Artificial intelligence models for the diagnosis and management of liver diseases
title Artificial intelligence models for the diagnosis and management of liver diseases
title_full Artificial intelligence models for the diagnosis and management of liver diseases
title_fullStr Artificial intelligence models for the diagnosis and management of liver diseases
title_full_unstemmed Artificial intelligence models for the diagnosis and management of liver diseases
title_short Artificial intelligence models for the diagnosis and management of liver diseases
title_sort artificial intelligence models for the diagnosis and management of liver diseases
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9816706/
https://www.ncbi.nlm.nih.gov/pubmed/36443931
http://dx.doi.org/10.14366/usg.22110
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