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Artificial intelligence applications in pediatric oncology diagnosis

Artificial intelligence (AI) algorithms have been applied in abundant medical tasks with high accuracy and efficiency. Physicians can improve their diagnostic efficiency with the assistance of AI techniques for improving the subsequent personalized treatment and surveillance. AI algorithms fundament...

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Detalles Bibliográficos
Autores principales: Yang, Yuhan, Zhang, Yimao, Li, Yuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Open Exploration 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10017189/
https://www.ncbi.nlm.nih.gov/pubmed/36937318
http://dx.doi.org/10.37349/etat.2023.00127
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author Yang, Yuhan
Zhang, Yimao
Li, Yuan
author_facet Yang, Yuhan
Zhang, Yimao
Li, Yuan
author_sort Yang, Yuhan
collection PubMed
description Artificial intelligence (AI) algorithms have been applied in abundant medical tasks with high accuracy and efficiency. Physicians can improve their diagnostic efficiency with the assistance of AI techniques for improving the subsequent personalized treatment and surveillance. AI algorithms fundamentally capture data, identify underlying patterns, achieve preset endpoints, and provide decisions and predictions about real-world events with working principles of machine learning and deep learning. AI algorithms with sufficient graphic processing unit power have been demonstrated to provide timely diagnostic references based on preliminary training of large amounts of clinical and imaging data. The sample size issue is an inevitable challenge for pediatric oncology considering its low morbidity and individual heterogeneity. However, this problem may be solved in the near future considering the exponential advancements of AI algorithms technically to decrease the dependence of AI operation on the amount of data sets and the efficiency of computing power. For instance, it could be a feasible solution by shifting convolutional neural networks (CNNs) from adults and sharing CNN algorithms across multiple institutions besides original data. The present review provides important insights into emerging AI applications for the diagnosis of pediatric oncology by systematically overviewing of up-to-date literature.
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spelling pubmed-100171892023-03-16 Artificial intelligence applications in pediatric oncology diagnosis Yang, Yuhan Zhang, Yimao Li, Yuan Explor Target Antitumor Ther Review Artificial intelligence (AI) algorithms have been applied in abundant medical tasks with high accuracy and efficiency. Physicians can improve their diagnostic efficiency with the assistance of AI techniques for improving the subsequent personalized treatment and surveillance. AI algorithms fundamentally capture data, identify underlying patterns, achieve preset endpoints, and provide decisions and predictions about real-world events with working principles of machine learning and deep learning. AI algorithms with sufficient graphic processing unit power have been demonstrated to provide timely diagnostic references based on preliminary training of large amounts of clinical and imaging data. The sample size issue is an inevitable challenge for pediatric oncology considering its low morbidity and individual heterogeneity. However, this problem may be solved in the near future considering the exponential advancements of AI algorithms technically to decrease the dependence of AI operation on the amount of data sets and the efficiency of computing power. For instance, it could be a feasible solution by shifting convolutional neural networks (CNNs) from adults and sharing CNN algorithms across multiple institutions besides original data. The present review provides important insights into emerging AI applications for the diagnosis of pediatric oncology by systematically overviewing of up-to-date literature. Open Exploration 2023 2023-02-28 /pmc/articles/PMC10017189/ /pubmed/36937318 http://dx.doi.org/10.37349/etat.2023.00127 Text en © The Author(s) 2023. https://creativecommons.org/licenses/by/4.0/This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Review
Yang, Yuhan
Zhang, Yimao
Li, Yuan
Artificial intelligence applications in pediatric oncology diagnosis
title Artificial intelligence applications in pediatric oncology diagnosis
title_full Artificial intelligence applications in pediatric oncology diagnosis
title_fullStr Artificial intelligence applications in pediatric oncology diagnosis
title_full_unstemmed Artificial intelligence applications in pediatric oncology diagnosis
title_short Artificial intelligence applications in pediatric oncology diagnosis
title_sort artificial intelligence applications in pediatric oncology diagnosis
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10017189/
https://www.ncbi.nlm.nih.gov/pubmed/36937318
http://dx.doi.org/10.37349/etat.2023.00127
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