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Artificial Intelligence in Current Diabetes Management and Prediction
PURPOSE OF REVIEW: Artificial intelligence (AI) can make advanced inferences based on a large amount of data. The mainstream technologies of the AI boom in 2021 are machine learning (ML) and deep learning, which have made significant progress due to the increase in computational resources accompanie...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8668843/ https://www.ncbi.nlm.nih.gov/pubmed/34902070 http://dx.doi.org/10.1007/s11892-021-01423-2 |
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author | Nomura, Akihiro Noguchi, Masahiro Kometani, Mitsuhiro Furukawa, Kenji Yoneda, Takashi |
author_facet | Nomura, Akihiro Noguchi, Masahiro Kometani, Mitsuhiro Furukawa, Kenji Yoneda, Takashi |
author_sort | Nomura, Akihiro |
collection | PubMed |
description | PURPOSE OF REVIEW: Artificial intelligence (AI) can make advanced inferences based on a large amount of data. The mainstream technologies of the AI boom in 2021 are machine learning (ML) and deep learning, which have made significant progress due to the increase in computational resources accompanied by the dramatic improvement in computer performance. In this review, we introduce AI/ML-based medical devices and prediction models regarding diabetes. RECENT FINDINGS: In the field of diabetes, several AI-/ML-based medical devices and regarding automatic retinal screening, clinical diagnosis support, and patient self-management tool have already been approved by the US Food and Drug Administration. As for new-onset diabetes prediction using ML methods, its performance is not superior to conventional risk stratification models that use statistical approaches so far. SUMMARY: Despite the current situation, it is expected that the predictive performance of AI will soon be maximized by a large amount of organized data and abundant computational resources, which will contribute to a dramatic improvement in the accuracy of disease prediction models for diabetes. |
format | Online Article Text |
id | pubmed-8668843 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-86688432021-12-28 Artificial Intelligence in Current Diabetes Management and Prediction Nomura, Akihiro Noguchi, Masahiro Kometani, Mitsuhiro Furukawa, Kenji Yoneda, Takashi Curr Diab Rep Diabetes Epidemiology (HC Yeh, Section Editor) PURPOSE OF REVIEW: Artificial intelligence (AI) can make advanced inferences based on a large amount of data. The mainstream technologies of the AI boom in 2021 are machine learning (ML) and deep learning, which have made significant progress due to the increase in computational resources accompanied by the dramatic improvement in computer performance. In this review, we introduce AI/ML-based medical devices and prediction models regarding diabetes. RECENT FINDINGS: In the field of diabetes, several AI-/ML-based medical devices and regarding automatic retinal screening, clinical diagnosis support, and patient self-management tool have already been approved by the US Food and Drug Administration. As for new-onset diabetes prediction using ML methods, its performance is not superior to conventional risk stratification models that use statistical approaches so far. SUMMARY: Despite the current situation, it is expected that the predictive performance of AI will soon be maximized by a large amount of organized data and abundant computational resources, which will contribute to a dramatic improvement in the accuracy of disease prediction models for diabetes. Springer US 2021-12-13 2021 /pmc/articles/PMC8668843/ /pubmed/34902070 http://dx.doi.org/10.1007/s11892-021-01423-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Diabetes Epidemiology (HC Yeh, Section Editor) Nomura, Akihiro Noguchi, Masahiro Kometani, Mitsuhiro Furukawa, Kenji Yoneda, Takashi Artificial Intelligence in Current Diabetes Management and Prediction |
title | Artificial Intelligence in Current Diabetes Management and Prediction |
title_full | Artificial Intelligence in Current Diabetes Management and Prediction |
title_fullStr | Artificial Intelligence in Current Diabetes Management and Prediction |
title_full_unstemmed | Artificial Intelligence in Current Diabetes Management and Prediction |
title_short | Artificial Intelligence in Current Diabetes Management and Prediction |
title_sort | artificial intelligence in current diabetes management and prediction |
topic | Diabetes Epidemiology (HC Yeh, Section Editor) |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8668843/ https://www.ncbi.nlm.nih.gov/pubmed/34902070 http://dx.doi.org/10.1007/s11892-021-01423-2 |
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