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Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here
An estimated 425 million people globally have diabetes, accounting for 12% of the world's health expenditures, and yet 1 in 2 persons remain undiagnosed and untreated. Applications of artificial intelligence (AI) and cognitive computing offer promise in diabetes care. The purpose of this articl...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Mary Ann Liebert, Inc., publishers
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6555175/ https://www.ncbi.nlm.nih.gov/pubmed/30256722 http://dx.doi.org/10.1089/pop.2018.0129 |
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author | Dankwa-Mullan, Irene Rivo, Marc Sepulveda, Marisol Park, Yoonyoung Snowdon, Jane Rhee, Kyu |
author_facet | Dankwa-Mullan, Irene Rivo, Marc Sepulveda, Marisol Park, Yoonyoung Snowdon, Jane Rhee, Kyu |
author_sort | Dankwa-Mullan, Irene |
collection | PubMed |
description | An estimated 425 million people globally have diabetes, accounting for 12% of the world's health expenditures, and yet 1 in 2 persons remain undiagnosed and untreated. Applications of artificial intelligence (AI) and cognitive computing offer promise in diabetes care. The purpose of this article is to better understand what AI advances may be relevant today to persons with diabetes (PWDs), their clinicians, family, and caregivers. The authors conducted a predefined, online PubMed search of publicly available sources of information from 2009 onward using the search terms “diabetes” and “artificial intelligence.” The study included clinically-relevant, high-impact articles, and excluded articles whose purpose was technical in nature. A total of 450 published diabetes and AI articles met the inclusion criteria. The studies represent a diverse and complex set of innovative approaches that aim to transform diabetes care in 4 main areas: automated retinal screening, clinical decision support, predictive population risk stratification, and patient self-management tools. Many of these new AI-powered retinal imaging systems, predictive modeling programs, glucose sensors, insulin pumps, smartphone applications, and other decision-support aids are on the market today with more on the way. AI applications have the potential to transform diabetes care and help millions of PWDs to achieve better blood glucose control, reduce hypoglycemic episodes, and reduce diabetes comorbidities and complications. AI applications offer greater accuracy, efficiency, ease of use, and satisfaction for PWDs, their clinicians, family, and caregivers. |
format | Online Article Text |
id | pubmed-6555175 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Mary Ann Liebert, Inc., publishers |
record_format | MEDLINE/PubMed |
spelling | pubmed-65551752019-06-07 Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here Dankwa-Mullan, Irene Rivo, Marc Sepulveda, Marisol Park, Yoonyoung Snowdon, Jane Rhee, Kyu Popul Health Manag Original Articles An estimated 425 million people globally have diabetes, accounting for 12% of the world's health expenditures, and yet 1 in 2 persons remain undiagnosed and untreated. Applications of artificial intelligence (AI) and cognitive computing offer promise in diabetes care. The purpose of this article is to better understand what AI advances may be relevant today to persons with diabetes (PWDs), their clinicians, family, and caregivers. The authors conducted a predefined, online PubMed search of publicly available sources of information from 2009 onward using the search terms “diabetes” and “artificial intelligence.” The study included clinically-relevant, high-impact articles, and excluded articles whose purpose was technical in nature. A total of 450 published diabetes and AI articles met the inclusion criteria. The studies represent a diverse and complex set of innovative approaches that aim to transform diabetes care in 4 main areas: automated retinal screening, clinical decision support, predictive population risk stratification, and patient self-management tools. Many of these new AI-powered retinal imaging systems, predictive modeling programs, glucose sensors, insulin pumps, smartphone applications, and other decision-support aids are on the market today with more on the way. AI applications have the potential to transform diabetes care and help millions of PWDs to achieve better blood glucose control, reduce hypoglycemic episodes, and reduce diabetes comorbidities and complications. AI applications offer greater accuracy, efficiency, ease of use, and satisfaction for PWDs, their clinicians, family, and caregivers. Mary Ann Liebert, Inc., publishers 2019-06-01 2019-05-30 /pmc/articles/PMC6555175/ /pubmed/30256722 http://dx.doi.org/10.1089/pop.2018.0129 Text en © Irene Dankwa-Mullan et al. 2018; Published by Mary Ann Liebert, Inc. This Open Access article is distributed under the terms of the Creative Commons License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Articles Dankwa-Mullan, Irene Rivo, Marc Sepulveda, Marisol Park, Yoonyoung Snowdon, Jane Rhee, Kyu Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here |
title | Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here |
title_full | Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here |
title_fullStr | Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here |
title_full_unstemmed | Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here |
title_short | Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here |
title_sort | transforming diabetes care through artificial intelligence: the future is here |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6555175/ https://www.ncbi.nlm.nih.gov/pubmed/30256722 http://dx.doi.org/10.1089/pop.2018.0129 |
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