Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here.
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TLDR
A predefined, online PubMed search of publicly available sources of information from 2009 onward using the search terms “diabetes” and “artificial intelligence” concluded that 450 published diabetes and AI articles met the inclusion criteria.Abstract:
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.read more
Citations
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Journal ArticleDOI
Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning.
Masaki Makino,Ryo Yoshimoto,Masaki Ono,Toshinari Itoko,Takayuki Katsuki,Akira Koseki,Michiharu Kudo,Kyoichi Haida,Jun Kuroda,Ryosuke Yanagiya,Eiichi Saitoh,Kiyotaka Hoshinaga,Yukio Yuzawa,Atsushi Suzuki +13 more
TL;DR: A new predictive model for diabetic kidney diseases (DKD) is constructed using AI, processing natural language and longitudinal data with big data machine learning, based on the electronic medical records of 64,059 diabetes patients and could predict DKD aggravation with 71% accuracy.
Journal ArticleDOI
Artificial Intelligence: The Future for Diabetes Care.
TL;DR: Intelligent technical reforms have produced better glycemic control with reductions in fasting and postprandial glucose levels, glucose excursions, and glycosylated hemoglobin.
Journal ArticleDOI
Deep Learning for Diabetes: A Systematic Review
TL;DR: A comprehensive review of the applications of deep learning within the field of diabetes is presented and it is noted that various deep learning techniques and frameworks have achieved state-of-the-art performance in many diabetes-related tasks by outperforming conventional machine learning approaches.
Journal ArticleDOI
A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities
TL;DR: This research explores various well-known databases using ”Breast Cancer” keyword to present a comprehensive survey on existing diagnostic schemes to open-up new research challenges for radiologists and researchers to intervene as early as possible to develop an efficient and reliable breast cancer prognosis system using prominent deep learning schemes.
Journal ArticleDOI
Prediction of Type 2 Diabetes Based on Machine Learning Algorithm
Henock M. Deberneh,Intaek Kim +1 more
TL;DR: In this article, a machine learning model was developed to predict type 2 diabetes (T2D) occurrence in the following year (Y + 1) using variables in the current year(Y).
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