scispace - formally typeset
Open AccessJournal ArticleDOI

Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here.

Reads0
Chats0
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
More filters
Journal ArticleDOI

Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning.

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

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).
References
More filters
Journal ArticleDOI

A new mathematical model for relative quantification in real-time RT-PCR.

TL;DR: This study enters into the particular topics of the relative quantification in real-time RT-PCR of a target gene transcript in comparison to a reference gene transcript and presents a new mathematical model that needs no calibration curve.
Journal ArticleDOI

The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus.

TL;DR: Intensive therapy effectively delays the onset and slows the progression of diabetic retinopathy, nephropathy, and neuropathy in patients with IDDM.
Journal ArticleDOI

Big data analytics in healthcare: promise and potential

TL;DR: Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs, and its potential is great; however there remain challenges to overcome.
Related Papers (5)