Artificial intelligence in diabetes management: Advancements, opportunities, and challenges

Abstract

The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.

Keywords: artificial intelligence; diabetes; digital health; management.

Graphical abstract

Figure 1

Current applications of machine intelligence in diabetes care and research Common algorithms used in supervised learning include (1) artificial neural networks, such as Boltzmann machines, restricted Boltzmann machines, multi-layer perceptron, radial basis function networks, recurrent neural networks, Hopfield networks, convolutional neural networks, and spiking neural networks; (2) Bayesian learning, such as naive Bayes, Gaussian naive Bayes, multiple naive Bayes, average one-dependence estimators, Bayesian belief networks, and Bayesian networks; (3) decision trees, such as classification and regression tree, Iterative Dichotomiser 3, C4.5 algorithm, C5tree.0 algorithm, chi-squared automatic interaction detection, decision stump, and supervised learning in quest; (4) ensemble methods, such as random forest, bagging, boosting, AdaBoost, and XGBoost; and (5) linear models, such as linear regression, logistic regression, generalized linear models, Fisher linear discriminant analysis, quadratic discriminant analysis, least absolute shrinkage and selection operator regression, multi-modal logistic regression, naive Bayes classifier, and perceptron and linear support vector machine. Common algorithms used in unsupervised learning include^, (1) transformation equivariant representations, such as group equivariant convolutions and autoencoding transformations; (2) generative models, such as flow-based generative models, generative adversarial networks, autoencoders, and disentangled representations; and (3) self-supervised methods, such as autoregressive and self-attention models.

Figure 2

Overview of AI application in diabetes management

Figure 3

Proposed AI-assisted digital health-care ecosystem for diabetes management Population with or without diabetes will use digital platforms to report electronic patient-reported outcomes (ePROs) to their electronic health record (EHR) and/or their clinicians. The information from the EHR and other diagnostic approaches will be integrated in a centralized and secure server environment. AI algorithms will be running on the data to enable the integration of all aspects of diabetes control, more effective visits with easier and better communication between visits, and better self-management through easier access to reliable and personalized health information.