Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes

Abstract

Artificial intelligence can use real-world data to create models capable of making predictions and medical diagnosis for diabetes and its complications. The aim of this commentary article is to provide a general perspective and present recent advances on how artificial intelligence can be applied to improve the prediction and diagnosis of six significant complications of diabetes including (1) gestational diabetes, (2) hypoglycemia in the hospital, (3) diabetic retinopathy, (4) diabetic foot ulcers, (5) diabetic peripheral neuropathy, and (6) diabetic nephropathy.

Artificial intelligence in cardiovascular prevention: new ways will open new doors

“…Similarly, diabetes is a leading cause of death and its management as another chronic disease requires a novel approach to improving patients’ prognosis. In this field, the use of artificial intelligence has been tested to enhance the prediction and diagnosis of significant complications of diabetes leading to adverse cardiovascular events 20 . This is of particular interest in the field of gestational diabetes and recent research has identified the possibility of using a dedicated machine learning approach to improve its prediction 21 …”

“…In this field, the use of artificial intelligence has been tested to enhance the prediction and diagnosis of significant complications of diabetes leading to adverse cardiovascular events. 20 This is of particular interest in the field of gestational diabetes and recent research has identified the possibility of using a dedicated machine learning approach to improve its prediction. 21 In the management of dyslipidaemia, different potential applications of artificial intelligence have been tested so far, starting from the diagnosis to the management and prognosis related to the disease.…”

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

“…The incidence of DR is primarily related to the course of diabetes and the degree of disease control. The longer the course of diabetes, the higher the incidence of DR ( Huang et al, 2023 ). At present, the pathogenesis of DR is unclear, but glucose metabolism disorder is the root cause of DR ( Han et al, 2023 ).…”

Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis

“…These words were searched for in the title, abstract, and keywords. Moreover, to refine the search scope, the most employed ML and DL techniques were identified based on Huang et al 8 These techniques were examined using the authors' keywords. The search was restricted to English articles and reviews published between 2000 and 2022.…”

“…[2][3][4][5] These AI-driven approaches use computational algorithms to analyze complex data sets, identify patterns, and develop predictive models that can contribute to early diagnosis, personalized treatment, and effective management strategies for people with diabetes. [6][7][8] Over time, AI-based tools have also been used to enhance aspects of diabetes education, such as prediction, dietary and exercise guidance, carbohydrate counting, insulin dose guidance, monitoring of complications, and selfcontrol. 9 These advancements highlight the potential of AI to revolutionize the approach to diagnosing, treating, and educating individuals about this disease.…”