Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes. (January 2023)

• Record Type: Journal Article
• Authors: Huang, Jingtong; Yeung, Andrea M.; Armstrong, David G.; Battarbee, Ashley N.; Cuadros, Jorge; Espinoza, Juan C.; Kleinberg, Samantha; Mathioudakis, Nestoras; Swerdlow, Mark A.; Klonoff, David C.
• Is Part Of: Journal of diabetes science and technology. Volume 17:Number 1(2023)
• Page Start: 224
• Page End: 238
• Publication Date: 2023-01
• Subjects: diabetes -- complications -- artificial intelligence -- machine learning algorithm -- risk factors -- prediction; Diabetes -- Periodicals; Medical technology -- Periodicals; Diabetes Mellitus -- Periodicals; 616.462005
• DOI: 10.1177/19322968221124583
• Languages: English
• ISSNs: 1932-2968

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.