Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes
Jingtong Huang, Andrea M. Yeung, David G. Armstrong, Ashley N. Battarbee, Jorge Cuadros, Juan Espinoza, Samantha Kleinberg, Nestoras Mathioudakis, Mark Swerdlow, David C. Klonoff
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.
Topics & Concepts
MedicineDiabetes mellitusGestational diabetesNephropathyHypoglycemiaDiabetic retinopathyIntensive care medicineRetinopathyDiabetic nephropathyPregnancyEndocrinologyGestationBiologyGeneticsArtificial Intelligence in HealthcareDiabetes Management and ResearchMachine Learning in Healthcare