Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes

Jingtong Huang, Andrea M. Yeung, David G. Armstrong, Ashley N. Battarbee, Jorge Cuadros, Juan C. Espinoza, Samantha Kleinberg, Nestoras Mathioudakis, Mark A. Swerdlow, David C. Klonoff

Research output: Contribution to journalComment/debate

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.

Original languageEnglish
Pages (from-to)224-238
Number of pages15
JournalJournal of diabetes science and technology
Volume17
Issue number1
DOIs
StatePublished - Jan 2023

ASJC Scopus subject areas

  • Bioengineering
  • Internal Medicine
  • Biomedical Engineering
  • Endocrinology, Diabetes and Metabolism

Keywords

  • artificial intelligence
  • complications
  • diabetes
  • machine learning algorithm
  • prediction
  • risk factors

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