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

doi:10.1177/19322968221124583

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

University of Southern California
University of Alabama at Birmingham
University of California at Berkeley
Johns Hopkins University
Sutter Health

Research output: Contribution to journal › Comment/debate

54 Scopus citations

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 language	English
Pages (from-to)	224-238
Number of pages	15
Journal	Journal of Diabetes Science and Technology
Volume	17
Issue number	1
DOIs	https://doi.org/10.1177/19322968221124583
State	Published - Jan 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

artificial intelligence
complications
diabetes
machine learning algorithm
prediction
risk factors

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10.1177/19322968221124583

Cite this

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title = "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.",

keywords = "artificial intelligence, complications, diabetes, machine learning algorithm, prediction, risk factors",

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volume = "17",

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AU - Huang, Jingtong

AU - Yeung, Andrea M.

AU - Armstrong, David G.

AU - Battarbee, Ashley N.

AU - Cuadros, Jorge

AU - Espinoza, Juan C.

AU - Kleinberg, Samantha

AU - Mathioudakis, Nestoras

AU - Swerdlow, Mark A.

AU - Klonoff, David C.

PY - 2023/1

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N2 - 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.

AB - 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.

KW - artificial intelligence

KW - complications

KW - diabetes

KW - machine learning algorithm

KW - prediction

KW - risk factors

UR - https://www.scopus.com/pages/publications/85139088128

UR - https://www.scopus.com/pages/publications/85139088128#tab=citedBy

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DO - 10.1177/19322968221124583

M3 - Comment/debate

C2 - 36121302

AN - SCOPUS:85139088128

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SP - 224

EP - 238

JO - Journal of Diabetes Science and Technology

JF - Journal of Diabetes Science and Technology

IS - 1

ER -

Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes

Abstract

UN SDGs

Keywords

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