TY - JOUR
T1 - Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes
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.
N1 - Publisher Copyright:
© 2022 Diabetes Technology Society.
PY - 2023/1
Y1 - 2023/1
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
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U2 - 10.1177/19322968221124583
DO - 10.1177/19322968221124583
M3 - Comment/debate
C2 - 36121302
AN - SCOPUS:85139088128
VL - 17
SP - 224
EP - 238
JO - Journal of Diabetes Science and Technology
JF - Journal of Diabetes Science and Technology
IS - 1
ER -