Artificial intelligence in diabetes management: Advancements, opportunities, and challenges

被引:100
作者
Guan, Zhouyu [1 ]
Li, Huating [1 ]
Liu, Ruhan [1 ,2 ,3 ]
Cai, Chun [1 ]
Liu, Yuexing [1 ]
Li, Jiajia [1 ,2 ]
Wang, Xiangning [4 ]
Huang, Shan [1 ,2 ]
Wu, Liang [1 ]
Liu, Dan [1 ]
Yu, Shujie [1 ]
Wang, Zheyuan [1 ,2 ]
Shu, Jia [1 ,2 ]
Hou, Xuhong [1 ]
Yang, Xiaokang [2 ]
Jia, Weiping [1 ]
Sheng, Bin [1 ,2 ]
机构
[1] Shanghai Jiao Tong Univ, Sch Med 6, Shanghai Peoples Hosp, Shanghai Clin Ctr Diabet,Shanghai Int Joint Lab In, Shanghai 200240, Peoples R China
[2] Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, MOE Key Lab AI, Shanghai 200240, Peoples R China
[3] Natl Engn Res Ctr Personalized Diagnost & Therapeu, Furong Lab, Changsha, Hunan, Peoples R China
[4] Shanghai Jiao Tong Univ, Affiliated Peoples Hosp 6, Dept Ophthalmol, Shanghai 200233, Peoples R China
关键词
DEEP LEARNING ALGORITHM; CHRONIC KIDNEY-DISEASE; NEURAL-NETWORK; RISK-FACTORS; RETINOPATHY; MELLITUS; PREDICTION; CLASSIFICATION; TECHNOLOGY; MODEL;
D O I
10.1016/j.xcrm.2023.101213
中图分类号
Q2 [细胞生物学];
学科分类号
071009 ; 090102 ;
摘要
The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge world-wide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medi-cations, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.
引用
收藏
页数:20
相关论文
共 149 条
[71]   Six Digital Health Technologies That Will Transform Diabetes [J].
Klonoff, Alexander N. ;
Lee, Wei-An ;
Xu, Nicole Y. ;
Nguyen, Kevin T. ;
DuBord, Ashley ;
Kerr, David .
JOURNAL OF DIABETES SCIENCE AND TECHNOLOGY, 2023, 17 (01) :239-249
[72]  
Kodama Satoru, 2021, JMIR Diabetes, V6, pe22458, DOI 10.2196/22458
[73]   Realization of a service for the long-term risk assessment of diabetes-related complications [J].
Lagani, Vincenzo ;
Chiarugi, Franco ;
Manousos, Dimitris ;
Verma, Vivek ;
Fursse, Joanna ;
Marias, Kostas ;
Tsamardinos, Ioannis .
JOURNAL OF DIABETES AND ITS COMPLICATIONS, 2015, 29 (05) :691-698
[74]   Deep learning [J].
LeCun, Yann ;
Bengio, Yoshua ;
Hinton, Geoffrey .
NATURE, 2015, 521 (7553) :436-444
[75]   A tongue features fusion approach to predicting prediabetes and diabetes with machine learning [J].
Li, Jun ;
Yuan, Pei ;
Hu, Xiaojuan ;
Huang, Jingbin ;
Cui, Longtao ;
Cui, Ji ;
Ma, Xuxiang ;
Jiang, Tao ;
Yao, Xinghua ;
Li, Jiacai ;
Shi, Yulin ;
Bi, Zijuan ;
Wang, Yu ;
Fu, Hongyuan ;
Wang, Jue ;
Lin, Yenting ;
Pai, ChingHsuan ;
Guo, Xiaojing ;
Zhou, Changle ;
Tu, Liping ;
Xu, Jiatuo .
JOURNAL OF BIOMEDICAL INFORMATICS, 2021, 115
[76]   An Application of Artificial Immune Recognition System for Prediction of Diabetes Following Gestational Diabetes [J].
Lin, Hung-Chun ;
Su, Chao-Ton ;
Wang, Pa-Chun .
JOURNAL OF MEDICAL SYSTEMS, 2011, 35 (03) :283-289
[77]   Predicting complications of diabetes mellitus using advanced machine learning algorithms [J].
Ljubic, Branimir ;
Hai, Ameen Abdel ;
Stanojevic, Marija ;
Diaz, Wilson ;
Polimac, Daniel ;
Pavlovski, Martin ;
Obradovic, Zoran .
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2020, 27 (09) :1343-1351
[78]   Artificial intelligence-enabled decision support in nephrology [J].
Loftus, Tyler J. ;
Shickel, Benjamin ;
Ozrazgat-Baslanti, Tezcan ;
Ren, Yuanfang ;
Glicksberg, Benjamin S. ;
Cao, Jie ;
Singh, Karandeep ;
Chan, Lili ;
Nadkarni, Girish N. ;
Bihorac, Azra .
NATURE REVIEWS NEPHROLOGY, 2022, 18 (07) :452-465
[79]   Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction [J].
Lopez, Beatriz ;
Torrent-Fontbona, Ferran ;
Vinas, Ramon ;
Manuel Fernandez-Real, Jose .
ARTIFICIAL INTELLIGENCE IN MEDICINE, 2018, 85 :43-49
[80]  
Magliano D. J., 2021, IDF DIABETES ATLAS, V10th