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Artificial intelligence for diabetes care: current and future prospects.
Sheng, Bin; Pushpanathan, Krithi; Guan, Zhouyu; Lim, Quan Hziung; Lim, Zhi Wei; Yew, Samantha Min Er; Goh, Jocelyn Hui Lin; Bee, Yong Mong; Sabanayagam, Charumathi; Sevdalis, Nick; Lim, Cynthia Ciwei; Lim, Chwee Teck; Shaw, Jonathan; Jia, Weiping; Ekinci, Elif Ilhan; Simó, Rafael; Lim, Lee-Ling; Li, Huating; Tham, Yih-Chung.
Affiliation
  • Sheng B; Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabol
  • Pushpanathan K; Centre of Innovation and Precision Eye Health, Department of Ophthalmology, National University of Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
  • Guan Z; Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabol
  • Lim QH; Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
  • Lim ZW; Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
  • Yew SME; Centre of Innovation and Precision Eye Health, Department of Ophthalmology, National University of Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
  • Goh JHL; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
  • Bee YM; Department of Endocrinology, Singapore General Hospital, Singapore; SingHealth Duke-National University of Singapore Diabetes Centre, Singapore Health Services, Singapore.
  • Sabanayagam C; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-National University of Singapore Medical School, Singapore; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
  • Sevdalis N; Centre for Behavioural and Implementation Science Interventions, National University of Singapore, Singapore.
  • Lim CC; Department of Renal Medicine, Singapore General Hospital, Singapore.
  • Lim CT; Department of Biomedical Engineering, National University of Singapore, Singapore; Institute for Health Innovation and Technology, National University of Singapore, Singapore; Mechanobiology Institute, National University of Singapore, Singapore.
  • Shaw J; Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
  • Jia W; Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabol
  • Ekinci EI; Australian Centre for Accelerating Diabetes Innovations, Melbourne Medical School and Department of Medicine, University of Melbourne, Melbourne, VIC, Australia; Department of Endocrinology, Austin Health, Melbourne, VIC, Australia.
  • Simó R; Diabetes and Metabolism Research Unit, Vall d'Hebron University Hospital and Vall d'Hebron Research Institute, Barcelona, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Madrid, Spain.
  • Lim LL; Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Asia Diabetes Foundation, Hong Kong Special Administrative Region, China.
  • Li H; Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabol
  • Tham YC; Centre of Innovation and Precision Eye Health, Department of Ophthalmology, National University of Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-National University of Singapore Me
Lancet Diabetes Endocrinol ; 12(8): 569-595, 2024 Aug.
Article de En | MEDLINE | ID: mdl-39054035
ABSTRACT
Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise care for people with diabetes and adapt treatments for complex presentations. However, the rapid advancement of AI also introduces challenges such as potential biases, ethical considerations, and implementation challenges in ensuring that its deployment is equitable. Ensuring inclusive and ethical developments of AI technology can empower both health-care providers and people with diabetes in managing the condition. In this Review, we explore and summarise the current and future prospects of AI across the diabetes care continuum, from enhancing screening and diagnosis to optimising treatment and predicting and managing complications.
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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Intelligence artificielle / Diabète Limites: Humans Langue: En Journal: Lancet Diabetes Endocrinol Année: 2024 Type de document: Article Pays de publication: Royaume-Uni
Texte intégral
Ajouter à My VHL
Imprimer
XML
PubMed Links
Recherche sur Google

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Intelligence artificielle / Diabète Limites: Humans Langue: En Journal: Lancet Diabetes Endocrinol Année: 2024 Type de document: Article Pays de publication: Royaume-Uni