Artificial intelligence for diabetes care: current and future prospects

Bin Sheng; Krithi Pushpanathan; Zhouyu Guan; Quan Hziung Lim; Zhi Wei Lim; Samantha Min Er Yew; Jocelyn Hui Lin Goh; Yong Mong Bee; Charumathi Sabanayagam; Nick Sevdalis; Cynthia Ciwei Lim; Chwee Teck Lim; Jonathan Shaw; Weiping Jia; Elif Ilhan Ekinci; Rafael Simó; Lee-Ling Lim; Huating Li; Yih-Chung Tham

doi:10.1016/S2213-8587(24)00154-2

Artificial intelligence for diabetes care: current and future prospects

Lancet Diabetes Endocrinol. 2024 Aug;12(8):569-595. doi: 10.1016/S2213-8587(24)00154-2.

Authors

Bin Sheng¹, Krithi Pushpanathan², Zhouyu Guan³, Quan Hziung Lim⁴, Zhi Wei Lim⁵, Samantha Min Er Yew², Jocelyn Hui Lin Goh⁶, Yong Mong Bee⁷, Charumathi Sabanayagam⁸, Nick Sevdalis⁹, Cynthia Ciwei Lim¹⁰, Chwee Teck Lim¹¹, Jonathan Shaw¹², Weiping Jia³, Elif Ilhan Ekinci¹³, Rafael Simó¹⁴, Lee-Ling Lim¹⁵, Huating Li¹⁶, Yih-Chung Tham¹⁷

Affiliations

¹ 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 Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China; Key Laboratory of Artificial Intelligence, Ministry of Education, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
² 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.
³ 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 Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China.
⁴ Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
⁵ Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
⁶ Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
⁷ Department of Endocrinology, Singapore General Hospital, Singapore; SingHealth Duke-National University of Singapore Diabetes Centre, Singapore Health Services, Singapore.
⁸ Ophthalmology and Visual Sciences Academic Clinical Program, Duke-National University of Singapore Medical School, Singapore; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
⁹ Centre for Behavioural and Implementation Science Interventions, National University of Singapore, Singapore.
¹⁰ Department of Renal Medicine, Singapore General Hospital, Singapore.
¹¹ 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.
¹² Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
¹³ 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.
¹⁴ 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.
¹⁵ 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.
¹⁶ 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 Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China. Electronic address: [email protected].
¹⁷ 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 Medical School, Singapore; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore. Electronic address: [email protected].

PMID: 39054035
DOI: 10.1016/S2213-8587(24)00154-2

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.

Publication types

Review

MeSH terms

Artificial Intelligence* / trends
Diabetes Mellitus* / diagnosis
Diabetes Mellitus* / therapy
Humans