Sheng, Bin and Pushpanathan, Krithi and Guan, Zhouyu and Lim, Quan Hziung and Lim, Zhi Wei and Yew, Samantha Min Er and Goh, Jocelyn Hui Lin and Bee, Yong Mong and Sabanayagam, Charumathi and Sevdalis, Nick and Lim, Cynthia Ciwei and Lim, Chwee Teck and Shaw, Jonathan and Jia, Weiping and Ekinci, Elif Ilhan and Simo, Rafael and Lim, Lee-Ling and Li, Huating and Tham, Yih-Chung (2024) Artificial intelligence for diabetes care: current and future prospects. Lancet Diabetes & Endocrinology, 12 (8). pp. 569-595. ISSN 2213-8587, DOI https://doi.org/10.1016/S2213-8587(24)00154-2.
Full text not available from this repository.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 healthcare 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.
| Item Type: | Article |
|---|---|
| Funders: | Shanghai Key Discipline of Public Health (GWVI11.120), General Fund of the National Natural Science Foundation of China (62272298), National Key Research & Development Program of China (2022YFA1004804), National Natural Science Foundation of China (NSFC) (82022012), Innovative research team of highlevel local universities in Shanghai (SHSMUZDCX20212700), National University Health System, Singapore (2023) |
| Subjects: | R Medicine > R Medicine (General) |
| Divisions: | Faculty of Medicine > Medicine Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 07 Apr 2025 08:07 |
| Last Modified: | 07 Apr 2025 08:07 |
| URI: | http://eprints.um.edu.my/id/eprint/46742 |
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