Effectiveness and safety of AI-driven closed-loop systems in diabetes management: a systematic review and meta-analysis
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| Publikašuvnnas: | Diabetology & Metabolic Syndrome vol. 17 (2025), p. 1-13 |
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| Váldodahkki: | |
| Eará dahkkit: | , , , , , , , , |
| Almmustuhtton: |
Springer Nature B.V.
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| Fáttát: | |
| Liŋkkat: | Citation/Abstract Full Text Full Text - PDF |
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|---|---|---|---|
| 001 | 3227650214 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1758-5996 | ||
| 024 | 7 | |a 10.1186/s13098-025-01819-0 |2 doi | |
| 035 | |a 3227650214 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 113214 |2 nlm | ||
| 100 | 1 | |a Wang, Xiaoya | |
| 245 | 1 | |a Effectiveness and safety of AI-driven closed-loop systems in diabetes management: a systematic review and meta-analysis | |
| 260 | |b Springer Nature B.V. |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a BackgroundDiabetes is a metabolic disease that can lead to severe cardiovascular diseases and neuropathy. The associated medical costs and complications make timely and effective management particularly important. Traditional diagnostic and management methods, like frequent glucose sampling and insulin injections, impose physical injuries on subjects. The development of artificial intelligence (AI) has opened new opportunities for diabetes management.MethodsWe conducted a meta-analysis integrating existing research, identifying a total of 1156 subjects to assess the effectiveness and safety of AI-based wearable devices, specifically closed-loop insulin delivery systems, in diabetes treatment.ResultsCompared to standard controls, AI-based closed-loop systems can analyze glucose data in real-time and automatically adjust insulin delivery, resulting in reduced time outside target glucose ranges (SMD = 0.90, 95% CI = 0.69 to 1.10, I2 = 58%, P < 0.001).ConclusionAI-based closed-loop systems enhance the precision and convenience of diabetes treatment. This meta-analysis providing essential references for clinical treatment and policymaking in diabetes care. | |
| 610 | 4 | |a Cochrane Library | |
| 653 | |a Diabetes | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Trends | ||
| 653 | |a Hypoglycemia | ||
| 653 | |a Automation | ||
| 653 | |a Insulin | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Metabolic disorders | ||
| 653 | |a Clinical outcomes | ||
| 653 | |a Bias | ||
| 653 | |a Disease management | ||
| 653 | |a Machine learning | ||
| 653 | |a Quality of life | ||
| 653 | |a Closed loop systems | ||
| 653 | |a Cardiovascular diseases | ||
| 653 | |a Diabetic neuropathy | ||
| 653 | |a Glucose | ||
| 653 | |a Diabetes mellitus | ||
| 653 | |a Neuropathy | ||
| 653 | |a Algorithms | ||
| 653 | |a Meta-analysis | ||
| 700 | 1 | |a Si, Jiayuan | |
| 700 | 1 | |a Li, Yihao | |
| 700 | 1 | |a Tse, Poki | |
| 700 | 1 | |a Zhang, Guoyi | |
| 700 | 1 | |a Wang, Xiaojie | |
| 700 | 1 | |a Ren, Junming | |
| 700 | 1 | |a Xu, Jin | |
| 700 | 1 | |a Sun, Jiancui | |
| 700 | 1 | |a Yao, Xi | |
| 773 | 0 | |t Diabetology & Metabolic Syndrome |g vol. 17 (2025), p. 1-13 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3227650214/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3227650214/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3227650214/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |