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Effectiveness and safety of AI-driven closed-loop systems in diabetes management: a systematic review and meta-analysis

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Publicado en:Diabetology & Metabolic Syndrome vol. 17 (2025), p. 1-13
Autor principal: Wang, Xiaoya
Otros Autores: Si, Jiayuan, Li, Yihao, Tse, Poki, Zhang, Guoyi, Wang, Xiaojie, Ren, Junming, Xu, Jin, Sun, Jiancui, Yao, Xi
Publicado:
Springer Nature B.V.
Materias:
Cochrane Library
Diabetes
Artificial intelligence
Trends
Hypoglycemia
Automation
Insulin
Performance evaluation
Metabolic disorders
Clinical outcomes
Bias
Disease management
Machine learning
Quality of life
Closed loop systems
Cardiovascular diseases
Diabetic neuropathy
Glucose
Diabetes mellitus
Neuropathy
Algorithms
Meta-analysis
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:1758-5996
DOI:10.1186/s13098-025-01819-0
Fuente:Health & Medical Collection