Effectiveness and safety of AI-driven closed-loop systems in diabetes management: a systematic review and meta-analysis
Abstract Background Diabetes 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 samplin...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-06-01
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| Series: | Diabetology & Metabolic Syndrome |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13098-025-01819-0 |
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| Summary: | Abstract Background Diabetes 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. Methods We 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. Results Compared 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). Conclusion AI-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. |
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| ISSN: | 1758-5996 |