The use of artificial intelligence (AI) in healthcare continues to provoke both excitement and skepticism, particularly in chronic disease management such as type 1 diabetes mellitus. In a recent study by Cetin et al. (2025), the early effectiveness of an AI-augmented automated insulin delivery (AID) system was closely examined to answer a central question: Is AI truly impactful in real-world glycemic control?
This clinical investigation evaluated the Medtrum A8 TouchCare® Nano system among 15 type 1 diabetes patients. The focus was on the system’s AI-driven self-learning capacity to optimize insulin dosing in real-time. The study’s strength lies in its meticulous comparison of glycemic parameters before and after AID initiation, especially during the first 15 days.
One of the most striking findings was the rapid improvement in blood glucose regulation immediately following AID initiation. Median Time in Range (TIR)—a key marker of glycemic control—improved from 55.9% before AID to 81.5% in the 15 days post-AID, with the sharpest gain of 10.1% observed just one day after starting the system. This swift improvement suggests that AI algorithms begin exerting a meaningful effect almost immediately, challenging the notion that a lengthy learning period is necessary.
Additionally, glycemic variability, measured by the interquartile range (IQR), significantly decreased from 78 mg/dL to 55 mg/dL. Hyperglycemia episodes dropped substantially, while hypoglycemia parameters remained unchanged—indicating that AI systems may enhance safety without increasing risk.
Interestingly, no significant glycemic improvements were observed when comparing different 5-day periods after AID initiation or extending the analysis to 30 and 90 days. This plateau effect implies that the major gains occur early and that the system sustains rather than improves performance over time.
The authors emphasize that these early-stage improvements cannot be attributed to increased insulin doses or patient behavioral changes. Rather, they reflect the AID system’s adaptive intelligence, which personalizes insulin delivery dynamically in response to real-time glucose data.
This study, though limited by its small sample size and lack of a control group, raises important implications for clinical practice. It underscores the need to revisit initiation protocols and supports the integration of AI systems from the very start of insulin therapy for eligible patients. Moreover, it calls for more rigorous, large-scale studies to establish optimal use guidelines and confirm these preliminary findings.
In summary, the study makes a compelling case that AI-powered insulin delivery systems are not only effective but begin working almost instantly, transforming diabetes management with unprecedented precision and speed.
Reference
Cetin, F., Sukru Goncuoglu, E., Abali, S., et al. (2025). Early stage effectiveness of the automated insulin delivery system—is artificial intelligence really effective? Endocrinology Research and Practice, 29(2), 101–106. https://doi.org/10.5152/erp.2025.24618
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