-1(1).png)
.png)
.png)
Objective: This study aimed to evaluate the effectiveness of the self-learning capabilities of artificial intelligence (AI) algorithms. The hypothesis was that if the success of closed-loop insulin delivery is mainly attributed to AI algorithms, then the improvement in glycemic control would be more significant just after the “learning” phase.
Methods: The Medtrum A8 TouchCare® Nano system was used on 15 patients with type 1 diabetes. Daily continuous glucose monitoring (CGM) data pre-automated insulin delivery (AID) was statistically compared with the post-AID period.
Results: Patients (median age 32 (6-54) years, 40% female) had a median HbA1c of 8.4% (5.3-10.7) before initiation of AID and a median GMI of 6.6% (5.8-8.3) after 2 weeks. The shifts in glycemia and glycemic variability between the 5-day period pre-AID vs. the first day and the 3 5-day periods post-AID were significant (pre-AID vs. 1-5-10-15 days; time in range (TIR, %): 55.9 vs. 76.6-81.7-83.8- 81.5 (P=.001); Q1 (mg/dL): 123 vs. 112-108-106-110 (P=.009); Q3 (mg/dL): 204 vs. 176-173-168-169 (P=.004); inter-quarter range (IQR, mg/dL): 78 vs. 57.2-56.6-53-55 (P=.002)). The biggest shift in TIR was achieved in the first day (10.1%). Comparative analysis of the 5-day intervals post-AID was insignificant by means of the improvement in glycemia (P > .05). No significant change in glycemic parameters between 15, 30, and 90 days were noted (P > .05).
Conclusion: Artificial intelligence-augmented AID becomes effective at the very early stages of initiation. There is a need for further research into glycemic changes in the early days of AID initiation to better define the principles of initiating AID systems.
Cite this article as: Cetin F, Sukru Goncuoglu E, Abali S, et al. Early stage effectiveness of the automated insulin delivery system—is artificial intelligence really effective? Endocrinol Res Pract. Published online March 3, 2025. doi 10.5152/erp.2025.24618.