AI-Driven CGM Insights Improved Glycemic Control | ADA 2025
In an abstract presented at the American Diabetes Association 85th Scientific Sessions, researchers tested the effectiveness of an AI-driven program for improving glycemic control.
With the help of artificial intelligence, daily continuous glucose monitor (CGM) insights resulted in significant improvements in glycemic control, according to an abstract presented at the American Diabetes Association 85th Scientific Sessions, held in Chicago, Illinois, from June 20-23, 2025.1
“A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes,” wrote authors of a study published in Cell Reports Medicine.2 “Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health care expenditures.”
Among all of the advancements in health care, one of the more notable developments has been AI’s integration within CGMs. With previous evidence showing a significant opportunity to combine AI with CGM technology, researchers and providers alike are trying to better understand how the 2 technologies can be leveraged in diabetes management.
Studies have gradually shown progress and integration of AI-powered technology within approaches to optimizing diabetes care. | image credit: Olga Gorkun / stock.adobe.com
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With AI on the cusp of advancing health care to places it has never been before, researchers of the current study wanted to better understand the effectiveness of AI within the diabetes and CGM spaces.
“AI-powered diabetes management platforms integrating CGM technology represent a promising advancement in health care,” wrote authors of the abstract.1 “This study evaluates the effectiveness of AI-integrated SDRMP platform in improving glycemic control.”
By including AI-driven solutions and insights within each patients’ diabetes care regimen, researchers also provided interventions for all participants through the SDRMP platform, or the SugarFit Diabetes Reversal and Management Program. The platform “integrates dietary changes, physical activity, and continuous support, evaluating its effectiveness in improving health outcomes,” according to authors of a study published in the International Journal of Diabetes and Technology.3
Using this program, researchers of the current study aimed to understand AI’s capabilities in meshing with CGM technology and improving patients’ diabetes outcomes.1
To understand the effectiveness of an AI-powered CGM, researchers conducted a 100-day retrospective study assessing the impact of personalized interventions for glycemic control. They recorded patients’ time in range (TIR), time below range (TBR), and time above range (TAR) using the CGM. Researchers also recorded patients’ HbA1c, fasting blood sugar (FBS), and weight.
The final analysis included a total of 1752 patients (77.5% men; mean age, 50.22 years). Finally, all participants gave their measurements at the start of the study period and were re-evaluated after an average of 100 days.
The most significant changes in glycemic control were identified in patients’ TBR, TAR, and TIR. Indeed, TBR decreased from 7.46 to 5.34, while TAR decreased from 49.89 to 45.33. TIR increased from 45.74 to 49.31. Finally, researchers uncovered reductions in weight, HbA1c, and FBS.
“Previous studies have shown that applying AI in diabetes management involves all aspects of disease control, including prediction, prevention, screening, diagnosis, and treatment,” continued authors of the Cell Reports Medicine study.2 “Integrating AI into clinical practice care could shift diabetes care toward precision, penetration, prediction, and personalization.”
AI within health care, and society as a whole, may be at its beginning stages. However, studies have gradually shown progress and integration of AI-powered technology within approaches to optimizing diabetes care. As diabetes becomes more prevalent worldwide, researchers continue to find better ways to adapt technology and streamline valued care for patients.
“Daily CGM trend-associated insights with intervention led to significant improvements in glycemic control, evident in substantial improvements in TIR, TBR, TAR, HbA1c, FBS, and weight, highlighting its effectiveness in optimizing metabolic outcomes and diabetes management,” concluded authors of the abstract.1
Read more from our coverage of the ADA’s 85th Scientific Sessions.
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References
1. Kumar S, Raymond AM, Sequeira A, et al. Real-world impact of AI-driven CGM platform on glycemic status in type 2 diabetes—a retrospective study. Presented at: American Diabetes Association 85th Scientific Sessions; June 20-23, 2025; Chicago, IL.
2. Guan Z, Li H, Liu R, et al. Artificial intelligence in diabetes management: advancements, opportunities, and challenges. Cell Rep Med. 2023 Oct 17;4(10):101213. doi: 10.1016/j.xcrm.2023.101213. Epub 2023 Oct 2.
3. DTechCon abstracts 2025. Int J Diabetes Technol. 4(Suppl 2):p S8-S22, June 2025. doi: 10.4103/ijdt.ijdt_20_25
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