AI and Diabetes: Smarter Glucose Monitoring for Better Health

By Soham NandiReviewed by Bethan DaviesMar 20 2025

Managing diabetes is no small task. With over 463 million people worldwide affected—and that number expected to hit 700 million by 2045—keeping blood sugar levels in check is more important than ever. The challenge at present is that traditional glucose monitoring methods can be invasive, inconvenient, and stressful. That’s where AI-powered glucose sensors are stepping in, making diabetes management smarter, easier, and more personalized.¹

Image Credit: everydayplus/Shutterstock.com

Continuous glucose monitoring (CGM) has already had a massive impact, providing real-time insights into blood sugar levels. But when combined with AI and machine learning (ML), CGMs become even more powerful, predicting trends, reducing the need for fingerpricks, and giving patients more control over their health. This isn’t just about convenience—it’s about improving outcomes and making life with diabetes a little less overwhelming.^2,3

Download your PDF copy now!

The Evolution of Glucose Monitoring

If you have diabetes (or know someone who does), you’re probably familiar with the traditional fingerprick test. While effective, it comes with some major downsides—pain, risk of infection, and the hassle of remembering to test multiple times a day. For people with type 1 diabetes (T1D), especially younger patients, these drawbacks can lead to inconsistent monitoring and poor glucose control.

Enter CGMs. Devices like the FreeStyle Libre and Dexcom G6 continuously track glucose levels without requiring constant fingerpricks. They provide real-time readings, trend insights, and even alerts for dangerously high or low blood sugar. No more guessing games, no more surprises.

Despite these benefits, CGMs aren’t perfect. Cost, user discomfort, and the need for education are still barriers to widespread adoption. Older adults and those unfamiliar with tech may find these systems challenging to use. Bridging these gaps is key to making CGMs accessible for all.^4-6

How AI Supercharges Glucose Monitoring

Now, imagine adding AI into the mix. AI-powered CGMs take things a step further by analyzing data in real-time, identifying patterns, and predicting glucose levels before they spike or drop. This means fewer surprises, more proactive care, and ultimately, better diabetes management.⁷

For example, researchers have developed neural network (NN)-based algorithms that can predict glucose levels 30 minutes ahead using just 20 minutes of CGM data. That’s a game-changer for people trying to prevent dangerous highs and lows. Long short-term memory (LSTM) models have also been able to predict glucose levels with impressive accuracy, offering a critical window for intervention.^7,13

AI isn’t just about prediction—it’s about personalization. By analyzing lifestyle factors like food intake, exercise, and medication, AI models tailor recommendations to each individual. The OhioT1DM dataset, for example, has helped researchers develop patient-specific glucose prediction models that outperform one-size-fits-all approaches.⁷

AI also plays a big role in hypoglycemia prevention. Machine learning classifiers like extreme gradient boosting (XGB) have been shown to improve time spent in a safe blood sugar range by predicting post-meal hypoglycemia. Decision trees and random forests have demonstrated accuracy rates of up to 86.67% in predicting hypoglycemia during exercise—helping patients take preventive action before it’s too late.^7,14

And then there are wearables. Devices like smartwatches are now equipped with biosensors that track heart rate, skin temperature, and blood oxygen levels. AI processes this data to create digital biomarkers, offering a non-invasive way to estimate blood glucose levels. The accuracy is impressive, with AI models using wearable data achieving RMSE ranges as low as 0.099.^8,9

AI and the Future of Insulin Delivery

AI isn’t just improving glucose tracking—it’s transforming insulin delivery, too. Closed-loop insulin pumps, also known as artificial pancreas systems, use AI-driven algorithms to automate insulin dosing based on real-time CGM data.

Take the Medtronic MiniMed 670G, for instance. This FDA-approved system adjusts basal insulin levels automatically, reducing the burden of constant manual adjustments. Similarly, the Tandem X2 pump, integrated with Dexcom G6, uses AI to predict glucose trends and optimize insulin delivery. These smart systems help keep blood sugar levels in check with less effort from the patient.¹⁰

NN and other ML algorithms are also enabling highly personalized diabetes care by adapting to individual metabolic responses. For instance, AI algorithms can analyze CGM data, insulin doses, carbohydrate intake, and physical activity to tailor insulin recommendations.¹¹

Several AI-driven platforms are already making an impact in diabetes care. Abbott’s FreeStyle Libre, combined with AI algorithms, provides real-time glucose predictions and alerts for hypo- and hyperglycemia, enhancing patient safety. The Guardian Connect System by Medtronic uses AI to predict hypoglycemic events up to an hour in advance, with an accuracy of 98.5 %. Another example is DreaMed Diabetes’ Advisor Pro, an FDA-approved platform that analyzes CGM and self-monitoring blood glucose (SMBG) data to recommend insulin dose adjustments, offering a viable alternative to specialist-led care.^10,12

The Roadblocks to AI-Driven Diabetes Care

As exciting as AI-powered diabetes tech is, there are still hurdles to overcome.¹¹

1. Data Quality & Bias – AI models need massive datasets to work well, but if the data is inaccurate, biased, or doesn’t represent diverse populations, the results can be flawed. Fair, high-quality data is key to making AI-powered diabetes care accessible to everyone.
2. User Experience Issues – Some AI-driven health tools have clunky interfaces that frustrate users. If patients struggle to navigate these systems, they’re less likely to stick with them. A more user-friendly design approach is essential.
3. Integration with Healthcare Systems – Many AI tools aren’t seamlessly integrated into clinical workflows yet. Doctors may be hesitant to trust AI-based recommendations, especially if they aren’t transparent. AI should complement healthcare providers, not replace them.
4. Privacy & Security – Health data is sensitive, and storing it in centralized systems poses security risks. New approaches like decentralized “swarm learning” could offer a more secure way to train AI models while keeping data private.
5. Adoption & Regulations – Patients won’t stick with AI-driven tools if they don’t see clear benefits. At the same time, unclear legal frameworks around AI liability can make healthcare providers wary of adoption. Smarter regulations and seamless EHR integration could help.

Final Thoughts

AI-powered glucose monitoring is changing the way people manage diabetes, offering smarter, more personalized insights that improve outcomes. By combining CGMs, wearables, and advanced machine learning algorithms, AI is making it easier to detect and prevent dangerous blood sugar fluctuations before they happen.

But to fully unlock its potential, we need to tackle challenges like data quality, usability, clinical integration, and privacy. As AI technology evolves, ensuring equitable access and intuitive design will be key to making AI-driven diabetes care available to everyone who needs it.

Download your PDF copy now!

Want to Learn More?

If this article has taken your interest, why not check out some of the below topics:

• A Guide to Understanding Blood Glucose Monitoring Sensors
• Transforming Diabetes Care with Continuous Glucose Monitoring
• What is Diabetes?
• Non-Invasive Diabetes Diagnosis and Monitoring

References and Further Reading

1. Gillani, S., Azhar, A., Mohiuddin, G., & Majeed, R. (2020). A systematic review on the clinical implication of continuous glucose monitoring in diabetes management. Journal of Pharmacy and Bioallied Sciences, 12(2), 102. DOI:10.4103/jpbs.jpbs_7_20
2. ‌Ahmed, A., Aziz, S., Qidwai, U., Abd-Alrazaq, A., & Sheikh, J. (2023). Performance of artificial intelligence models in estimating blood glucose level among diabetic patients using non-invasive wearable device data. Computer Methods and Programs in Biomedicine Update, 3, 100094. DOI:10.1016/j.cmpbup.2023.100094
3. ‌Jin, X., Cai, A., Xu, T., & Zhang, X. (2023). Artificial intelligence biosensors for continuous glucose monitoring. Interdisciplinary Materials, 2(2), 290–307. DOI:10.1002/idm2.12069
4. ‌Al Hayek, A. A., Robert, A. A., & Al Dawish, M. A. (2019). Differences of FreeStyle Libre Flash Glucose Monitoring System and Finger Pricks on Clinical Characteristics and Glucose Monitoring Satisfactions in Type 1 Diabetes Using Insulin Pump. Clinical Medicine Insights: Endocrinology and Diabetes, 12, 117955141986110. DOI:10.1177/1179551419861102
5. ‌Garg, S., & Parkin, C. G. (2023). Past, Present, and Future of Continuous Glucose Monitors (CGMs). Diabetes Technology & Therapeutics. DOI:10.1089/dia.2023.0041
6. ‌Prasad-Reddy, L., Godina, A., Chetty, A., & Isaacs, D. (2022). Use of Continuous Glucose Monitoring in Older Adults: A Review of Benefits, Challenges and Future Directions. European Endocrinology, 18(2), 116. DOI:10.17925/ee.2022.18.2.116
7. ‌Vettoretti, M., Cappon, G., Facchinetti, A., & Sparacino, G. (2020). Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors. Sensors, 20(14), 3870. DOI:10.3390/s20143870
8. ‌Jin, X., Cai, A., Xu, T., & Zhang, X. (2023). Artificial intelligence biosensors for continuous glucose monitoring. Interdisciplinary Materials, 2(2), 290–307. DOI:10.1002/idm2.12069
9. ‌Ahmed, A., Aziz, S., Qidwai, U., Abd-Alrazaq, A., & Sheikh, J. (2023). Performance of artificial intelligence models in estimating blood glucose level among diabetic patients using non-invasive wearable device data. Computer Methods and Programs in Biomedicine Update, 3, 100094. DOI:10.1016/j.cmpbup.2023.100094
10. ‌Almurashi, A. M., Rodriguez, E., & Garg, S. K. (2023). Emerging Diabetes Technologies: Continuous Glucose Monitors/Artificial Pancreases. Journal of the Indian Institute of Sciences, 103. DOI:10.1007/s41745-022-00348-3
11. ‌Guan et al. (2023). Artificial intelligence in diabetes management: Advancements, opportunities, and challenges. Cell Reports Medicine, 4(10), 101213–101213. DOI:10.1016/j.xcrm.2023.101213
12. ‌Nomura, A., Noguchi, M., Kometani, M., Furukawa, K., & Yoneda, T. (2021). Artificial Intelligence in Current Diabetes Management and Prediction. Current Diabetes Reports, 21(12). DOI:10.1007/s11892-021-01423-2
13. Pérez-Gandía et al. (2010). Artificial Neural Network Algorithm for Online Glucose Prediction from Continuous Glucose Monitoring. Diabetes Technology & Therapeutics, 12(1), 81–88. DOI:10.1089/dia.2009.0076
14. Cappon, Facchinetti., Sparacino., Georgiou, & Herrero. (2019). Classification of Postprandial Glycemic Status with Application to Insulin Dosing in Type 1 Diabetes—An In Silico Proof-of-Concept. Sensors, 19(14), 3168. DOI:10.3390/s19143168

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.