Can AI predict glucose changes before they happen?
· Medical Xpressby Kaunas University of Technology
edited by Swati Mestri, reviewed by Robert Egan
Swati Mestri
Scientific Editor
Meet our editorial team
Behind our editorial process
Robert Egan
Senior Editor
Meet our editorial team
Behind our editorial process Editors' notes
This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility:
fact-checked
peer-reviewed publication
trusted source
proofread
The GIST Add as preferred source
Diabetes is one of the most common chronic diseases, affecting around 66 million adults in Europe—a figure projected to rise to more than 72 million by 2050. For people with type 1 diabetes, keeping glucose levels within a safe range requires continuous monitoring and careful insulin management. Yet even with experience and careful treatment, unexpected fluctuations can occur, potentially leading to hypoglycemia or hyperglycemia.
Glucose levels are influenced by far more than food and insulin. Physical activity, stress, hormonal changes, sleep quality, circadian rhythms and individual differences in metabolism can all play a role. Anticipating fluctuations could therefore help assess risks and support better-informed decisions about diabetes management.
Continuous glucose monitoring systems already record glucose levels every few minutes, generating vast amounts of data. Researchers at Kaunas University of Technology (KTU) are now exploring whether artificial intelligence (AI) can uncover patterns within these data. Their approach brings together glucose readings with information on insulin delivery, carbohydrate intake and physical activity.
AI identifies what matters most
In their latest study, KTU researchers developed a model that forecasts glucose changes and assesses the risk of hypoglycemia 30 and 60 minutes ahead. They also investigated how AI could be used to retrospectively evaluate personalized insulin adjustments.
To achieve this, the researchers combined several AI methods within a single framework.
The research is published in the Journal of the American Medical Informatics Association.
While the underlying technology may appear complex, KTU Professor Rytis Maskeliūnas says the basic idea is considerably simpler.
"Conventional forecasting models treat data as a chronological sequence, whereas the new approach represents each point in time as a node in a graph network and connects it to similar points in the past. This allows the model to learn not only from the direct sequence of events, but also from complex relationships between different physiological states," explains Maskeliūnas.
An important component of the model is what is known as an attention mechanism. It allows the algorithm to identify which earlier data points are most relevant to the outcome.
"It is similar to an experienced doctor assessing a patient's condition: rather than mechanically reviewing all the available data, they identify the factors that matter most in that particular situation," explains Maskeliūnas.
This principle also addresses another important challenge surrounding the use of AI in medicine: explainability. Even a highly accurate algorithm has limited value in health care if clinicians cannot understand how it arrived at a particular result. The researchers' approach therefore makes it possible to identify which features and earlier points in time had the greatest influence on the model's output.
Model could support clinical decision-making
The model was tested using international type 1 diabetes datasets and demonstrated high accuracy in both glucose forecasting and hypoglycemia risk assessment. The researchers also found that performance improved when the algorithm was trained to carry out both tasks simultaneously rather than separately.
"The results showed that the new models achieve very high forecasting accuracy and perform well even with patients whose data the algorithm has not previously encountered," says KTU Ph.D. student Muhammad Abdullah Sarwar, who contributed to the development of the model.
The personalized insulin adjustments evaluated in the study are not intended to allow patients to alter their treatment independently. Instead, they were investigated as a potential decision-support tool for clinical analysis.
"This is particularly important when developing widely applicable clinical decision-support tools, as such systems need to perform reliably across different hospitals and countries, and with different patient populations," emphasizes Sarwar.
High accuracy, however, does not mean that such algorithms are ready to become part of patients' everyday lives. "Further clinical studies are needed before the technology can be widely adopted in clinical practice. The models were evaluated using historical patient data, so it is essential to demonstrate that they remain equally accurate under real-world clinical conditions. Our medical partners will be responsible for this next stage," says Maskeliūnas.
Alongside clinical testing, questions remain around data security, patient privacy and the seamless integration of algorithms into clinicians' everyday work. The researchers see AI not as a technology that could replace doctors, but as a way to make better use of the vast amounts of data generated by patients and, in the future, provide more personalized support.
"Advanced, explainable AI models will not only allow us to predict glucose changes more accurately, but will also provide a foundation for developing safer, more personalized and more effective decision-support systems that can help reduce the heavy workload faced by clinicians," says the KTU professor.
Publication details
Muhammad Abdullah Sarwar et al, GAT-BiGRU: explainable multi-task temporal graph learning for glucose forecasting, hypoglycemia risk, and counterfactual insulin adjustment, Journal of the American Medical Informatics Association (2026). DOI: 10.1093/jamia/ocag104
Journal information: Journal of the American Medical Informatics Association
Key medical concepts
Clinical categories
EndocrinologyCommon illnesses & Prevention Provided by Kaunas University of Technology Who's behind this story?
Swati Mestri
Swati Mestri holds a bachelor's degree in Electronics Engineering and has worked as a content editor since 2019. She has experience editing research documents across technology, health care, and materials science, and has a particular interest in technology and space. Full profile →
Robert Egan
Bachelor's in mathematical biology, Master's in creative writing. Well-traveled with unique perspectives on science and language. Full profile →
Citation: Can AI predict glucose changes before they happen? (2026, September 9) retrieved 9 September 2026 from https://medicalxpress.com/news/2026-09-ai-glucose.html This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.