Diabetes, obesity & metabolism · 2026
Artificial Intelligence in Diabetes Care.
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Dubský Michal, Liegertová Michaela, Bém Robert, Sojáková Dominika, Fejfarová Vladimíra, Jude Edward B
Аннотация
BACKGROUND: Diabetes care requires frequent and high-stakes decisions that must be made in the setting of substantial day-to-day physiologic variability. The growing availability of continuous glucose monitoring, connected insulin delivery devices and longitudinal electronic health record data has created an opportunity for algorithm-enabled tools that can synthesise high-frequency data, reduce cognitive burden for patients and clinicians, and support safer and more consistent decision-making.
AIM: In this review, artificial intelligence (AI) is used broadly to describe computational systems that generate predictions, recommendations or automation from clinical data.
METHODS: We distinguish between algorithmic automation and control methods that underpin many currently deployed automated insulin delivery (AID) systems and machine learning-based models, including deep learning and large language models (LLMs), that are increasingly used for pattern recognition, risk prediction and natural language applications.
RESULTS: This distinction is clinically relevant because evidence standards, safety risks and governance needs vary substantially across these categories.
CONCLUSION: This narrative review summarises current and emerging applications of AI in diabetes care with an emphasis on clinical readiness, strength of evidence and implementation considerations. We highlight established applications in AID, emerging approaches that seek greater autonomy and interoperability and newer tools such as LLMs, wearables and digital twin frameworks, focusing on where evidence is strongest, where risks are highest and what safeguards are required for responsible clinical use.
AIM: In this review, artificial intelligence (AI) is used broadly to describe computational systems that generate predictions, recommendations or automation from clinical data.
METHODS: We distinguish between algorithmic automation and control methods that underpin many currently deployed automated insulin delivery (AID) systems and machine learning-based models, including deep learning and large language models (LLMs), that are increasingly used for pattern recognition, risk prediction and natural language applications.
RESULTS: This distinction is clinically relevant because evidence standards, safety risks and governance needs vary substantially across these categories.
CONCLUSION: This narrative review summarises current and emerging applications of AI in diabetes care with an emphasis on clinical readiness, strength of evidence and implementation considerations. We highlight established applications in AID, emerging approaches that seek greater autonomy and interoperability and newer tools such as LLMs, wearables and digital twin frameworks, focusing on where evidence is strongest, where risks are highest and what safeguards are required for responsible clinical use.