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Artificial Intelligence in Diabetes Care.

Michal Dubský1,2, Michaela Liegertová3, Robert Bém1,4

  • 1Diabetes Center, Institute for Clinical and Experimental Medicine, Prague, Czech Republic.

Diabetes, Obesity & Metabolism
|March 31, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) offers tools for diabetes management by synthesizing data for better decisions. This review examines AI applications, from automated insulin delivery to large language models, focusing on clinical readiness and responsible implementation.

Keywords:
glycaemic controlinsulin pump therapyrisk predictiontype 1 diabetes

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Area of Science:

  • Diabetes technology and computational health

Background:

  • Diabetes management involves complex decisions amid physiological variability.
  • Advancements in continuous glucose monitoring, connected insulin devices, and EHR data enable algorithm-driven tools.
  • These tools can synthesize high-frequency data, reduce patient/clinician cognitive load, and improve decision safety.

Purpose of the Study:

  • To broadly review artificial intelligence (AI) applications in diabetes care.
  • To differentiate between algorithmic automation/control and machine learning-based AI models.
  • To assess clinical readiness, evidence strength, and implementation considerations for AI in diabetes.

Main Methods:

  • Distinguishing between algorithmic automation/control and machine learning (ML) models (deep learning, LLMs).
  • Reviewing current and emerging AI applications in diabetes.
  • Focusing on clinical readiness, evidence, risks, and safeguards.

Main Results:

  • Algorithmic automation and ML-based models have distinct evidence standards, safety risks, and governance needs.
  • Established applications exist in automated insulin delivery (AID).
  • Emerging AI tools include LLMs, wearables, and digital twins.

Conclusions:

  • AI holds significant potential for improving diabetes care through data synthesis and decision support.
  • Responsible implementation requires careful consideration of evidence strength, clinical readiness, and potential risks.
  • Safeguards are crucial for the safe and effective clinical use of AI in diabetes management.