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Applications of Artificial Intelligence and Machine Learning in Prediabetes: A Scoping Review.

Benjamin Lalani1, Rohan Herur1, Daniel Zade1

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Journal of Diabetes Science and Technology
|July 8, 2025
PubMed
Summary

Artificial intelligence and machine learning show promise in predicting prediabetes and aiding lifestyle interventions. However, more real-world validation and comparison to standard tools are needed for widespread clinical adoption.

Keywords:
artificial intelligencediabetesimpaired glucose tolerancelifestyle interventionmachine learningprediabetes

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Area of Science:

  • Endocrinology
  • Medical Informatics
  • Computational Biology

Background:

  • Prediabetes is common and can be managed with early detection and lifestyle changes.
  • Artificial intelligence (AI) and machine learning (ML) offer advanced tools for prediabetes diagnosis, risk assessment, and intervention delivery.
  • This review examines the current uses of AI/ML in managing prediabetes.

Purpose of the Study:

  • To review and synthesize the existing applications of AI/ML in prediabetes prediction and management.
  • To identify trends and limitations in AI/ML research for prediabetes.

Main Methods:

  • A scoping review of studies from PubMed, EMBASE, and Web of Science (up to May 2025) was performed.
  • Studies focused on AI/ML applications in prediabetes prediction or management were included; population-level forecasting and combined conditions were excluded.
  • Data extraction used structured REDCap instruments, with descriptive statistics summarizing the findings.

Main Results:

  • 149 studies met the criteria, with 118 focusing on prediction models and 20 on interventions.
  • Machine learning models demonstrated favorable performance (mean C-statistic 0.81) for predicting prediabetes, progression, complications, and glucose metrics.
  • External validation, comparison to standard tools, and data/code availability were limited; AI-based interventions showed positive outcomes but require more robust evidence.

Conclusions:

  • Current AI/ML research in prediabetes is heavily weighted toward predictive modeling, showing potential but facing translation challenges.
  • AI-driven interventions can potentially enhance behavioral support but need rigorous evaluation against standard care.
  • Future research should emphasize external validation, comparative effectiveness, and integration strategies into clinical practice.