Applications of Artificial Intelligence and Machine Learning in Prediabetes: A Scoping Review
Authors' conclusion
Most AI/ML research in prediabetes focused on predictive modeling, which shows promise but limited translation to real-world settings. Artificial intelligence-based interventions may scale behavioral change support but need further evaluation versus standard care. Future efforts should prioritize external validation, assess added value over standard tools, and address barriers to integration into care.
Study context
- Population
- Original studies applying artificial intelligence (AI)/machine learning (ML) to prediabetes prediction or management; population-level forecasting and models combining prediabetes with other conditions excludedstudies included
- Participants
- 149 studies
- Outcome
- Applications of AI/ML in prediabetes
Methodological detailsPubMed, EMBASE, Web of Science through May 2025, structured REDCap data extraction validated by secondary review
- PubMed, EMBASE, Web of Science through May 2025
- Structured REDCap data extraction validated by secondary review
Main finding
As reported in the abstract
- Records screened
- 2072
- Prediction model studies
- 118
- Intervention studies
- 20
- Miscellaneous studies
- 11
- Overall model performance, mean C-statistic
- 0.81
- Studies reporting external validation
- 20
- AI-based diabetes prevention programs showing positive clinical outcomes (randomized controlled trial (RCT) evidence limited)
- 6
- Personalized nutrition interventions with mixed efficacy
- 3
Original publication
Applications of Artificial Intelligence and Machine Learning in Prediabetes: A Scoping Review. J Diabetes Sci Technol. Published 8 July 2025. doi:10.
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