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

Abstract

INTRODUCTION: Prediabetes is a prevalent condition in which early detection and lifestyle interventions can prevent or delay progression to diabetes. Artificial intelligence (AI) and machine learning (ML) offer enhanced tools for diagnosis, risk stratification, and scalable delivery of lifestyle interventions. This review synthesizes current applications of AI/ML in patients with prediabetes. METHODS: We conducted a scoping review using PubMed, EMBASE, and Web of Science (through May 2025) to identify original studies applying AI/ML to prediabetes prediction or management. Population-level forecasting and models combining prediabetes with other conditions were excluded. Data were extracted via structured REDCap instruments and validated through secondary review. Descriptive statistics summarized findings. RESULTS: Of 2072 records screened, 149 studies met criteria: 118 prediction model studies, 20 intervention studies, and 11 miscellaneous. Machine learning models primarily targeted prediction of prediabetes, progression to diabetes, diabetic complications, and glucose metrics. Overall model performance was favorable (mean C-statistic 0.81), with random forests, neural networks, and support vector machines showing better performance. Only 20 studies reported external validation, few compared ML to standard risk tools, and data/code availability was limited. Six AI-based diabetes prevention programs showed positive clinical outcomes, though randomized controlled trial (RCT) evidence was limited. Three personalized nutrition interventions showed mixed efficacy. 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.

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