Artificial Intelligence for Glucose Prediction and Glycemic Management in Diabetes: A Systematic Review of Predictive Performance and Clinical Outcomes

Artificial intelligence (AI) is increasingly being investigated for glucose prediction and personalized glycemic management in diabetes, particularly through continuous glucose monitoring and multimodal patient data.However, strong predictive performance does not necessarily translate into clinically meaningful benefit.This systematic review evaluated the predictive performance and clinical outcomes of AI-based approaches for glucose prediction, prediction of glycemic events, and AI-enabled glycemic management in adults with diabetes mellitus.A systematic literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science, the Cochrane Library, and Google Scholar from database inception through July 2026.Studies evaluating AI or machine-learning approaches for glucose prediction, prediction of hypoglycemia or hyperglycemia, or AI-enabled glycemic management were included.Data on study characteristics, diabetes type, AI architecture, input variables, prediction targets and horizons, validation strategies, predictive performance measures, clinical outcomes, and methodological quality were extracted.Risk of bias was assessed using design-appropriate validated tools.Thirty studies were included, comprising evidence from type 1 diabetes, type 2 diabetes, and mixed diabetes populations.Most studies investigated short-term glucose forecasting or hypoglycemia prediction, with prediction horizons ranging from approximately 15 minutes to 24 hours.Reported predictive performance was generally promising, although substantial heterogeneity in datasets, model architectures, prediction horizons, outcome definitions, and validation strategies limited direct comparisons.Increasingly, models incorporated insulin, meals, physical activity, demographic characteristics, and other clinical variables to support personalized and multimodal prediction.However, clinical evidence was considerably more limited than technical predictive evidence, with relatively few studies evaluating prospective clinical or decision-support interventions and clinically meaningful outcomes such as time in range or HbA1c.Overall, AI-based approaches demonstrated promising performance for glucose and glycemic event prediction, with a growing emphasis on personalized and multimodal modeling, but evidence for clinical effectiveness remains limited.Stronger prospective, externally validated, and randomized studies using standardized, clinically meaningful outcomes are needed to establish the clinical effectiveness, generalizability, and implementation of AIbased approaches in routine diabetes care.