J Med Internet Res. 2026 Nov 09. 28
e100653
Background: Machine learning models are increasingly used for glycemic event prediction and continuous glucose forecasting, but targets, populations, horizons, validation, and uncertainty reporting remain heterogeneous.
Objective: This study aimed to evaluate machine learning models for glycemic prediction in type 1, type 2, and mixed diabetes populations while distinguishing the full evidence map from the quantitatively analyzable subset.
Methods: We searched 9 English- and Chinese-language databases from inception to April 12, 2026, for studies predicting future glycemic events or glucose values in people with diabetes. Continuous forecasting and binary event prediction were analyzed separately. Root-mean-square error (RMSE) was the principal continuous end point; mean absolute error was supportive. Area under the receiver operating characteristic curve was synthesized using a Bayesian random-effects model because analyzable 2×2 data were sparse. Two reviewers (HY and DA) independently applied PROBAST (Prediction Model Risk of Bias Assessment Tool) to 19 quantitative-source reports (28 assessment units).
Results: We identified 787 records, assessed 310 full texts, and included 299 reports representing 270 studies; 19 reports contributed to quantitative synthesis. The review included 128 type 1, 26 type 2, 78 mixed, and 38 unclear-diabetes-type studies. Pooled RMSE was 1.18 mmol/L (95% CI 1.01-1.34; 37 effects), supportive mean absolute error was 0.90 mmol/L (95% CI 0.78-1.02; 20 effects), and Bayesian pooled area under the receiver operating characteristic curve was 0.926 (95% credible interval 0.877-0.957). Supportive sensitivity and specificity were 0.77 and 0.80. Of 28 PROBAST units, 24 were high risk, 2 unclear, and 2 low. Post hoc exploratory within-report analysis estimated an RMSE increase of 0.481 mmol/L per additional 30 minutes (95% CI 0.213-0.749), but overlap sensitivity reduced certainty. In the 270-study clinical readiness audit, external validation occurred in 4.0% (10/251), prospective evaluation in 0.7% (2/270), calibration in 4.8% (13/270), decision-curve or net-benefit analysis in 2.6% (7/270), and implementation or human-factors evaluation in 2.6% (7/270).
Conclusions: Glycemic prediction studies show forecasting and discrimination signals, but heterogeneity, high risk of bias, incomplete uncertainty reporting, and limited external and prospective validation preclude deployable benchmarks. Pooled estimates should be viewed as summaries of analyzable literature. Future studies need transparent horizon, threshold, calibration, validation, uncertainty, and implementation reporting.
Keywords: artificial intelligence; blood glucose forecasting; continuous glucose monitoring; diabetes mellitus; hyperglycemia; hypoglycemia; machine learning; meta-analysis; systematic review; type 1 diabetes; type 2 diabetes