Human-in-the-loop AI predictive digital twin to extend virtual precision diabetes care between visits.

In a 6-month randomized trial, we evaluated a digitally enabled "human-in-the-loop" care support model using a predictive artificial intelligence (AI) digital twin to provide personalized daily short message service (SMS) feedback for adults with type 2 diabetes (T2D). The parent study enrolled 40 adults aged ≥18 years with T2D who completed 3 months of baseline observation followed by a 3-month intervention period, generating 6467 longitudinal data points across weight, dietary intake, physical activity, and glucose monitoring (mean follow-up: 174 days). For this ancillary AI intervention, a subset of 19 participants was randomized to receive either AI-generated individualized daily feedback (AI group, n = 10) or no daily feedback (control group, n = 9). The online human-in-the-loop predictive control model incorporated a transfer-learning artificial neural network predictive digital twin trained on participant self-monitoring data, including weight, food logs, physical activity, and glucose values. A particle swarm optimization controller identified personalized behavioral recommendations aligned with glucose and weight goals, and the digital twin was retrained weekly using newly accrued data. The model achieved ≥80% prediction accuracy across all diet-condition subgroups. During the intervention period, participants receiving AI-generated feedback demonstrated trends toward increased daily step counts and improved adherence to caloric and carbohydrate intake targets. The AI intervention group achieved significantly greater weight loss than controls (mean loss 5.87 lbs vs 3.57 lbs; p < 0.012) while maintaining stable glucose levels throughout the study period (p = 0.661). These findings suggest that AI-enabled predictive digital twin models may offer a scalable approach for extending precision diabetes self-management support beyond clinic visits.