Advancing Type 2 Diabetes Management: Digital Twin and AI for Dynamic Dosing and Nutrition

Abstract

Type 2 Diabetes (T2D) is a chronic metabolic disorder caused by insulin resistance and dysfunctional glucose regulation that affects millions of individuals worldwide. However, managing T2D is challenging because blood glucose levels fluctuate due to diet, exercise, and patients’ idiosyncrasies. Approaches such as real-time monitoring, predictive modeling, and personalized treatment have been among the major breakthroughs in the field. Still, their holistic integration for dynamic dosing and dietary optimization has remained largely unexplored. This research analyzes insights from recent research that reveal different methodologies such as machine learning (ML), deep learning (DL), and predictive models in improving diabetes management outcomes. Some studies showed up to 1.5% HbA1c reduction and time-in-range improvement by 20% to 25% due to artificial intelligence (AI) driven interventions. The accuracy of blood glucose prediction in these studies ranged from 85% to 95%, thus showing that these models can control blood sugar levels at any given moment. The preliminary results show that dynamic dosing models combined with dietary recommendations may achieve a 0.8% to 1.2% decrease in HbA1c and a 10% to 15% improvement in overall glycemic control. This study investigates the applications of Digital Twin (DT) technology, Reinforcement Learning (RL), and Generative AI (Gen AI) in diabetic control systems. The proposed model adopts these strategies to illustrate the possibility of connecting the existing research gaps and prepare for future developments on adaptive AI-powered framework for diabetes care.

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