DiaGuardianAI

DiaGuardianAI is a research framework for continuous, intelligent, and safe diabetes management. It integrates advanced machine learning models, synthetic patient simulation, and reinforcement learning to optimize insulin delivery and predict glucose levels with clinical precision. The core of the system is a multi-agent architecture that assigns specialized roles to predictive, decision, safety, and pattern-recognition agents. A key innovation of DiaGuardianAI is its infinite training loop: a continuous learning mechanism that adapts to individual physiological dynamics while ensuring robust safety guarantees. The system incorporates uncertainty quantification, physiologically stratified experience replay, and safety-constrained policy optimization to mitigate risks such as hypoglycemia. Experimental results using 1,000 synthetic patients over 30 simulated days demonstrate significant improvements in time-in-range (TIR), prediction accuracy, and safety compared to standard AID systems. The framework is modular, extensible, and built with reproducibility in mind. It is designed for researchers exploring AI-driven personalized medicine, multi-agent coordination in health systems, and reinforcement learning under clinical constraints. This version is intended for research and educational purposes only and is not approved for clinical use.