AI-Powered Personalized Dietary Recommender for Insulin Resistance Management

Insulin resistance (IR) is a major risk factor for type 2 diabetes and cardiovascular disease, yet day-to-day dietary management remains largely manual and generic. While recent advances have produced AI-based tools for glucose prediction and metabolic modeling, current systems are typically static, population-level, and do not close the loop between real-time glucose feedback and personalized dietary recommendations. Moreover, explainability and privacy are often treated as afterthoughts, limiting user trust and large-scale deployment. This paper proposes an AI-powered, privacy-aware dietary recommendation system for adults with insulin resistance that integrates continuous glucose monitoring (CGM), meal logs, and lifestyle data. The system combines supervised models for postprandial glycemic response prediction with a reinforcement learning (RL) policy for adaptive meal planning, multi-modal data fusion for richer context, and explainable AI techniques to improve user understanding and adherence. Privacy is enforced through federated learning and differential privacy. We outline the data pipeline, model architecture, learning and deployment strategy, and an evaluation plan based on both machine learning metrics (MAE, RMSE) and clinical endpoints (time-in-range, HOMA-IR). The proposed framework aims to bridge scientific gaps in real-time, personalized, and privacy-preserving nutritional decision support for insulin resistance management.