Design of an Integrated Model for Neuro Symbolic GenAI and RAG Driven Personalized Cardiometabolic Care Sets

Abstract: Increasing demand for clinically trustworthy, patient-specific decision systems has highlighted a gap between GenAI models and cardiometabolic care, where symptoms fluctuate gradually, data streams change hourly, and therapies must be ethical. Fragmented multimodal integration, shallow personalization, and confident but unsupported suggestions plague existing techniques. Due to these limitations, clinicians may generate technically impressive work that is hard to justify or apply to everyday hypertension and type 2 diabetes treatment. GenAI + RAG learns from heterogeneous data and follows causal, ethical, and evidentiary paths. It solves long-standing difficulties using a context-aware multimodal learning engine, retrieval-grounded generative reasoning, and five analytical methodologies in its validation pipeline. Salt sensitivity and nighttime heart-rate variability are examined for persistence using Multiscale Causal Uncertainty Stratification Analysis. Dynamic Ethical Constraint Verification Engine then assesses ethical restrictions' effects. Hierarchical Evidential RAG Stress-Test Simulation evaluates retrieval under conflicting clinical guidelines. A Persona- Calibrated Recommendation Consistency Audit checks for lifestyle or medication inconsistencies among patient archetypes. The last layer, Longitudinal Personalized Improvement Prediction Benchmark, predicts six-month A1C, systolic blood pressure, and marker trajectories from these suggestions. Causality, contextual explainability, and long- term clinical relevance appear to improve with the architecture. It may imply a generation of medical AI systems that reason modestly, trace their conclusions, and offer clinicians evidence-based pathways rather than isolated predictions.

Keywords: GenAI, RAG Systems, Cardiometabolic Care, Multimodal Learning, Explainable AI, Analysis.