From Prediction to Personalization: A Novel AI Framework for Decoding Black Box Mechanisms and Guiding Treatment Recommendations in Diabetes Care

The problem of diabetes is a serious challenge to the health of the people all over the world since it is reported that there are more than 537 million adulthood cases of diabetes with India recording more than 74 million cases. Traditional paradigms of machine learning in diabetes management (with prediction accuracy of 70-85) are mostly black-box in nature and have low mechanistic explanations and cannot personalize themselves with adaptability. The paper introduces hybrid AI systems based on the combination of multi-omics, clinical, and lifestyle data to offer the mechanistic explainability and personalized recommendations. Using explainable AI, deep reinforcement learning, and graph neural networks as pathway mapping, the proposed system operates in a dynamic manner to simulate the effects of treatments, and adjust the recommendations to patient responses in real-time. The framework in experimental assessment of 12,000 patient dataset demonstrated a predictive accuracy of 92, increased interpretability as indicated by SHAP/XAI scores, and was able to implement personalized interventions that were able to stabilize blood glucose levels within target range to 87% of patients. The framework is scalable to rural and resource constrained environments and congruent with the global health goals, including the WHO targets and Sustainable Development Goals. In addition to diabetes, the system demonstrates possibilities of application to cancer, cardiovascular disease, and obesity which are a step forward to digital precision medicine which involves prediction, mechanistic understanding and adaptive personalization.