An AI-Driven Digital Twin Framework for Personalized Drug Response Prediction and Virtual Treatment Simulation

Abstract

The rapid convergence of Artificial Intelligence (AI), Machine Learning (ML), and Digital Twin technologies is reshaping modern healthcare by enabling precision, patient-specific treatment strategies. Traditional pharmacotherapy operates on population-averaged guidelines that inadequately address the genomic, metabolic, and physiological variability inherent to individual patients, resulting in inconsistent drug responses and preventable adverse events. This paper proposes a novel AI-driven Digital Twin framework that constructs continuously updated virtual patient replicas by integrating Electronic Health Records (EHRs), pharmacogenomic data, wearable Internet of Things (IoT) biosensor streams, and medical imaging. The proposed hybrid prediction engine combines XGBoost ensemble learning, Long Short-Term Memory (LSTM) deep learning, and Transformer-based multimodal fusion to predict personalized drug efficacy and adverse drug reaction (ADR) risk. A Federated Learning protocol enables privacy-preserving multi-institutional model training without centralizing sensitive patient data, while SHAP-based Explainable AI (XAI) modules ensure clinical interpretability. Experimental evaluation on the MIMIC-III clinical database and PharmGKB pharmacogenomics dataset yields a drug response prediction accuracy of 94.7%, ROC-AUC of 0.963, and F1-Score of 0.921, substantially outperforming six established baseline models. The results validate the clinical feasibility and technical superiority of the proposed framework for real-world precision medicine deployment