Digital Twin Modeling for AI-Based Personalized Drug Response Prediction and Virtual Treatment Simulation: A Literature Review
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Abstract
The rapid advancement of Artificial Intelligence (AI), Machine Learning (ML), and Digital Twin technologies has transformed modern healthcare systems toward precision and personalized medicine. Traditional treatment approaches often fail to account for patient-specific biological variations, resulting in inconsistent drug responses and ineffective treatment outcomes. Digital Twin modeling provides a promising solution by creating virtual replicas of patients using real-time physiological, genetic, and clinical data. These virtual models can simulate disease progression, predict drug responses, and optimize treatment strategies before actual clinical implementation. This literature review explores recent advancements in AI-based personalized drug response prediction and virtual treatment simulation using Digital Twin technology. The paper analyzes existing methodologies, machine learning algorithms, data integration techniques, and simulation frameworks used in healthcare Digital Twins. Furthermore, it identifies major research gaps including data privacy concerns, interoperability issues, computational complexity, and limited real-time adaptability. The review concludes with future research directions involving Explainable AI, Federated Learning, IoT-enabled healthcare systems, and multi-organ Digital Twin architectures for improving precision medicine and patient care.
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