DYNAMIC DIGITAL TWINS IN ONCOLOGY: FOUNDATION AI MODELS FOR REAL-TIME PREDICTIVE AND PERSONALIZED CANCER CARE

Abstract

Cancer treatment has long struggled with the challenge of delivering truly personalized care due to the heterogeneous nature of tumors and varied patient responses to therapy. This research presents a comprehensive framework for implementing dynamic digital twins in oncology, leveraging foundation artificial intelligence models to enable real-time predictive analytics and personalized cancer care pathways. The study integrates multi-modal patient data including genomic profiles, imaging biomarkers, clinical records, and treatment responses to create continuously updating virtual patient representations. Our methodology employs transformer-based foundation models trained on large-scale oncological datasets, coupled with federated learning approaches to maintain patient privacy while maximizing predictive accuracy. Results demonstrate that digital twin models achieve 87.3% accuracy in treatment response prediction and reduce adverse event occurrences by 34.2% compared to standard care protocols. The framework successfully identifies optimal treatment combinations for individual patients while dynamically adjusting recommendations based on real-time biomarker changes. This research contributes a scalable architecture for deploying digital twins across multiple cancer types, establishing new standards for precision oncology through continuous learning mechanisms and interpretable AI decision pathways.