Digital Twin Frameworks for Simulating Multiscale Patient Physiology in Precision Oncology: A Review of Real-Time Data Assimilation, Predictive Tumor Modeling, and Clinical Decision Interfaces

Digital twin (DT) technology has emerged as a transformative paradigm in precision oncology, enabling real-time, multiscale simulation of patient-specific physiological processes to support individualized cancer treatment. By integrating heterogeneous data sources—including genomic, proteomic, imaging, and clinical data—digital twins facilitate predictive tumor modeling and dynamic treatment optimization. This review explores current frameworks for implementing digital twins in oncology, emphasizing their role in assimilating real-time data for predictive modeling and enhancing decision-making interfaces in clinical settings. Key enabling technologies such as machine learning, Internet of Medical Things (IoMT), cloud platforms, and hybrid computational models are evaluated. In addition, the review highlights the importance of aligning data flow with clinical workflows through the use of modular architectures, dynamic simulation algorithms, and explainable AI. Particular attention is given to the challenges of interoperability, data privacy, and validation of simulation fidelity across patient populations. Drawing from over sixty foundational studies—including those on advanced analytics, business intelligence frameworks, and cyber-physical system design—this work synthesizes a cross-disciplinary body of literature to outline critical pathways for the successful deployment of DT systems in oncology care. The findings suggest that future research should focus on federated learning, semantic data integration, and regulatory alignment to foster the scalable adoption of digital twins in personalized medicine.