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

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This review highlights predictive modeling and real-time data assimilation in precision oncology, suggesting improved clinical decision interfaces.

Digital twins enhance cancer treatment by facilitating predictive modeling and real-time data analysis, supporting personalized medicine.
Key technologies evaluated include machine learning and Internet of Medical Things, addressing the challenges of data integration.
The review synthesizes over sixty studies, focusing on frameworks for effective implementation in oncology settings.
Future research should prioritize federated learning and data privacy to enhance scalability in digital twin adoption.

Omolayo et al. (2025) studied this question.

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This review highlights predictive modeling and real-time data assimilation in precision oncology, suggesting improved clinical decision interfaces.