Joint Therapy-Imaging Digital Twins in Medicine: Transforming Modeling, Personalization, and Prediction
Description
Digital twins—computational replicas of patients, organs, or biological systems—are redefining how we model disease, predict treatment response, and personalize medical care. By integrating imaging, biological, and clinical data with physics-based and AI-driven models, digital twins enable dynamic simulation of patient outcomes under varying therapeutic scenarios. For medical physicists, this represents not only a natural extension of their modeling and dosimetry expertise but also a transformative framework for precision medicine. This symposium will explore the scientific foundations, current progress, and future directions of digital twins across medical physics and healthcare. The first talk will provide an overview of the digital twin concept and its relevance to radiology and radiation therapy, highlighting how multiscale modeling—from molecular to organ to whole-body levels—can unify imaging, dosimetry, and biology. The second talk will discuss computational and data infrastructure challenges, including scalable simulation workflows, HPC integration, and secure handling of multimodal clinical data. The third talk will focus on real-world applications, demonstrating use cases in radiation therapy optimization, imaging-based prediction of biological response, and radiopharmaceutical dosimetry. Together, these talks will illustrate how digital twins can advance the field from retrospective analysis to predictive simulation, offering a powerful scientific tool for patient-specific planning, risk assessment, and outcome optimization. The symposium will also discuss how physicists can lead this rapidly evolving frontier through collaboration, computation, and clinical insight.