Overview

Digital twin (DT) is an emerging frontier in healthcare, offering dynamic, patient-specific virtual models that combine medical imaging, physiological data, and computational simulations to replicate and predict real-world health outcomes. While DT technology is still nascent in the MICCAI community, its potential to transform personalized medicine, surgical planning, and clinical decision-making is immense. This workshop seeks to establish DTs as a vibrant new topic within the MICCAI community, bridging interdisciplinary domains like medical imaging, computational modeling in medicine, biomechanics, and trustworthy AI.

By introducing this workshop, we aim to catalyze discussions on integrating DT models with more conventional medical imaging and computational tools, providing a forum to explore innovations and build a new MICCAI sub-community dedicated to digital twins in healthcare. This workshop is strategically positioned to expand MICCAI’s scope by attracting researchers from fields such as computational modeling, electrophysiology, hemodynamic, biomechanical modeling, and sensor data analysis. DTs not only align with MICCAI topics like image-guided surgery, personalized medicine, and trustworthy AI, but also open doors to underexplored applications, including real-time patient monitoring and population imaging informatics. The workshop also emphasizes inclusivity by addressing affordable and accessible imaging solutions for DTs, especially in under-represented populations.

Topic

Digital twin modeling for organs, tissues, and diseases
Multi-modal data integration (imaging, signals, genomics, biosensors)
AI-driven simulation and predictive modeling for digital twins
Digital twin-guided diagnosis, treatment, and intervention planning
Verification, Validation, and uncertainty quantification in digital twins
Trustworthy AI, fairness, and explainability in digital twin applications
Cloud-based and federated learning approaches for scalable digital twins
Ethical, regulatory, and clinical translation challenges of digital twins
Digital twins for drug development and in silico clinical trials

Timeline

May

Paper Abstract Submission. 20 May 2025

June

Paper Submission Deadline. 15 June 2025

July

Reviews Due. 15 July 2025

Notification of Acceptance. 20 July 2025

Aug

Camera Ready Submission. 1 August 2025

Sep

DT4H workshop (Daejeon, Republic of Korea). 23/27 September 2025

Call for paper

We invite original research contributions on digital twins for healthcare. Papers should be a maximum of 8 pages (including text, figures, and tables) with up to 2 additional pages for references. Submissions must follow the Springer LNCS format (Latex or MS Word templates) and undergo double-blind peer review. The workshop will be held in person only, and authors of accepted papers are required to present their work in person.

All submissions will be reviewed by external experts and the organizing committee for oral or poster presentation. Accepted papers will be published with Springer LNCS, and the best papers will be recognized with industry-sponsored awards. We encourage both theoretical and applied research, including proof-of-concept studies that explore novel directions in digital twins. As MICCAI expands its global reach, we especially welcome submissions that address challenges in digital twins for low- and middle-income countries. This workshop will also feature live demonstrations, allowing authors to showcase their digital twin applications in AI-driven modeling, real-time simulations, and clinical decision support.

Submissions should be made via CMT system, with email registration required before submission. If you experience any issues, please contact the Program Chairs via the submission platform or email. We invite contributions on topics such as:

Development of digital twins using medical imaging data.
Integrating imaging and non-imaging data for dynamic digital twins.
Computational modeling of specific diseases (e.g., cardiovascular, neurology, oncology, pulmonology oncology, etc).
AI techniques for digital twin efficiency and accuracy.
Validation and deployment challenges for digital twins in clinical practice.

Program

Keynote Speaker

Linwei Wang

Professor, Rochester Institute of Technology

TALK

🚧 To be Added

BIOGRAPHY

Dr. Linwei Wang is a Professor of Computing and Information Sciences at the Rochester Institute of Technology (RIT) in Rochester, NY, where she serves as the Director of the Personalized Healthcare Technology (PHT180) Research Center that consists of over 120 faculty affiliates across nine colleges at RIT. Prior to that, Dr. Wang obtained her BS degree in Optic-Electrical Engineering from Zhejiang University (China) in 2005, her M.Phil degree in Electronic and Computer Engineering from Hong Kong University of Science and Technology in 2007, and her PhD in Computing and Information Sciences from RIT prior to joining the faculty of RIT in 2009. Dr. Wang also directs the Computational Biomedical Lab at RIT, with core research interests centered around statistical inference, Bayesian deep learning, and inverse problems with applications to signal and image analysis in a variety of domains including healthcare, astrophysics, and material design. Dr. Wang is a recipient of the NSF CAREER Award in 2014 and the United States Presidential Early Career Award for Scientists and Engineers (PECASE) in 2019. Dr. Wang currently serves as the Executive Secretary on the Board of the Medical Image Computing and Computer-Assisted Intervention (MICCAI) Society.

Mathias Unberath

Associate Professor, Johns Hopkins University

TALK

Digital Twins for Surgical Data Science

Digital twins are virtual representations of real-world environments and processes. By virtue of being computational, digital twins offer abundant possibilities for optimizing and supporting real-world processes through algorithmic analysis and feedback. In this context, digital twins play a critical role in advancing surgical data science, an emerging research thrust striving to improve the quality of interventional healthcare. In this talk, I will first provide a contextual framework in support of digital twins as a useful representation for scene analysis and then showcase some exciting research outcomes and opportunities that demonstrate the potential for digital twins in surgical data science and beyond.

BIOGRAPHY

Dr. Mathias Unberath is the John C. Malone Associate Professor of Computer Science at Johns Hopkins University. He’s the Research Director for Embodied AI in the Data Science and AI Institute, core member of the Malone Center for Engineering in Healthcare and the Laboratory for Computational Sensing and Robotics, and a member of the Institute for Assured Autonomy. He holds secondary appointments in the School of Medicine. With his team, the ARCADE research group on Advanced Robotics and Computationally AugmenteD Environments, he builds the future of AI-assisted medicine. Through synergistic research on imaging, computer vision, machine learning, and interaction design, he creates human-centered solutions that are embodied in emerging technology such as mixed reality and robotics. Mathias has published more than 190 journal and conference articles, and has received numerous awards, grants, and fellowships, including the NSF Career, NIH NIBIB R21 Trailblazer and Google Research Scholar Award, and more than 20 international paper awards.