Description

Advances in physics-informed and data-driven systems, together with novel robotic technologies are equipping medical robots with increasingly autonomous and intelligent capabilities. These systems require accurate, high-fidelity twin models that provide reliable environments for design, testing, optimisation, and validation, paving the way towards personalised medical robotic solutions.

This workshop aims to bring together researchers, clinicians, and industry experts to explore how physics- and AI-driven digital twin technologies can enable patient-specific diagnoses, interventions, and therapies. By integrating high-fidelity modelling, multimodal sensing, and adaptive control, twin-enabled systems hold the potential to transform surgical robotics, diagnostic imaging, interventional procedures, and therapeutic robotics.

The workshop will examine recent progress in patient-specific anatomical, biomechanical, and physiological modelling, particularly of the brain; real-time data assimilation from visual, force, and physiological sensors; and the development of physical twin platforms for capturing robot–tissue dynamics. It will address the role of digital twins in diagnostic and image-guided robotic systems, including predictive interpretation of multimodal imaging, automated disease characterisation, and personalised procedural planning.

Digital twins are also essential for generating patient-specific behaviours through reinforcement learning and emerging foundation models. Twin-driven learning environments enable robots to acquire and refine skills in realistic settings tailored to individual patients. Applications in surgical and interventional robotics include twin-assisted trajectory optimisation, predictive closed-loop control, and high-fidelity simulation platforms for training, evaluating, and validating autonomous or semi-autonomous robotic systems.

Finally, the workshop will highlight the emerging role of digital and physical twins in enhancing human–robot physical and cognitive interaction. Topics include safety, risk-aware control, predictive and adaptive interaction driven by user- and patient-specific models, personalised therapeutic robotics, and shared autonomy frameworks that integrate intent prediction and skill adaptation.

Overall, the workshop will provide a multidisciplinary forum to discuss foundational methods, translational challenges, and future directions for physics- and AI-driven digital twins in personalised medical robotics.

Poster Competition

We are delighted to announce that NVIDIA will sponsor the Workshop Best Poster Award, with the winner receiving an NVIDIA DGX Spark Personal AI Supercomputer worth almost £5,000! This cutting-edge GPU platform is designed to accelerate AI development and experimentation, providing exceptional support for next-generation research and innovation.

We are now accepting poster submissions for this workshop.

Submission Deadline: 21 June 2026
Notification of Acceptance: 22 June 2026

Programme

Learning Outcomes

By the end of the workshop, participants will be able to:

1. Understand how physics and AI and support creating digital twins for patient-specific anatomy, biomechanics, and physiology;
2. Describe the role of multimodal sensing and real-time data assimilation in maintaining accurate, high-fidelity twins during robotic tasks;\
3. Recognise the importance of twin-based environments for designing, testing, optimising, and validating emerging robotic technologies, including autonomous and semi-autonomous capabilities.
4. Exploit accurate twin environments with AI-based methods that support personalised diagnosis, intervention planning, and therapeutic procedures.
5. Analyse applications of twins in surgical and interventional robotics, including trajectory optimisation, predictive closed-loop control, and simulation-based training and evaluation.
6. Evaluate how twin-informed representations enhance human–robot interaction through adaptive assistance, personalised therapeutic robotics, and shared autonomy frameworks.
7. Identify key methodological and translational challenges in integrating physics- and AI-driven digital twins into clinical workflows for personalised medical robotics.

Sponsor This Workshop -Physics- and AI-Driven Digital Twins for Patient-Specific Robotic Diagnosis, Interventions and Therapies

Suggested Price: £500.00

Sponsor This Workshop for £500

As an official Workshop Sponsor, sponsors will receive:

• Prominent logo placement on the workshop webpage
• Acknowledgement in workshop-related social media posts (LinkedIn, X, and Instagram)/and cross-posting from organiser accounts
• Recognition on prize certificates presented to participants

Sponsorship contributions will support essential workshop operational costs, including speaker travel and accommodation, poster prizes, and participant registration fees.

 

Please note that workshop sponsorship is separate from the symposium sponsorship packages and does not include exhibition or live demonstration opportunities.

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