# A Comprehensive Survey on Surgical Digital Twin

arXiv: https://arxiv.org/abs/2512.00019 · HTML: https://mlsanity.com/paper/2512.00019/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 109 references that are in the map.
- First posted on arXiv 28 Oct 2025.

## Builds on (most cited first, 11 of 11)

- [Digital Twin: Values, Challenges and Enablers](https://mlsanity.com/paper/1910.01719/) (2019)
- [Understanding artificial intelligence ethics and safety](https://mlsanity.com/paper/1906.05684/) (2019)
- [Predictive Digital Twin for Optimizing Patient-Specific Radiotherapy Regimens under Uncertainty in High-Grade Gliomas](https://mlsanity.com/paper/2308.12429/) (2023)
- [Networking Architecture and Key Supporting Technologies for Human Digital Twin in Personalized Healthcare: A Comprehensive Survey](https://mlsanity.com/paper/2301.03930/) (2023)
- [Towards Robust Surgical Automation via Digital Twin Representations from Foundation Models](https://mlsanity.com/paper/2409.13107/) (2024)
- [Privacy-Preserving Operating Room Workflow Analysis using Digital Twins](https://mlsanity.com/paper/2504.12552/) (2025)
- [Managing Latency in Edge-Cloud Environment](https://mlsanity.com/paper/2011.11450/) (2020)
- [Deep Reinforcement Learning in Surgical Robotics: Enhancing the Automation Level](https://mlsanity.com/paper/2309.00773/) (2023)
- [VR Isle Academy: A VR Digital Twin Approach for Robotic Surgical Skill Development](https://mlsanity.com/paper/2406.00002/) (2024)
- [A Digital Twin for Telesurgery under Intermittent Communication](https://mlsanity.com/paper/2411.13449/) (2024)
- [Advancements in Real-Time Oncology Diagnosis: Harnessing AI and Image Fusion Techniques](https://mlsanity.com/paper/2503.11332/) (2025)

## Built on by (most cited first, 1 of 1)

- [Medical world models in healthcare: foundations, applications, and challenges for trustworthy clinical translation](https://mlsanity.com/paper/2607.25242/) (2026)

## Abstract (arXiv)

With the accelerating availability of multimodal surgical data and real-time computation, Surgical Digital Twins (SDTs) have emerged as virtual counterparts that mirror, predict, and inform decisions across pre-, intra-, and postoperative care. Despite promising demonstrations, SDTs face persistent challenges: fusing heterogeneous imaging, kinematics, and physiology under strict latency budgets; balancing model fidelity with computational efficiency; ensuring robustness, interpretability, and calibrated uncertainty; and achieving interoperability, privacy, and regulatory compliance in clinical environments. This survey offers a critical, structured review of SDTs. We clarify terminology and scope, propose a taxonomy by purpose, model fidelity, and data sources, and synthesize state-of-the-art achievements in deformable registration and tracking, real-time simulation and co-simulation, AR/VR guidance, edge-cloud orchestration, and AI for scene understanding and prediction. We contrast non-robotic twins with robot-in-the-loop architectures for shared control and autonomy, and identify open problems in validation and benchmarking, safety assurance and human factors, lifecycle "digital thread" integration, and scalable data governance. We conclude with a research agenda toward trustworthy, standards-aligned SDTs that deliver measurable clinical benefit. By unifying vocabulary, organizing capabilities, and highlighting gaps, this work aims to guide SDT design and deployment and catalyze translation from laboratory prototypes to routine surgical care.