Cognitive Digital Twins in Surgery: Insights from an International Multidisciplinary Workshop
8 affiliations
- 1Department of Industrial Engineering, University of Trento, Trento, ITALY.
- 2Department of Information Engineering and Computer Science, University of Trento, Trento, ITALY.
- 3IMT School for Advanced Studies Lucca, Lucca, ITALY.
- 4Surgical Sabermetrics Laboratory (SaberLab), University of Edinburgh, Edinburgh, SCOTLAND.
- 5Ken Kennedy Institute, Rice University, Houston, TEXAS.
- 6Department of Surgery & Cancer - Faculty of Medicine, Imperial College London, London, UNITED KINGDOM.
- 7Department of Civil and Environmental Engineering, Northeastern University, Boston, MA, USA.
- 8Division of Cardiac Surgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Abstract
A multidisciplinary workshop, 'Toward Cognitive Digital Twins in Surgery,' was held during the Hamlyn Symposium in London, UK on Friday, June 27, 2025. This work summarizes the main insights that emerged from the discussion, including the potential benefits and limitations of Cognitive Digital Twins (CogDT) in surgery. The CogDT framework is designed to capture the entire clinical system, including patients and healthcare providers. We introduce the novel concept of the Surgical Cognitive Digital Twin (S-CogDT), highlighting its potential for advancement in surgical care. Participants conceptualized S-CogDTs as potential tools for creating dynamic, individualized profiles for personalized provider training, serving as real-time early-warning indicators to mitigate cognitive overload, and optimizing organizational workflows within the surgical environment. However, the effective adoption of this technology faces significant challenges. These include the difficulty of measuring and validating dynamic cognitive states, the considerable hurdles in achieving user acceptance and trust, and critical questions surrounding data governance, privacy, and regulatory pathways. These results inform future research in this novel, multidisciplinary field by clearly showing the potential of S-CogDTs and mapping the potential challenges required for their successful implementation.
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BibTeX
@article{fruet2026cognitive,
title = {{Cognitive Digital Twins in Surgery: Insights from an International Multidisciplinary Workshop}},
author = {Damiano Fruet and Marco Bombieri and Erica Ordali and Steven Yule and Vaibhav Unhelkar and Daniel Leff and George Mylonas and Pietro Pietrini and Gilbert Yang Ye and Ravi Naik and Giandomenico Nollo and Roger Daglius Dias and Marco A Zenati},
journal = {IEEE transactions on medical robotics and bionics},
year = {2026},
month = oct,
doi = {10.1109/tmrb.2026.3742591},
url = {https://europepmc.org/article/PMC/PMC13727292},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Cognitive Digital Twins in Surgery: Insights from an International Multidisciplinary Workshop
AU - Damiano Fruet
AU - Marco Bombieri
AU - Erica Ordali
AU - Steven Yule
AU - Vaibhav Unhelkar
AU - Daniel Leff
AU - George Mylonas
AU - Pietro Pietrini
AU - Gilbert Yang Ye
AU - Ravi Naik
AU - Giandomenico Nollo
AU - Roger Daglius Dias
AU - Marco A Zenati
PY - 2026
DA - 2026/10/16
JO - IEEE transactions on medical robotics and bionics
DO - 10.1109/tmrb.2026.3742591
UR - https://europepmc.org/article/PMC/PMC13727292
AB - A multidisciplinary workshop, 'Toward Cognitive Digital Twins in Surgery,' was held during the Hamlyn Symposium in London, UK on Friday, June 27, 2025. This work summarizes the main insights that emerged from the discussion, including the potential benefits and limitations of Cognitive Digital Twins (CogDT) in surgery. The CogDT framework is designed to capture the entire clinical system, including patients and healthcare providers. We introduce the novel concept of the Surgical Cognitive Digital Twin (S-CogDT), highlighting its potential for advancement in surgical care. Participants conceptualized S-CogDTs as potential tools for creating dynamic, individualized profiles for personalized provider training, serving as real-time early-warning indicators to mitigate cognitive overload, and optimizing organizational workflows within the surgical environment. However, the effective adoption of this technology faces significant challenges. These include the difficulty of measuring and validating dynamic cognitive states, the considerable hurdles in achieving user acceptance and trust, and critical questions surrounding data governance, privacy, and regulatory pathways. These results inform future research in this novel, multidisciplinary field by clearly showing the potential of S-CogDTs and mapping the potential challenges required for their successful implementation.
SN - 2576-3202
ER -