Plenary Lectures and VIP Guests

Dr. Nguyen Tran

Abstract:

Minimally invasive surgery is going through its fastest transformation since the introduction of laparoscopy. The end of the robotic monopoly, the rise of augmented and image-guided surgery, and the arrival of artificial intelligence in the operating room are reshaping not only how we operate, but what it means to be competent. Yet surgical education still largely relies on a model built for a slower world: apprenticeship, case volume and time spent in training. That model is now under strain, with fewer learning opportunities in the operating room, multiple platforms to master, and growing pressure for safety and accountability.

This lecture argues that simulation must change in nature. It can no longer be a mere rehearsal space; it must become a system that structures, measures and predicts skill. Drawing on fifteen years of robotic surgery training in Nancy, we will show how a progressive, validated curriculum, from virtual reality to high-fidelity wet lab, ensures the transfer of skills to real practice, and how performance data can identify early warning signals of failure long before a final assessment. This is where artificial intelligence finds its most promising role in education: not replacing the trainer, but making learning personalized, objective and continuous.

We will also present a translational experience, to our knowledge unique: transferring this human curriculum to veterinary surgeons, an initiative that has helped bring robotic surgery into veterinary practice on several continents. It shows that training can do more than support innovation; it can create new surgical practices.

Finally, we will discuss what lies ahead: automated assessment, adaptive learning pathways, and a shift from one-time certification to lifelong, data-driven credentialing. Training, in this new era, means measuring, predicting and transmitting across disciplines, platforms and species.