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About

Lilian Calvet is a researcher at the forefront of medical imaging and surgical navigation, with a focus on integrating artificial intelligence and augmented reality into orthopedic and laparoscopic procedures. Her major contributions lie in overcoming practical barriers to the adoption of advanced surgical technologies, particularly through domain adaptation strategies for 3D reconstruction. In her most-cited work (2024, 10 citations), she addresses critical limitations in surgical navigation—such as time, cost, radiation exposure, and workflow integration—by applying domain adaptation to reconstruct the lumbar spine from real fluoroscopy data, building on her earlier X23D framework. This work demonstrates a clear path toward more accessible and efficient intraoperative guidance. Calvet also contributed to the preliminary trial of augmented reality in robot-assisted laparoscopic partial nephrectomies (2019, 3 citations), showcasing her versatility across surgical specialties. Her research is notable for its translational impact, directly targeting the hurdles that prevent novel imaging and navigation tools from reaching routine clinical use. For students and researchers, Calvet’s work exemplifies how AI-driven solutions can bridge the gap between laboratory innovation and real-world surgical practice.