Editorial draft · This page remains noindex until author approval.
Discussions of artificial intelligence in surgery often begin with accuracy. The more important clinical question is whether a system can explain which anatomical state and operative action informed its judgment, and how that judgment relates to patient safety and outcomes. Verifiable Surgery begins with that question.
The research trajectory follows the same direction: from post-transplant clinical outcomes, to structuring preoperative anatomy with MRI and CT, to recognizing biliary anatomy in real laparoscopic surgery, and to validating intraoperative navigation across institutions. The Surgical Data Factory connects these projects as components of an operating-room learning infrastructure.
The goal is not automation for its own sake. The goal is to make the operative process measurable, structured, linked to outcomes, and repeatedly testable. Autonomy is meaningful only within the boundaries established by that verification.
Connected public evidence
- Physical AI goes to the operating room: are we ready for the Surgical Data Factory?
- AI-assisted intraoperative navigation for safe right liver mobilization in pure laparoscopic donor hepatectomy: an experimental multi-institutional validation study
- Comprehensive deep learning-based assessment of living liver donor CT angiography: from vascular segmentation to volumetric analysis
- Improved recurrence-free survival in patients with HCC with post-transplant plasma exchange