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[2602.21706] SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video
Summary
The paper presents SurGo-R1, a model designed to enhance contextual reasoning in surgical video analysis, addressing challenges in identifying safe operative zones during minimally invasive surgeries.
Why It Matters
This research is crucial as it tackles the complex task of ensuring safety in surgical procedures by integrating AI with visual and contextual cues. The development of a new benchmark and model could significantly improve surgical outcomes and training methodologies.
Key Takeaways
- SurGo-R1 improves contextual reasoning in surgical video analysis.
- The model achieves significant performance improvements over existing vision-language models.
- A new benchmark for laparoscopic frames has been established to evaluate surgical safety.
- Phase-dependent reasoning is critical for enhancing surgical safety.
- The research highlights the limitations of current AI systems in dynamic surgical environments.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.21706 (cs) [Submitted on 25 Feb 2026] Title:SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video Authors:Guanyi Qin, Xiaozhen Wang, Zhu Zhuo, Chang Han Low, Yuancan Xiao, Yibing Fu, Haofeng Liu, Kai Wang, Chunjiang Li, Yueming Jin View a PDF of the paper titled SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video, by Guanyi Qin and 9 other authors View PDF HTML (experimental) Abstract:Minimally invasive surgery has dramatically improved patient operative outcomes, yet identifying safe operative zones remains challenging in critical phases, requiring surgeons to integrate visual cues, procedural phase, and anatomical context under high cognitive load. Existing AI systems offer binary safety verification or static detection, ignoring the phase-dependent nature of intraoperative reasoning. We introduce ResGo, a benchmark of laparoscopic frames annotated with Go Zone bounding boxes and clinician-authored rationales covering phase, exposure quality reasoning, next action and risk reminder. We introduce evaluation metrics that treat correct grounding under incorrect phase as failures, revealing that most vision-language models cannot handle such tasks and perform poorly. We then present SurGo-R1, a model optimized via RLHF with a multi-turn phase-then-go architecture where the model first identifies the surgical phase, then gener...