AI RESEARCH
Sum-of-Checks: Structured Reasoning for Surgical Safety with Large Vision-Language Models
arXiv CS.LG
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ArXi:2604.22156v1 Announce Type: new Purpose: Accurate assessment of the Critical View of Safety (CVS) during laparoscopic cholecystectomy is essential to prevent bile duct injury, a complication associated with significant morbidity and mortality. While large vision-language models (LVLMs) offer flexible reasoning, their predictions remain difficult to audit and unreliable on safety-critical surgical tasks. Methods: We Results: Sum-of-Checks improves average frame-level mean average precision by 12--14% relative to the best baseline across all three models and criteria.