mlsanityData to 14 Oct 2026

The SAGES Critical View of Safety Challenge: A Global Benchmark for AI-Assisted Surgical Quality Assessment

arXiv 2509.17100 ↗First posted 21 Sep 2025cs.CVcited by 6

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Abstract (arXiv)

Advances in artificial intelligence (AI) for surgical quality assessment promise to democratize access to expertise, with applications in training, guidance, and accreditation. This study presents the SAGES Critical View of Safety (CVS) Challenge, the first AI competition organized by a surgical society, using the CVS in laparoscopic cholecystectomy, a universally recommended yet inconsistently performed safety step, as an exemplar of surgical quality assessment. A global collaboration across 54 institutions in 24 countries engaged hundreds of clinicians and engineers to curate 1,000 videos annotated by 20 surgical experts according to a consensus-validated protocol. The challenge addressed key barriers to real-world deployment in surgery, including achieving high performance, capturing uncertainty in subjective assessment, and ensuring robustness to clinical variability. To enable this scale of effort, we developed EndoGlacier, a framework for managing large, heterogeneous surgical video and multi-annotator workflows. Thirteen international teams participated, achieving up to a 17% relative gain in assessment performance, over 80% reduction in calibration error, and a 17% relative improvement in robustness over the state-of-the-art. Analysis of results highlighted methodological trends linked to model performance, providing guidance for future research toward robust, clinically deployable AI for surgical quality assessment.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 33 of its 67 references that are in the map. First posted on arXiv 21 Sep 2025.

Builds on

33 of 67 references.

References · 33

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21 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 6.

6 papers.

  1. 2026Surg-R1: A Hierarchical Reasoning Foundation Model for Scalable and Interpretable Surgical Decision Support with Multi-Center Clinical Validationcited by 2
  2. 2026SurgSigma: A Spectrum of Large-Scale Multimodal Data and Foundation Models for Surgical Intelligencecited by 2
  3. 2026Sum-of-Checks: Structured Reasoning for Surgical Safety with Large Vision-Language Modelscited by 0
  4. 2026AI for Quality Assurance in the Operating Roomcited by 0
  5. 2026SPIRIT: Spatio-temporal Pairwise Relational Modeling of Instrument-Tissue Interactions for Surgical Action Triplet Recognitioncited by 0
  6. 2026A Surgical Foundation Model Reveals Task-Dependent Label Efficiencycited by 0