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arxiv: 2506.22500 · v2 · submitted 2025-06-25 · 💻 cs.CV · cs.AI

OR-VSKC: Resolving Visual-Semantic Knowledge Conflicts in Operating Rooms with Synthetic Data-Guided Alignment

Weiyi Zhao , Xiaoyu Tan , Liang Liu , Sijia Li , Youwei Song , Xihe Qiu This is my paper

Pith reviewed 2026-05-19 07:33 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords Visual-Semantic Knowledge ConflictsOperating Room SafetySynthetic DataMultimodal Large Language ModelsSurgical Risk DetectionBenchmark DatasetModel Alignment
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The pith

Fine-tuning multimodal models on the OR-VSKC synthetic benchmark reduces visual-semantic knowledge conflicts and improves detection of operating room safety violations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces OR-VSKC, a benchmark of synthetic images designed to study and correct cases where multimodal models know surgical safety rules but fail to apply them when viewing images. Real operating room data is scarce and privacy-restricted, so the authors use a generative framework to create images grounded in actual safety standards and existing OR datasets. Evaluations show that leading models have large gaps on this task, yet fine-tuning on the new benchmark closes those gaps and allows the models to handle new camera angles they were not trained on. A sympathetic reader would care because reliable automated safety checks could directly reduce risks to patients in strictly regulated environments.

Core claim

OR-VSKC is a benchmark built through a Protocol-to-Pixel Generative Framework that produces 28,190 high-fidelity synthetic images plus a 713-image expert-validated challenge set drawn from real OR contexts in the 4D-OR and CAMMA-MVOR datasets. State-of-the-art multimodal models exhibit substantial reliability gaps in activating safety knowledge during visual inspection. Fine-tuning on OR-VSKC mitigates these visual-semantic knowledge conflicts and enables robust generalization to unseen camera viewpoints.

What carries the argument

The Protocol-to-Pixel Generative Framework, which converts authoritative safety protocols and real OR scene contexts into high-fidelity synthetic images that preserve both visual features and violation semantics.

If this is right

  • State-of-the-art multimodal models display substantial reliability gaps when identifying safety violations from OR images.
  • Fine-tuning on the OR-VSKC benchmark reduces visual-semantic knowledge conflicts.
  • Models trained this way generalize to images captured from previously unseen camera viewpoints.
  • The benchmark supports external validation through its CAMMA-MVOR-derived portion.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same protocol-driven synthetic generation method could address similar knowledge conflicts in other privacy-sensitive visual inspection domains such as radiology or emergency response.
  • Improved models might be combined with live video feeds to provide real-time safety alerts during procedures.
  • Cross-dataset results suggest the alignment technique may remain stable when deployed across different hospital camera setups.

Load-bearing premise

The synthetic images match the visual appearance and safety-violation semantics of real operating room scenes closely enough that training gains transfer to actual clinical use.

What would settle it

Measure whether a model fine-tuned on OR-VSKC identifies safety violations more accurately than the base model when tested on a fresh collection of real, non-synthetic operating room images.

Figures

Figures reproduced from arXiv: 2506.22500 by Liang Liu, Sijia Li, Weiyi Zhao, Xiaoyu Tan, Xihe Qiu, Youwei Song.

Figure 1
Figure 1. Figure 1: Example of visual-semantic knowledge conflicts [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the OR-VSKC Dataset Generation and VS-KC Inspection Framework: (a) Constructing specifications [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Impact of LoRA Fine-tuning on Detection Accu [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Examples of Generated Images merely overfitting to specific canonical views but can generalize their understanding across different lines of sight. Together, these efforts in diversifying entity placement and viewpoints contribute to a rich and challenging dataset for thoroughly investigating and addressing VS-KC in MLLMs. A.3 Details of the Human-Annotated Subset To complement the extensive AI-generated c… view at source ↗
Figure 5
Figure 5. Figure 5: Composition of the OR-VSKC dataset components by conflict category. Left: AI-Generated Test Set (N=9,118 images), [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
read the original abstract

Automated identification of surgical safety risks is critical for improving patient outcomes; however, Multimodal Large Language Models (MLLMs) frequently suffer from Visual-Semantic Knowledge Conflicts (VS-KC), a phenomenon where models possess safety knowledge but fail to activate it during visual inspection. Investigating this alignment gap in operating rooms (ORs) is impeded by a critical bottleneck: the scarcity and privacy constraints of real-world OR data depicting safety violations. To address this, we introduce OR-VSKC, a benchmark for studying VS-KC and surgical risk perception in strictly regulated OR environments. Constructed via our Protocol-to-Pixel Generative Framework, OR-VSKC comprises 28,190 high-fidelity synthetic images grounded in authoritative safety standards, complemented by a 713-image expert-authored challenge subset validated by multiple experts. The full benchmark is built from authentic OR contexts drawn from the 4D-OR and CAMMA-MVOR datasets, where the 4D-OR-based portion serves as the primary benchmark core and the CAMMA-MVOR-based portion is reserved for external validation and cross-dataset generalization analysis. Evaluations of state-of-the-art MLLMs reveal substantial reliability gaps even in advanced generalist models. Furthermore, experiments show that fine-tuning on OR-VSKC effectively mitigates VS-KC and enables robust generalization to unseen camera viewpoints. We open-source the code and dataset to support reproducible research in safety-critical medical environments. The source code is available at https://github.com/zgg2577/VS-KC.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper introduces OR-VSKC, a benchmark for Visual-Semantic Knowledge Conflicts (VS-KC) in operating rooms, generated via a Protocol-to-Pixel Generative Framework yielding 28,190 synthetic images grounded in 4D-OR and CAMMA-MVOR contexts plus a 713-image expert-validated challenge subset. It reports substantial VS-KC gaps in SOTA MLLMs and shows that fine-tuning on OR-VSKC mitigates these conflicts while enabling generalization to unseen camera viewpoints, with the full dataset and code open-sourced.

Significance. If the synthetic images preserve the visual and semantic features of real OR safety violations, the benchmark and fine-tuning results would provide a practical path to improving MLLM reliability in privacy-constrained medical environments. The open-sourcing of code and data is a clear strength supporting reproducibility.

major comments (1)
  1. [§4 (Experiments and Generalization Analysis)] §4 (Experiments and Generalization Analysis): The claim that fine-tuning on OR-VSKC mitigates VS-KC and enables robust generalization to unseen viewpoints depends on the synthetic distribution matching real OR safety-violation visuals. The manuscript provides expert validation only for the 713-image challenge subset and reports gains on synthetic held-out viewpoint splits, but includes no quantitative fidelity metrics (FID, CLIP semantic alignment, or region-specific perceptual scores) and no real-image hold-out evaluation. This leaves open the possibility that observed improvements stem from synthetic artifacts rather than genuine semantic alignment.
minor comments (1)
  1. [Abstract] The distinction between the 4D-OR-based primary core and the CAMMA-MVOR-based external validation portion could be stated more explicitly in the abstract and early sections to clarify the cross-dataset analysis.

Simulated Author's Rebuttal

1 responses · 1 unresolved

We thank the referee for the constructive feedback on the fidelity of our synthetic data and the robustness of the generalization claims. We address the major comment point by point below, clarifying our evaluation strategy while acknowledging areas where additional evidence can be provided.

read point-by-point responses

  1. Referee: The claim that fine-tuning on OR-VSKC mitigates VS-KC and enables robust generalization to unseen viewpoints depends on the synthetic distribution matching real OR safety-violation visuals. The manuscript provides expert validation only for the 713-image challenge subset and reports gains on synthetic held-out viewpoint splits, but includes no quantitative fidelity metrics (FID, CLIP semantic alignment, or region-specific perceptual scores) and no real-image hold-out evaluation. This leaves open the possibility that observed improvements stem from synthetic artifacts rather than genuine semantic alignment.

    Authors: We agree that stronger evidence of distribution alignment would further support the claims. The 713-image expert-validated subset was designed to provide direct domain-expert confirmation of visual-semantic fidelity for the most challenging cases, which we view as more task-relevant than purely statistical metrics for safety-critical applications. Nevertheless, we will add quantitative fidelity metrics in the revision, including FID scores, CLIP semantic alignment, and region-specific perceptual scores between synthetic images and their real counterparts from the source 4D-OR and CAMMA-MVOR datasets. For real-image hold-out evaluation, we note that the paper's premise is the unavailability of real OR images depicting safety violations due to privacy regulations; this is precisely why synthetic data is introduced. We instead demonstrate cross-dataset generalization using the CAMMA-MVOR-based portion as an external real-data validation set. We will expand §4 to discuss these design choices and limitations explicitly. revision: partial

standing simulated objections not resolved
  • Direct hold-out evaluation on real images of OR safety violations is not feasible due to privacy constraints and data scarcity, which is the core motivation for the synthetic benchmark.

Circularity Check

0 steps flagged

No significant circularity; empirical benchmark and fine-tuning evaluation

full rationale

The paper constructs OR-VSKC as a synthetic benchmark from existing external datasets (4D-OR and CAMMA-MVOR) via a Protocol-to-Pixel Generative Framework, then reports empirical evaluations of MLLMs and fine-tuning results for VS-KC mitigation. No equations, derivations, or fitted parameters are described that reduce the central claims to quantities defined by the paper's own inputs. No self-citations are invoked as load-bearing uniqueness theorems or ansatzes. The work remains self-contained as an empirical contribution with experimental gains on held-out synthetic splits, without any reduction by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Central claim rests on the assumption that synthetic images faithfully capture real safety violation semantics; no free parameters are explicitly fitted to target results in the abstract description.

axioms (1)
  • domain assumption Synthetic images generated from safety protocols accurately reflect visual features of real OR safety violations
    Invoked when claiming that fine-tuning on the benchmark transfers to real environments

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    Their presence violates fundamental principles of asepsis

    Biological Contaminants General Risk: These entities in- troduce non-sterile biological matter, posing a significant risk of surgical site infections, contamination of sterile fields, instruments, and implants. Their presence violates fundamental principles of asepsis. Specific Entities: The presence of insects, exemplified by ant, butterfly, or a general...

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    Inappropriate Objects and Misplaced Equipment General Risk: Introduction of non-medical, non-sterile, or improperly man- aged objects can lead to contamination, physical hazards (e.g., trip- ping hazards, fire risks), distraction, or interference with surgical procedures and medical equipment. Specific Entities: Common- place items such as a Teddy Bear, t...

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  34. [34]

    Specific Entities: The presence of any consumable items, for instance, bread, a generic food entity, fruit, or coffee, directly violates sterility protocols

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    (low qual- ity:1.5), (blurry:1.5)

    Unauthorized Personnel General Risk: Only authorized, ap- propriately trained, and attired personnel are permitted within the OR to ensure patient safety, maintain sterility, prevent procedural interference, and protect patient privacy. Specific Entities: An individual such as a chef in their professional attire clearly lacks the specific training, qualif...

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