How Far Are Surgeons from Surgical World Models? A Pilot Study on
  Zero-shot Surgical Video Generation with Expert Assessment
Authors: name: Zhen Chen, name: Qing Xu, name: Jinlin Wu, name: Biao Yang, name: Yuhao Zhai, name: Geng Guo, name: Jing Zhang, name: Yinlu Ding, name: Nassir Navab, name: Jiebo Luo

Foundation models in video generation are demonstrating remarkable
capabilities as potential world models for simulating the physical world.
However, their application in high-stakes domains like surgery, which demand
deep, specialized causal knowledge rather than general physical rules, remains
a critical unexplored gap. To systematically address this challenge, we present
SurgVeo, the first expert-curated benchmark for video generation model
evaluation in surgery, and the Surgical Plausibility Pyramid (SPP), a novel,
four-tiered framework tailored to assess model outputs from basic appearance to
complex surgical strategy. On the basis of the SurgVeo benchmark, we task the
advanced Veo-3 model with a zero-shot prediction task on surgical clips from
laparoscopic and neurosurgical procedures. A panel of four board-certified
surgeons evaluates the generated videos according to the SPP. Our results
reveal a distinct "plausibility gap": while Veo-3 achieves exceptional Visual
Perceptual Plausibility, it fails critically at higher levels of the SPP,
including Instrument Operation Plausibility, Environment Feedback Plausibility,
and Surgical Intent Plausibility. This work provides the first quantitative
evidence of the chasm between visually convincing mimicry and causal
understanding in surgical AI. Our findings from SurgVeo and the SPP establish a
crucial foundation and roadmap for developing future models capable of
navigating the complexities of specialized, real-world healthcare domains.