arXiv:2608. 20284v1 Announce Type: new Abstract: Reliable surgical planning requires models to anticipate not only how instruments will move, but also how the operative visual state will evolve together with such motion.
arXiv:2608. 11204v1 Announce Type: cross Abstract: Learning reliable surgical manipulation policies is bottlenecked by the scarcity of action-labeled demonstrations: teleoperated surgical robot (e.
SurgAtlas is the largest surgical video‑language dataset, containing 15,291 videos (2,391 hours) across 18 specialties and over 5,000 procedure types, all sourced from public YouTube. It uniquely includes open‑surgery videos at scale (6,182) alongside more than 9,000 minimally invasive recordings, and introduces standardized benchmarks for open‑surgery video understanding. The dataset offers a rich, multi‑tier annotation schema—segment‑level captions, step/phase descriptions, video‑level surgical narratives, and reasoning‑oriented VQA pairs—validated by experts and built through an automated LLM‑enriched pipeline. "whyItMatters":"SurgAtlas provides an unprecedentedly large, diverse, and clinically validated resource that can train and benchmark multimodal surgical AI models, advancing the development of next‑generation foundation models for surgery."
arXiv:2603.29962v4 Announce Type: replace Abstract: Surgical procedures are inherently complex and risky, requiring extensive expertise and constant focus to navigate evolving intraoperative scenes....
This survey reviews recent advances in surgical video generation, categorizing methods into unconditional, conditional, and world modeling generation. It highlights a shift from creating visually plausible frames to modeling the causal dynamics of surgical scenes, and discusses challenges such as pixel-level fidelity versus clinical plausibility, generalization, physical realism, controllability, and interpretability. The paper also compiles experimental results from public datasets to serve as a quantitative benchmark for the field.
We introduce SurgAtlas, the largest surgical video-language dataset to date, comprising 15,291 videos (2,391 hours) spanning 18 surgical specialties and over 5,000 procedure types, sourced entirely from publicly available YouTube content. SurgAtlas is also the first surgical video-language dataset to include open surgery at scale, with 6,182 open procedure videos alongside over 9,000 minimally invasive recordings, and the first to establish standardized benchmarks for open-surgery video understanding.
The paper introduces LaST, a large-small collaborative framework for zero-shot surgical phase recognition. It combines a foundation model that generates frame-level phase priors with a lightweight model that refines predictions through iterative temporal refinement, dynamic quality control, and dual-model cross-learning. Experiments show LaST outperforms baseline and state-of-the-art methods, achieving significant accuracy gains on unseen clinical domains.
arXiv:2608.21441v1 Announce Type: cross Abstract: Automated training of surgeons is one of the most crucial factors that significantly minimize surgical training risks and expenses. With recent advan...
OphBiWSSD is a new framework for temporal action localization in ophthalmic surgeries that uses Bidirectional State Space Duality to avoid the quadratic memory cost of Transformers. It employs a weight‑tied selective scan that incorporates both past and future surgical context, enabling linear‑time global synthesis of non‑causal temporal cues. On the OphNet benchmark, OphBiWSSD achieves state‑of‑the‑art mean Average Precisions of 44.42 % for phases and 43.08 % for operations, outperforming baselines by 6.80 % and 6.66 % respectively.
The FedSurg Challenge is the first international effort to evaluate Federated Learning (FL) for surgical vision, using a multi‑center dataset of laparoscopic appendectomies. Three participant models were tested for generalization to an unseen clinical center and for center‑specific adaptation, compared against centralized, Swarm Learning, and parameter‑efficient fine‑tuning baselines. The study found that temporal modeling most consistently improves generalization, but overall performance remains low (26.31% F1‑score on the unseen center), highlighting the need for structured personalized FL and revealing limitations of current approaches.
arXiv:2606. 30657v1 Announce Type: cross Abstract: Surgical outcomes depend not only on patient factors and postoperative care but are also strongly influenced by the quality of the operation itself.
arXiv:2608.30872v1 Announce Type: new Abstract: Objective assessment of surgical technical skill is important for surgical training and structured feedback, but current workflows remain dependent on...