SurgRAW introduces a multi‑agent, chain‑of‑thought workflow for robotic surgical video analysis, leveraging a new SurgCoTBench benchmark with 14,256 QA pairs across five surgical tasks. The system uses an orchestrator to split scene understanding into two reasoning streams, panel‑discussion‑style collaboration among task‑specific agents, and retrieval‑augmented generation to incorporate surgical knowledge. Experiments show SurgRAW outperforms mainstream vision‑language models and a supervised baseline by 14.61% accuracy.
By Chang Han Low, Ziyue Wang, Tianyi Zhang, Zhu Zhuo, Zhitao Zeng, Evangelos B. Mazomenos, Yueming Jin
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.
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."
By Filippos Bellos, Andre S. Gala-Garza, Miaowei Wang, Alyssa M. Hardin, Ahmad M. Hider, Li Yayuan, Jing Bi, Susan Liang, Chenliang Xu, Donald S. Likosky, Jason J. Corso
arXiv:2608. 01473v1 Announce Type: cross Abstract: Multimodal large language models (MLLM) for surgical scene understanding typically inject hundreds of dense visual tokens into a language model, leading to costly inference and limited spatial traceability for generated answers.
By Guiqiu Liao, Matjaz Jogan, Daniel A. Hashimoto
TEDi is a Temporal memory-Enhanced and Denoising Transformer designed for surgical instrument segmentation. It introduces a query-level memory bank with a memory search enhancement encoder to incorporate discriminative representations from past frames, and a temporal consistency denoising module that builds a cross‑frame semantic anchor to stabilize predictions. Experiments on EndoVis 2017 and EndoVis 2018 show that TEDi outperforms existing state‑of‑the‑art methods, indicating its effectiveness for computer‑assisted surgery.
By Jiahong Yuan, Weiming Mi, Tao Zhang, Haoyin Zhou
Effective multi-task learning for surgical scene understanding is fundamentally hindered by annotation granularity mismatch; temporal workflow tasks such as phase recognition, step recognition and anticipation benefit from dense frame-level supervision, whereas pixel-level spatial tasks including instrument segmentation and action recognition are only sparsely annotated on selected keyframes due to prohibitive labeling costs. This supervision imbalance undermines shared representation learning and limits joint optimization across heterogeneous surgical tasks.
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....
By Shi Li, Vinkle Srivastav, Nicolas Chanel, Saurav Sharma, Nabani Banik, Lorenzo Arboit, Kun Yuan, Pietro Mascagni, Nicolas Padoy
arXiv:2609.37283v1 Announce Type: new
Abstract: Medical multimodal large language models (MLLMs) are increasingly expected not only to answer clinical questions, but also to localize the visual evide...
By Xuyang Cao, Enyou Liu, Jun Zhao, Zhuoyun Liu, Jintao Fei, Leo
arXiv:2608.24541v1 Announce Type: new
Abstract: Surgical instrument segmentation (SIS) is fundamental for computer-assisted surgery, where reliable instrument masks enable precise scene understanding...
By Xinning Yao, Jingjing Wang, Jinghua Yue, Xiaoyan Luo, Fugen Zhou, Bo Liu
arXiv:2606. 29247v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models represent a promising direction for embodied intelligence in surgical robotics.
By Jiashuo Sun, Yue He, Wenxuan Liu, Tao Mao, Jiazheng Wang, Xiang Chen, Min Liu
Surgical instrument segmentation (SIS) is fundamental for computer-assisted surgery, where reliable instrument masks enable precise scene understanding and clinical assistance. Recently, adapting foun...
arXiv:2511. 12110v5 Announce Type: replace-cross Abstract: Despite notable progress in text-guided medical image segmentation nowadays, these methods are limited to single-round dialogues and fail to support multi-round reasoning, which is important for medical education scenarios.
By Qinyue Tong, Ziqian Lu, Jun Liu, Rui Zuo, Zheming Lu, Yueming Jin