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[2503.10265] SurgRAW: Multi-Agent Workflow with Chain of Thought Reasoning for Robotic Surgical Video Analysis
Summary
The article presents SurgRAW, a multi-agent workflow utilizing Chain of Thought reasoning for enhanced robotic surgical video analysis, addressing limitations in existing AI models.
Why It Matters
This research is significant as it introduces a unified approach to robotic-assisted surgery (RAS) scene understanding, overcoming challenges like task fragmentation and hallucinations in AI models. By proposing a novel benchmark and workflow, it aims to improve surgical outcomes and AI interpretability, which is crucial for clinical applications.
Key Takeaways
- SurgRAW enhances robotic surgical video analysis through a multi-agent workflow.
- It introduces SurgCoTBench, the first benchmark focused on reasoning in RAS.
- The proposed method improves accuracy by 14.61% over existing models.
- Chain of Thought reasoning reduces hallucinations and enhances interpretability.
- Collaboration among task-specific agents improves overall workflow efficiency.
Computer Science > Artificial Intelligence arXiv:2503.10265 (cs) [Submitted on 13 Mar 2025 (v1), last revised 18 Feb 2026 (this version, v2)] Title:SurgRAW: Multi-Agent Workflow with Chain of Thought Reasoning for Robotic Surgical Video Analysis Authors:Chang Han Low, Ziyue Wang, Tianyi Zhang, Zhu Zhuo, Zhitao Zeng, Evangelos B. Mazomenos, Yueming Jin View a PDF of the paper titled SurgRAW: Multi-Agent Workflow with Chain of Thought Reasoning for Robotic Surgical Video Analysis, by Chang Han Low and 6 other authors View PDF HTML (experimental) Abstract:Robotic-assisted surgery (RAS) is central to modern surgery, driving the need for intelligent systems with accurate scene understanding. Most existing surgical AI methods rely on isolated, task-specific models, leading to fragmented pipelines with limited interpretability and no unified understanding of RAS scene. Vision-Language Models (VLMs) offer strong zero-shot reasoning, but struggle with hallucinations, domain gaps and weak task-interdependency modeling. To address the lack of unified data for RAS scene understanding, we introduce SurgCoTBench, the first reasoning-focused benchmark in RAS, covering 14256 QA pairs with frame-level annotations across five major surgical tasks. Building on SurgCoTBench, we propose SurgRAW, a clinically aligned Chain-of-Thought (CoT) driven agentic workflow for zero-shot multi-task reasoning in surgery. SurgRAW employs a hierarchical reasoning workflow where an orchestrator divides surgic...
