让手术机器人具备推理能力，变被动执行为主动协作。
How can reasoning capability empower the AI copilot robot in endoscopic surgery
- 基于视觉-语言-动作模型，整合多模态信息进行推理
- 能理解手术意图并推断组织隐藏动态，减少术中不确定性
- 适合希望提升手术精度与安全性的医疗AI研究者
推理能力在通用领域已显著提升复杂逻辑推理与机器人决策水平。然而，在基于视觉-语言-动作（VLA）模型的人工智能（AI）副手机器人中，其在内镜手术中的潜力尚未被探索。有效的推理应使AI副手机器人能够融合多模态线索，解读手术意图，并推断隐藏的组织动力学，从而缓解术中不确定性与外科医生的认知负担。合理实现的推理驱动自主性可将AI副手从被动执行者转变为认知协作者，提升临床实践中的精准度、安全性与可持续性。
原文摘要 · Abstract (English)
Reasoning capability has significantly advanced complex logical inference and robotic decision-making in general domains. However, its potential in the Artificial Intelligence (AI) copilot robot-particularly implemented based on the Vision-Language-Action (VLA) model-remains unexplored in endoscopic surgery. Effective reasoning should enable AI copilot robots to integrate multimodal cues, interpret surgical intent, and infer hidden tissue dynamics, thereby alleviating intraoperative uncertainty and cognitive burden on surgeons. Properly implemented, reasoning-driven autonomy can transform AI copilot robots from reactive executors into cognitive collaborators, enhancing precision, safety, and sustainability in clinical practice.
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