智能体手术AI：在视觉-语言-动作框架中通过离散扩散实现外科医生风格指纹识别与隐私风险量化
Agentic Surgical AI: Surgeon Style Fingerprinting and Privacy Risk Quantification via Discrete Diffusion in a Vision-Language-Action Framework
AI总结：
本研究提出结合离散扩散与VLA流水线的智能体手术AI方法，可学习外科医生个性化运动指纹以预测手术手势，同时揭示个性化嵌入提升性能的同时会增加身份泄露风险，需平衡二者关系。
摘要：
外科医生展现出由培训、经验和运动行为塑造的独特手术风格，但多数手术AI系统忽视了这一个性化信号。我们提出一种用于机器人手术中外科医生特定行为预测的新型智能体建模方法，将离散扩散框架与视觉-语言-动作（VLA）流水线相结合。手势预测被构建为结构化序列去噪任务，以手术视频、意图语言以及外科医生身份和技能的个性化嵌入等多模态输入为条件。这些嵌入通过第三方语言模型利用自然语言提示进行编码，使模型能够保留个体行为风格，同时不会泄露明确的身份信息。我们在JIGSAWS数据集上评估了所提方法，结果表明该方法能准确重建手势序列，同时学习到每位外科医生特有的有意义运动指纹。为量化个性化带来的隐私影响，我们开展了成员推理攻击实验，发现表达能力更强的嵌入在提升任务性能的同时，也增加了身份泄露的风险。这些发现表明，尽管个性化嵌入能提升性能，但它们也会提高身份泄露的脆弱性，凸显了在手术建模中平衡个性化与隐私风险的重要性。代码可在以下链接获取：https://github.com/huixin-zhan-ai/Surgeon_style_fingerprinting。
英文摘要：
Surgeons exhibit distinct operating styles shaped by training, experience, and motor behavior-yet most surgical AI systems overlook this personalization signal. We propose a novel agentic modeling approach for surgeon-specific behavior prediction in robotic surgery, combining a discrete diffusion framework with a vision-language-action (VLA) pipeline. Gesture prediction is framed as a structured sequence denoising task, conditioned on multimodal inputs including surgical video, intent language, and personalized embeddings of surgeon identity and skill. These embeddings are encoded through natural language prompts using third-party language models, allowing the model to retain individual behavioral style without exposing explicit identity. We evaluate our method on the JIGSAWS dataset and demonstrate that it accurately reconstructs gesture sequences while learning meaningful motion fingerprints unique to each surgeon. To quantify the privacy implications of personalization, we perform membership inference attacks and find that more expressive embeddings improve task performance but simultaneously increase susceptibility to identity leakage. These findings demonstrate that while personalized embeddings improve performance, they also increase vulnerability to identity leakage, revealing the importance of balancing personalization with privacy risk in surgical modeling. Code is available at: https://github.com/huixin-zhan-ai/Surgeon_style_fingerprinting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
