Surgeon Style Fingerprinting and Privacy Risk Quantification via Discrete Diffusion Models in a Vision-Language-Action Framework

2025-06-09

AI Summary

This study addresses the dual challenges of modeling surgeon-specific operative styles and preserving privacy in robotic surgery. We propose a discrete diffusion model—built upon the Vision-Language-Action (VLA) framework—that integrates endoscopic vision, surgical intent language, and privacy-aware identity encoding to perform structured denoising prediction of gesture sequences. Our key innovation lies in encoding surgeon style as interpretable, privacy-safe natural language prompts—enabling the first semantic modeling of “motion fingerprints.” Furthermore, we quantitatively evaluate the privacy–utility trade-off of personalized embeddings against membership inference attacks. On the JIGSAWS dataset, our model accurately reconstructs individualized gesture sequences, empirically confirming that highly expressive embeddings improve reconstruction fidelity yet exacerbate identity leakage. This work establishes a novel paradigm for trustworthy, privacy-preserving personalized surgical AI.

Technology Category

• Machine Learning: Privacy
• Natural Language Processing: Safety and Robustness
• Computer Vision: Diffusion Models for Vision

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Surgeons exhibit distinct operating styles due to differences in training, experience, and motor behavior - yet current AI systems often ignore this personalization signal. We propose a novel approach to model fine-grained, surgeon-specific fingerprinting in robotic surgery using a discrete diffusion framework integrated with a vision-language-action (VLA) pipeline. Our method formulates gesture prediction as a structured sequence denoising task, conditioned on multimodal inputs including endoscopic video, surgical intent language, and a privacy-aware embedding of surgeon identity and skill. Personalized surgeon fingerprinting is 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.

Problem

• Modeling surgeon-specific fingerprinting in robotic surgery
• Balancing personalization with privacy risk in surgical AI
• Quantifying identity leakage in personalized surgical embeddings

Innovation

• Discrete diffusion models for surgeon fingerprinting
• Vision-language-action framework integration
• Privacy-aware embedding for identity protection