Surgical AI Copilot: Energy-Based Fourier Gradient Low-Rank Adaptation for Surgical LLM Agent Reasoning and Planning

The introduction of the Surgical AI Copilot represents a significant leap in the integration of AI within surgical practices, particularly in image-guided surgery. By leveraging large language models (LLMs), this system facilitates dynamic task planning and predictive decision support, overcoming the limitations of traditional static AI models. The development of the PitAgent dataset is crucial, as it provides the necessary context-aware data for training LLM agents to perform complex intraoperative tasks such as MRI tumor segmentation, endoscope anatomy segmentation, and instrument tracking. This advancement not only enhances the capabilities of surgical teams but also aims to improve patient outcomes through more precise and informed surgical interventions. As the field of surgical AI continues to evolve, the Surgical AI Copilot stands out as a promising tool that could redefine surgical workflows and decision-making processes.