Draft Status
Ready - team will start page creating immediately
Category
IGT and Training
Key Investigators
- SangHyuk Kim (BWH & UMass Boston, USA)
- Puxun Tu (BWH & SJTU, USA)
- Steve Pieper (Isomics, USA)
- Lipeng Ning (BWH & HMS, USA)
Project Description
SlicerTMS is a 3DSlicer module for patient-specific transcranial stimulation. It integrates several functions, including neuronavigation, electric field modeling, real-time EEG streaming and recording, and TMS control. These functions involve a complex user interface, and some tasks, such as neuronavigation registration, may need more than one user. To simplify the interface and improve the user experience, we will develop a new version leveraging LLM models and Slicer AI Agent tools. Specifically, we will eliminate LLM hallucinations at the infrastructure level by executing medical software APIs through human-verified Markdown Cookbooks and local Vector RAG technologies. Furthermore, the system establishes a next-generation intelligent environment featuring a self-evolving Auto-Correction engine that tracks and learns directly from clinician adjustment patterns, all while seamlessly supporting the trusted clinical interfaces medical professionals already use.
Objective
- Implement a voice-based interface for neuronavigation registration.
- Improve the data visualization interface using the AI agent, e.g., by switching between visualization methods.
- Simplify the interface with the EEG and TMS devices AI agent.
- Minimize LLM hallucination in SlicerTMS by compiling user intent into strict template-based execution payloads.
Approach and Plan
- Zero-Hallucination RAG System: Modularize verified VTK recipes into Markdown Cookbooks, embed them into a local Vector DB, and inject real-time scene variables for deterministic execution.
- UI and UX Modernization: Integrate high-density clinical data and 2D/3D visualizations into a dynamic spatial layout to overcome Slicer's legacy UI constraints.
- Architecture Evaluation Benchmark inference accuracy, latency, and medical data privacy between closed-network offline models and cloud-based counterparts.
Progress and Next Steps
No response
Illustrations
No response
Background and References
SlicerTMS, Slicer AI Agent, NousNav
Draft Status
Ready - team will start page creating immediately
Category
IGT and Training
Key Investigators
Project Description
SlicerTMS is a 3DSlicer module for patient-specific transcranial stimulation. It integrates several functions, including neuronavigation, electric field modeling, real-time EEG streaming and recording, and TMS control. These functions involve a complex user interface, and some tasks, such as neuronavigation registration, may need more than one user. To simplify the interface and improve the user experience, we will develop a new version leveraging LLM models and Slicer AI Agent tools. Specifically, we will eliminate LLM hallucinations at the infrastructure level by executing medical software APIs through human-verified Markdown Cookbooks and local Vector RAG technologies. Furthermore, the system establishes a next-generation intelligent environment featuring a self-evolving Auto-Correction engine that tracks and learns directly from clinician adjustment patterns, all while seamlessly supporting the trusted clinical interfaces medical professionals already use.
Objective
Approach and Plan
Progress and Next Steps
No response
Illustrations
No response
Background and References
SlicerTMS, Slicer AI Agent, NousNav