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Related Experiment Video

Updated: Jan 18, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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EndoChat: Grounded multimodal large language model for endoscopic surgery.

Guankun Wang1, Long Bai2, Junyi Wang3

  • 1The Chinese University of Hong Kong, 999077, Hong Kong Special Administrative Region of China; Theory Lab, Central Research Institute, 2012 Labs, Huawei Technologies Co. Ltd., 999077, Hong Kong Special Administrative Region of China.

Medical Image Analysis
|September 10, 2025
PubMed
Summary
This summary is machine-generated.

EndoChat, a new Multimodal Large Language Model (MLLM), enhances robotic-assisted surgery training and decision-making. It excels in endoscopic procedure understanding, offering advanced surgical scene analysis and dialogue capabilities.

Keywords:
Dialogue paradigmEndoscopic surgeryMultimodal large language modelSurgical scene understanding

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Area of Science:

  • Artificial Intelligence
  • Medical Robotics
  • Computer Vision

Background:

  • Multimodal Large Language Models (MLLMs) show promise in computer-aided diagnosis and robotic-assisted surgery.
  • A gap exists in specialized MLLMs for endoscopic surgical scene understanding.

Purpose of the Study:

  • To introduce EndoChat, an MLLM designed for understanding endoscopic procedures.
  • To develop a specialized dataset and novel mechanisms for improved surgical scene analysis.

Main Methods:

  • Constructed the Surg-396K dataset using a novel pipeline for surgical information extraction and annotation.
  • Implemented a multi-scale visual token interaction mechanism.
  • Introduced a visual contrast-based reasoning mechanism for enhanced learning.

Main Results:

  • Achieved state-of-the-art performance on five dialogue paradigms and seven surgical scene understanding tasks.
  • Surgeons provided positive feedback on EndoChat's conversational case generation.
  • Demonstrated potential for advancing training and automation in robotic-assisted surgery.

Conclusions:

  • EndoChat represents a significant advancement in MLLMs for surgical applications.
  • The developed dataset and model can accelerate progress in robotic-assisted surgery training and automation.
  • Public availability of the dataset and model facilitates further research and development.