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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Computer-Aided Diagnosis Using the Large-Scale Visual Language Models in Screening of Digestive Endoscopy.

Yilin Li1, Zhonghua Du2, Renbo Li1

  • 1School of Control Science and Engineering, Shandong University, Jinan, 250012, Shandong, China.

Journal of Imaging Informatics in Medicine
|August 12, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) aids computer-aided diagnosis (CAD) in digestive endoscopy by analyzing images. This AI model improves diagnostic accuracy and speed, assisting healthcare professionals in screening programs.

Keywords:
Convolutional neural networkDigestive endoscopy screeningLarge-scale visual-language modelMulti-head attention

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Gastroenterology

Background:

  • High prevalence of digestive tract diseases necessitates large-scale endoscopic screening in China.
  • Limited staff-to-patient ratios create a diagnostic workload challenge in endoscopic screening programs.

Purpose of the Study:

  • To develop and evaluate an AI-powered computer-aided diagnosis (CAD) method for digestive endoscopy screening.
  • To leverage pre-trained vision-language models for enhanced classification of endoscopic images.

Main Methods:

  • A CAD method utilizing pre-trained large-scale vision-language models was developed.
  • The model fused visual and textual information for classification of endoscopic images.
  • Training data comprised 41,191 multicenter images across 19 disease categories and 4 normal conditions.

Main Results:

  • The proposed AI model outperformed eight state-of-the-art models in comparative evaluations.
  • In human-AI collaboration, the model showed superior diagnostic performance compared to experienced endoscopists.
  • AI assistance led to faster decision-making times in diagnostic tasks.

Conclusions:

  • The AI-driven CAD method is effective for analyzing large-scale endoscopic datasets.
  • AI has the potential to significantly improve diagnostic efficiency and accuracy in digestive endoscopy.
  • This study advances AI-assisted diagnostics, moving towards AI-driven clinical reality in endoscopy.