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Domain specific multimodal large language model for automated endoscopy reporting with multicenter prospective

Ruiqing Jiang1,2,3,4, Boru Chen1,2,3,4, Zehua Dong1,2,3,4

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Summary
This summary is machine-generated.

An AI system called Report-Angel automatically generates draft endoscopy reports for upper gastrointestinal diseases. This tool aims to improve accuracy and reduce workloads for medical professionals.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Gastroenterology

Background:

  • Accurate upper gastrointestinal (UGI) endoscopy reports are vital for patient diagnosis and management.
  • Current report generation is often labor-intensive, time-consuming, and prone to errors and omissions.

Purpose of the Study:

  • To develop and evaluate Report-Angel, an AI system designed to automatically generate detailed draft reports for UGI endoscopy.
  • To assess the clinical acceptability, completeness, and accuracy of AI-generated reports.

Main Methods:

  • Developed Report-Angel, an integrated AI system combining a multi-modal large language model (MLLM) and deep learning models.
  • Trained the system on a dataset of 20,617 image-text pairs.
  • Validated the system on prospective internal and external cohorts, and retrospective and prospective video datasets.

Main Results:

  • Report-Angel achieved clinically acceptable report rates of 79.3% (internal) and 83.3% (external).
  • Case-level completeness was 88.51% and accuracy was 78.93%.
  • Lesion-level reporting accuracies ranged from 83.94% to 91.92% across different datasets, with an average processing time of 1.5 seconds per lesion.

Conclusions:

  • Report-Angel generates expert-level draft endoscopy reports with robust generalizability.
  • The system has the potential to standardize reporting and significantly reduce endoscopists' workloads.
  • AI-driven report generation offers a promising solution for improving efficiency and accuracy in UGI endoscopy.