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Background <p>High-quality endoscopic reports are crucial for clinical decision-making in gastric lesions. However, manually generated reports often suffer from inconsistencies, resulting in incomplete lesion documentation and insufficient descriptions. The objective is to validate an artificial intelligence (AI) reporting system for improving the quality of gastric lesion documentation with a particular focus on narrowing the experience gap among endoscopists.</p> Methods <p>We retrospectively collected gastric images to develop a reporting system for gastric lesions. The system comprises several deep learning models for lesion detection, classification, and feature recognition, and was validated using 276 video clips. Both retrospective and prospective case validations were conducted to evaluate the system’s clinical effectiveness, comparing the completeness of reports with and without AI assistance, particularly in describing features of suspicious lesions.</p> Results <p>In video validation, the system identified 99.27% (274/276) of lesions, with 88.97% accuracy in neoplasm detection, achieving 93.75% sensitivity and 86.98% specificity. Retrospective analyses showed that junior endoscopists using AI reported significantly more complete lesion documentation than original reports (74.75% <i>vs</i> 58.08%, <i>P</i> &lt; 0.001). In the prospective trial, the AI-assisted group demonstrated superior lesion reporting completeness (86.50% <i>vs</i> 75.11%, <i>P</i> = 0.001). Notably, the AI group reported all 18 identified suspicious neoplasms, while the routine group reported only 16.</p> Conclusions <p>This study confirms the efficacy of an AI reporting system in documenting gastric focal lesions, highlighting significant improvements in the completeness and accuracy of reports generated by endoscopists.</p>

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Artificial intelligence assistance narrows the experience gap in endoscopic reporting of gastric lesions: a prospective clinical trial

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月4日

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  • Hang You,
  • Xiao Tao,
  • Zehua Dong,
  • Yanxia Li,
  • Li Huang,
  • Jia Li,
  • Hongliu Du,
  • Mei Deng,
  • Zhifeng Wu,
  • Xia Tan,
  • Ting Yang,,
  • Jun Liu,
  • Honggang Yu

摘要

Background

High-quality endoscopic reports are crucial for clinical decision-making in gastric lesions. However, manually generated reports often suffer from inconsistencies, resulting in incomplete lesion documentation and insufficient descriptions. The objective is to validate an artificial intelligence (AI) reporting system for improving the quality of gastric lesion documentation with a particular focus on narrowing the experience gap among endoscopists.

Methods

We retrospectively collected gastric images to develop a reporting system for gastric lesions. The system comprises several deep learning models for lesion detection, classification, and feature recognition, and was validated using 276 video clips. Both retrospective and prospective case validations were conducted to evaluate the system’s clinical effectiveness, comparing the completeness of reports with and without AI assistance, particularly in describing features of suspicious lesions.

Results

In video validation, the system identified 99.27% (274/276) of lesions, with 88.97% accuracy in neoplasm detection, achieving 93.75% sensitivity and 86.98% specificity. Retrospective analyses showed that junior endoscopists using AI reported significantly more complete lesion documentation than original reports (74.75% vs 58.08%, P < 0.001). In the prospective trial, the AI-assisted group demonstrated superior lesion reporting completeness (86.50% vs 75.11%, P = 0.001). Notably, the AI group reported all 18 identified suspicious neoplasms, while the routine group reported only 16.

Conclusions

This study confirms the efficacy of an AI reporting system in documenting gastric focal lesions, highlighting significant improvements in the completeness and accuracy of reports generated by endoscopists.