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<p>Esophagogastroduodenoscopy (EGD) is an essential clinical procedure for diagnosing gastrointestinal (GI) diseases, while the subsequent EGD reports play a vital role in clinical decision-making and therapeutic interventions. This paper makes the first attempt to bootstrap Multimodal Large Language Models (MLLM) with medical knowledge for EGD diagnosis and reporting (EDR). We collected the largest multicentric EGD dataset so far, containing 4461 participants with 203,838 EGD images and 4461 corresponding EGD reports. Experimental results demonstrate that the proposed method, MLLM-EDR, achieves an average diagnostic accuracy of 0.882 for nineteen GI diseases, outperforming both state-of-the-art AI models (0.720) and junior endoscopists (0.784). The completeness and facticity scores of our generated reports match the quality of those created by senior endoscopists. Moreover, our method substantially reduces the endoscopists’ workload from 7 minutes to 13.48 seconds. These results highlight the vigor and significance of MLLM-EDR for AI-assisted EGD diagnostic and reporting applications.</p>

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Bootstrapping multimodal large language model with medical knowledge for automatic esophagogastroduodenoscopy diagnosis and reporting

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月17日

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  • Miaojing Shi,
  • Zijie Yue,
  • Huihui Sun,
  • Bo Li,
  • Yikun Ma,
  • Tianyu Cen,
  • Ying Chen,
  • Xiaofen Wu,
  • Hanli Wang,
  • Jun Wang,
  • Shuchang Xu

摘要

Esophagogastroduodenoscopy (EGD) is an essential clinical procedure for diagnosing gastrointestinal (GI) diseases, while the subsequent EGD reports play a vital role in clinical decision-making and therapeutic interventions. This paper makes the first attempt to bootstrap Multimodal Large Language Models (MLLM) with medical knowledge for EGD diagnosis and reporting (EDR). We collected the largest multicentric EGD dataset so far, containing 4461 participants with 203,838 EGD images and 4461 corresponding EGD reports. Experimental results demonstrate that the proposed method, MLLM-EDR, achieves an average diagnostic accuracy of 0.882 for nineteen GI diseases, outperforming both state-of-the-art AI models (0.720) and junior endoscopists (0.784). The completeness and facticity scores of our generated reports match the quality of those created by senior endoscopists. Moreover, our method substantially reduces the endoscopists’ workload from 7 minutes to 13.48 seconds. These results highlight the vigor and significance of MLLM-EDR for AI-assisted EGD diagnostic and reporting applications.