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Large Language Model-Driven Analysis and Report Generation of Endoscopy Videos-A Pilot Study.

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Summary
This summary is machine-generated.

Multimodal large language models (MLLMs) show inadequate performance in generating clinical esophagogastroduodenoscopy (EGD) reports. Further research is needed to optimize MLLMs for accurate EGD analysis and reporting.

Keywords:
artificial intelligencedigestive systemendoscopygastrointestinalnatural language processing

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis
  • Gastroenterology

Background:

  • Multimodal large language models (MLLMs) offer potential for automated clinical video analysis.
  • The efficacy of MLLMs in full esophagogastroduodenoscopy (EGD) analysis and the influence of computer-aided detection/diagnosis (CAD) overlays are not well-established.

Purpose of the Study:

  • To evaluate the ability of an MLLM (Gemini 2.5 Pro) to generate clinically adequate EGD reports.
  • To determine the impact of on-screen CAD overlays on MLLM performance in EGD analysis.

Main Methods:

  • Five full EGD videos were analyzed by Gemini 2.5 Pro in both clean and CAD-overlay versions.
  • Five blinded endoscopists assessed report adequacy across completeness, visualization, and lesion characteristics.
  • Two expert endoscopists evaluated MLLM accuracy for landmarks and lesions using the time-window rule.

Main Results:

  • MLLM completeness was rated adequate in 56.0% of clean videos versus 48.0% with CAD overlays (p=0.500).
  • Visualization and lesion characteristics ratings were identical between clean and overlay videos (p=1.000).
  • Overall landmark agreement accuracy was significantly lower with CAD overlays (0.33) compared to clean videos (0.55) (p=0.029).

Conclusions:

  • Gemini 2.5 Pro demonstrated inadequate performance for clinical EGD reporting in this pilot study.
  • The presence of CAD overlays negatively impacted MLLM landmark detection accuracy.
  • Substantial optimization and large-scale validation are required before MLLM deployment for EGD analysis.