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Related Experiment Video

Updated: Jun 4, 2026

Endoscopic Cholesteatoma Surgery
08:47

Endoscopic Cholesteatoma Surgery

Published on: January 19, 2022

Large Language Models for Cholesteatoma Diagnosis: A Pathology-Validated Study.

Alper Yenigun1, Ramazan B Kucuk1, Cagri Yildiz1

  • 1Department of Otorhinolaryngology, Faculty of Medicine, Bezmialem Vakif University.

The Journal of Craniofacial Surgery
|June 2, 2026
PubMed
Summary

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A large language model (LLM) showed diagnostic performance comparable to radiology for detecting cholesteatoma. This AI tool offers rapid, image-centered analysis, potentially aiding otologic practice.

Area of Science:

  • Otolaryngology
  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis

Background:

  • Cholesteatoma detection relies heavily on radiologic interpretation.
  • Large Language Models (LLMs) are emerging as potential tools for medical image analysis.
  • Evaluating LLM performance in otologic diagnostics is crucial for clinical integration.

Purpose of the Study:

  • To assess the diagnostic accuracy of the LLM Gemini 2.5 for cholesteatoma detection.
  • To compare the LLM's performance against routine radiologic assessment.
  • To explore the LLM's utility as a decision-support tool in otologic practice.

Main Methods:

  • Retrospective analysis of 244 temporal bone MRIs (2017-2025).
  • MRI data converted to video files for LLM evaluation without fine-tuning.
Keywords:
Artificial intelligencecholesteatomadecision support systemsdiffusion-weighted imaginglarge language modelsmagnetic resonance imaging

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Last Updated: Jun 4, 2026

Endoscopic Cholesteatoma Surgery
08:47

Endoscopic Cholesteatoma Surgery

Published on: January 19, 2022

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Robot-Assisted Transcanal Endoscopic Ear Surgery for Congenital Cholesteatoma

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  • Diagnostic metrics calculated against histopathology (gold standard).

    • Main Results:

    • LLM achieved 79.1% diagnostic accuracy; radiology achieved 84.0%.
    • Sensitivity was comparable, but specificity was higher for radiology.
    • Performance remained consistent across disease subtypes and postoperative anatomy.

    Conclusions:

    • LLM demonstrated comparable diagnostic performance to radiology for cholesteatoma.
    • LLMs offer rapid, image-centered analysis without specialized infrastructure.
    • LLM-based systems can serve as practical complementary tools in otologic evaluation.