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AI-Assisted Detection Support for Middle Ear Diseases Using Multimodal Large Language Models.

Yuan-Chia Chu1,2, Kuan-Hsun Lin1,2, Yuan-Chen Chou3

  • 1Department of Information Management, Taipei Veterans General Hospital, Taipei, Taiwan.

Studies in Health Technology and Informatics
|August 8, 2025
PubMed
Summary

This study introduces an AI system for detecting middle ear diseases from otoscopic images. The novel approach enhances diagnostic accuracy in primary care settings.

Keywords:
Artificial IntelligenceDetection ImagingDiagnostic ImagingLarge Language ModelsMiddle Ear EffusionMobile ApplicationsOtitis MediaOtoendoscopyPoint-of-Care SystemsTelemedicine

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Area of Science:

  • Otolaryngology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Accurate detection of middle ear diseases like otitis media is challenging in primary care.
  • Existing diagnostic methods may lack efficiency and accessibility in non-specialist settings.

Purpose of the Study:

  • To develop and evaluate an AI-powered system for the early detection of ten middle ear conditions using otoscopic images.
  • To improve the diagnostic capabilities for middle ear pathologies in primary care and telemedicine.

Main Methods:

  • Development of an AI system leveraging Azure OpenAI's GPT-4 Vision, a multimodal large language model (LLM).
  • Implementation using a Model-View-Controller (MVC) architecture for efficient image processing.
  • Analysis of otoscopic images to identify ten distinct middle ear conditions.

Main Results:

  • The AI system successfully analyzes otoscopic images and detects ten middle ear conditions.
  • Image processing time is under 5 seconds, ensuring rapid diagnostic support.
  • Bilingual (English/Chinese) reports are generated, including confidence scores and treatment recommendations.

Conclusions:

  • The AI-powered system offers a scalable and efficient solution for diagnosing middle ear diseases.
  • Integration of this technology can significantly enhance diagnostic workflows in telemedicine and primary care.
  • This multimodal LLM approach represents a novel advancement in automated otoscopic image analysis.