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Middle ear diseases, such as otitis media and middle ear effusion, pose significant diagnostic challenges, especially in primary care settings, where misdiagnoses or delayed treatments can lead to severe complications and unnecessary antibiotic use. To address these issues, we developed a cloud-based AI system that integrates convolutional neural networks (CNNs) and large language models (LLMs) to assist clinicians in diagnosing middle ear diseases with high accuracy. This study involved a retrospective analysis of 2,820 de- identified otoendoscopic images collected from Taipei Veterans General Hospital between January 2011 and December 2019. The CNN models, including InceptionV3, were optimized through transfer learning and validated against independent test datasets. The AI system, deployed on cloud servers, enables real-time analysis via a smartphone application, with the LLM providing contextual medical advice based on CNN outputs. The system achieved a diagnostic accuracy of 97.6%, comparable to otolaryngology specialists (98.2%) and significantly higher than general practitioners (84.3%), with a precision of 96.8%, recall of 95.4%, and F1-score of 96.1%. The area under the ROC curve (AUC) was 0.98, indicating excellent model performance. Additionally, the integration of the LLM improved clinical utility by offering relevant medical advice, which was highly rated in usability tests. This cloud- based AI system demonstrates high diagnostic accuracy and practical utility, particularly in resource-limited settings, making it a valuable tool for enhancing patient safety and clinical outcomes.

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Enhancing Clinical Accuracy in Middle Ear Disease Diagnosis with a Cloud-Based AI System Integrating CNNs and LLMs

  • 会议论文

  • 发表日期：

    2025年

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  • Yuan-Chia Chu,
  • Kuan-Hsun Lin,
  • Yen-Chi Chen,
  • Chien-Yeh Hsu,
  • Chen-Tsung Kuo,
  • Yen-Fu Cheng,
  • Wen-Huei Liao

摘要

Middle ear diseases, such as otitis media and middle ear effusion, pose significant diagnostic challenges, especially in primary care settings, where misdiagnoses or delayed treatments can lead to severe complications and unnecessary antibiotic use. To address these issues, we developed a cloud-based AI system that integrates convolutional neural networks (CNNs) and large language models (LLMs) to assist clinicians in diagnosing middle ear diseases with high accuracy. This study involved a retrospective analysis of 2,820 de- identified otoendoscopic images collected from Taipei Veterans General Hospital between January 2011 and December 2019. The CNN models, including InceptionV3, were optimized through transfer learning and validated against independent test datasets. The AI system, deployed on cloud servers, enables real-time analysis via a smartphone application, with the LLM providing contextual medical advice based on CNN outputs. The system achieved a diagnostic accuracy of 97.6%, comparable to otolaryngology specialists (98.2%) and significantly higher than general practitioners (84.3%), with a precision of 96.8%, recall of 95.4%, and F1-score of 96.1%. The area under the ROC curve (AUC) was 0.98, indicating excellent model performance. Additionally, the integration of the LLM improved clinical utility by offering relevant medical advice, which was highly rated in usability tests. This cloud- based AI system demonstrates high diagnostic accuracy and practical utility, particularly in resource-limited settings, making it a valuable tool for enhancing patient safety and clinical outcomes.