What question did this study set out to answer?

To introduce a hybrid AI framework integrating CNNs and LLMs for real-time otoscopic diagnosis.

May 1, 2025Digital HealthOpen Access

Hybrid artificial intelligence frameworks for otoscopic diagnosis: Integrating convolutional neural networks and large language models toward real-time mobile health

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Hybrid AI improves diagnostic accuracy in otitis media, suggesting enhanced mobile health applications.

To introduce a hybrid AI framework integrating CNNs and LLMs for real-time otoscopic diagnosis.
Developed a dual-path AI system combining CNN-based feature extraction with LLM-supported interpretation.
Optimized for mobile deployment with lightweight models operating on-device.
Utilized a dataset of 10,465 otoendoscopic images for training and validation.
Achieved an overall diagnostic accuracy of 97.6%.
Provided feedback within sub-200 ms.
Delivered specialist-level performance in identifying common ear pathologies.

Chu et al. (2025) studied this question.

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