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

ABSTRACT:

Background

Otitis media remains a significant global health concern, particularly in resource-limited settings where timely diagnosis is challenging. Artificial intelligence (AI) offers promising solutions to enhance diagnostic accuracy in mobile health applications.

Objective

This study introduces a hybrid AI framework that integrates convolutional neural networks (CNNs) for image classification with large language models (LLMs) for clinical reasoning, enabling real-time otoscopic diagnosis.

Methods

We developed a dual-path system combining CNN-based feature extraction with LLM-supported interpretation. The framework was optimized for mobile deployment, with lightweight models operating on-device and advanced reasoning performed via secure cloud APIs. A dataset of 10

SUBMITTER: Chu YC 

PROVIDER: S-EPMC12639226 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature