This research introduces a novel multimodal framework that leverages large pre-trained models to detect early signs of dementia through context-based analysis of patient data.

Key Innovations:

• Combines GPT, BERT, and CLAP models to analyze multiple data types for more accurate dementia detection
• Incorporates contextual understanding of patient information to improve diagnostic accuracy
• Enables earlier intervention possibilities through advanced pattern recognition
• Creates a more accessible screening tool that could reduce diagnostic barriers

Medical Significance: Early detection of dementia is crucial for treatment effectiveness and quality of life. This approach could transform clinical practice by providing cost-effective, non-invasive screening tools that identify subtle cognitive changes before traditional methods, potentially slowing disease progression through timely interventions.

Dementia Insights: A Context-Based MultiModal Approach