Document Type

Article

Publication Date

2025

Keywords

Large Language Model, Artificial Intelligence

Disciplines

Artificial Intelligence and Robotics

Abstract

High Cost of Traditional Screening: Formal cognitive assessments for dementia are resource-intensive and not easily accessible for large-scale screening. Speech-Based Alternatives: Existing speech-based methods (e.g., picture description, telephone interviews) aim to address this but have limitations. Lack of Natural Dialogue: These conventional approaches often use rigid, repetitive prompts and do not simulate real conversations. Engagement Issues: Repetition and lack of conversational depth can reduce engagement and affect the accuracy of responses over time. Untapped Potential of LLMs: Large language models (LLMs) are capable of generating natural, coherent, and adaptive dialogue. Research Gap: The application of LLMs for dementia detection through natural conversation remains largely unexplored. Study Goal: This work investigates whether LLMs can simulate meaningful dialogue and detect dementia-related linguistic patterns effectively.

Comments

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