Objective:

To enhance the prediction of Alzheimer's Disease (AD) through linguistic feature analysis using large language models (LLMs), emphasizing the significance of early detection.

Key Findings:

• The global prevalence of Alzheimer's Disease is projected to increase significantly, necessitating early detection strategies.
• Language impairment is a notable early symptom of AD, and integrating language assessments can improve prediction of disease progression.
• Deep learning approaches, including LLMs, show promise in automating feature extraction from speech data for AD detection.
• Integrating linguistic assessments into cognitive evaluations can significantly enhance prediction accuracy.

Interpretation:

The study highlights the potential of LLMs in enhancing the accuracy of AD prediction through linguistic analysis, addressing the need for interpretable methods in clinical settings and their implications for patient care.

Limitations:

• Challenges regarding explainability and interpretability of deep learning models remain significant.
• Existing studies often provide limited examples for model performance, lacking comprehensive explanations.
• Potential biases in LLMs could affect the reliability of predictions.

Conclusion:

Incorporating linguistic assessments into cognitive evaluations could play a pivotal role in early detection and management of Alzheimer's Disease, particularly when combined with existing detection methods.