Optimizing and Evaluating Pre- Trained Large Language Models for Alzheimer's Disease Detection

This research focuses on developing improved diagnostic tools for Alzheimer's Disease (AD), a condition impacting approximately 50 million individuals globally. In the paper, we achieve automatic AD detection by leveraging pre-trained Large Language Models (LLMs) for linguistic analysis applied to the ADReSS/ADReSSo (Alzheimer's Dementia Recognition through Spontaneous Speech/only) Challenges datasets, following speech-to-text conversion. While the recent advancements in LLMs offer a robust foundation for their application in healthcare, fine-tuning these models for specific tasks, like AD detection, requires optimization to balance performance and computational efficiency. Also in response to data privacy concerns in healthcare, we implement our methodology on consumer-level GPU cards, which offer a practical solution for local data processing. Our approach uses fine-tuning techniques such as Low Ranking Adaptation and Parameter-Efficient Fine-Tuning to enhance the capabilities of Large Language Models within the limits of consumer-grade hardware. Additionally, we incorporate quantization to reduce computational demands while preserving model accuracy. Conducted on setups with RTX 4090 and dual RTX 3090 GPUs, our experiments demonstrate promising results that align with or surpass existing benchmarks in dementia recognition.