LSEAD: Privacy-Preserving LLM Framework for Early Alzheimer's Screening via Speech
The newly introduced framework, LSEAD, utilizes pretrained open-source large language models (LLMs) to enable the speech-based early identification of Alzheimer's disease (AD). This innovative approach meets the demand for non-invasive and affordable screening across various clinical environments. By automatically transcribing speech recordings and generating text embeddings with locally deployed LLMs, it prioritizes user privacy by eliminating cloud processing. Dimensionality reduction is achieved through principal component analysis (PCA) prior to classification. Announced on arXiv (ID: 2608.07378), the study emphasizes the feasibility of speech analysis as a scalable screening method, especially in settings lacking specialized equipment. LSEAD's design focuses on accessibility and practicality, aiming to enhance timely interventions that could slow disease progression and improve patient outcomes, highlighting AI's expanding role in healthcare diagnostics for neurodegenerative diseases.
Key facts
- LSEAD is a speech-based AD detection framework using pretrained open-source LLMs.
- Speech recordings are automatically transcribed and text embeddings are extracted using locally deployed LLMs.
- Principal component analysis (PCA) is applied to reduce dimensionality before classification.
- The framework is designed for non-invasive and cost-effective screening in real-world clinical settings.
- The study is announced on arXiv with ID 2608.07378.
- Early diagnosis of Alzheimer's disease is critical for timely interventions.
- The framework relies on locally deployed LLMs to ensure privacy.
- The method leverages recent advances in LLMs for rich linguistic representations and strong generalization.
Entities
Institutions
- arXiv