A Machine Learning Framework for Cognitive Impairment Screening from Speech with Multimodal Large Models.

ABSTRACT: Background: Early diagnosis of Alzheimer's disease (AD) is essential for slowing disease progression and mitigating cognitive decline. However, conventional diagnostic methods are often invasive, time-consuming, and costly, limiting their utility in large-scale screening. There is an urgent need for scalable, non-invasive, and accessible screening tools. Methods: We propose a novel screening framework combining a pre-trained multimodal large language model with structured MMSE speech tasks. An artificial intelligence-assisted multilingual Mini-Mental State Examination system (AAM-MMSE) was utilized to collect voice data from 1098 participants in Sichuan and Chongqing. CosyVoice2 was used to extract speaker embeddings, speech labels, and acoustic features, which were converted

SUBMITTER: Chen S 

PROVIDER: S-EPMC12837762 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature