Jove
Visualize
Contact Us

Related Concept Videos

Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

418
Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
418
Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

145
Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
145

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

UQCRC1 deficiency impairs mitophagy via PINK1-dependent mechanisms in Parkinson's disease.

NPJ Parkinson's disease·2026
Same author

Dysregulated metabolism of ceramides and glycosphingolipids in Parkinson's disease.

Journal of lipid research·2025
Same author

Correction: Improving the Robustness and Clinical Applicability of Automatic Respiratory Sound Classification Using Deep Learning-Based Audio Enhancement: Algorithm Development and Validation.

JMIR AI·2025
Same author

Improving the Robustness and Clinical Applicability of Automatic Respiratory Sound Classification Using Deep Learning-Based Audio Enhancement: Algorithm Development and Validation.

JMIR AI·2025
Same author

A missense mutation in human INSC causes peripheral neuropathy.

EMBO molecular medicine·2024
Same author

Using Artificial Intelligence to Interpret Clinical Flow Cytometry Datasets for Automated Disease Diagnosis and/or Monitoring.

Methods in molecular biology (Clifton, N.J.)·2024
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 24, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

938

Profiling Patient Transcript Using Large Language Model Reasoning Augmentation for Alzheimer's Disease Detection.

Chin-Po Chen, Jeng-Lin Li

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new framework using large language models (LLMs) to analyze speech for Alzheimer's disease (AD) detection. The method improves accuracy by profiling linguistic deficits, enhancing early diagnosis potential.

    More Related Videos

    Author Spotlight: Exploring Sex-Specific Glial Signatures and Therapeutic Leads for Alzheimer's Disease
    04:22

    Author Spotlight: Exploring Sex-Specific Glial Signatures and Therapeutic Leads for Alzheimer's Disease

    Published on: May 20, 2024

    743
    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    475

    Related Experiment Videos

    Last Updated: May 24, 2025

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
    09:47

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

    Published on: December 15, 2023

    938
    Author Spotlight: Exploring Sex-Specific Glial Signatures and Therapeutic Leads for Alzheimer's Disease
    04:22

    Author Spotlight: Exploring Sex-Specific Glial Signatures and Therapeutic Leads for Alzheimer's Disease

    Published on: May 20, 2024

    743
    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    475

    Area of Science:

    • Computational linguistics
    • Neurodegenerative disease research
    • Artificial intelligence in healthcare

    Background:

    • Alzheimer's disease (AD) is a leading cause of dementia, marked by progressive speech and language decline.
    • Current automated detection methods using speech transcripts lack global linguistic insight, limiting accuracy and interpretability.
    • Large language models (LLMs) offer advanced reasoning but are underutilized for AD detection and interpretation.

    Purpose of the Study:

    • To develop a novel patient-level transcript profiling framework for Alzheimer's disease (AD) detection.
    • To leverage LLM-based reasoning to systematically identify and analyze linguistic deficit attributes.
    • To enhance the discriminability and interpretability of automated AD detection models.

    Main Methods:

    • A framework was designed to augment patient speech transcripts with LLM-based reasoning.
    • Linguistic deficit attributes were systematically elicited and summarized into embeddings.
    • These embeddings were integrated into an Albert model for AD detection.

    Main Results:

    • The proposed framework demonstrated significant improvements in accuracy (ACC) and F1 score on the ADReSS dataset compared to baseline methods.
    • Specifically, an 8.51% ACC and 8.34% F1 improvement was achieved through LLM-based reasoning augmentation.
    • Further analysis confirmed the effectiveness of the identified linguistic deficit attributes.

    Conclusions:

    • LLM-based reasoning augmentation offers a promising approach to enhance AD detection from speech.
    • The framework provides a more interpretable method for identifying linguistic markers of Alzheimer's disease.
    • This approach holds potential for improving early diagnosis and understanding of AD through speech analysis.