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Related Concept Videos

Alzheimer Disease l: Introduction01:29

Alzheimer Disease l: Introduction

Alzheimer disease is a chronic, progressive, and irreversible neurodegenerative disorder and the most common cause of dementia in older adults. It leads to gradual neuronal loss, causing cognitive decline, behavioral changes, and loss of functional independence.Risk Factors and EtiologyThe disease is multifactorial. Age is the strongest risk factor, with prevalence doubling every 5 years after age 65. Genetic factors include mutations in genes such as APP, PSEN1, and PSEN2, which are associated...
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Related Experiment Video

Updated: May 31, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Advancing Alzheimer Disease Prediction With Large Language Model-Based Linguistic Feature Analysis: Development and

Ming-Hsia Hsu1,2, San-Yih Hwang1, Yi-Hang Tsai1

  • 1Department of Information Management, National Sun Yat-sen University, No. 70, Lienhai Rd, Kaohsiung, 804201, Taiwan, +886-7-5252000 ext 4723.

JMIR Medical Informatics
|May 28, 2026
PubMed
Summary

This study introduces a novel framework using large language models (LLMs) for early Alzheimer disease (AD) detection via speech analysis. The AI-driven approach achieves high accuracy and interpretability, offering a scalable, noninvasive diagnostic tool.

Keywords:
alzheimer diseaseearly detectionlarge language modelslinguistic featuresprompt engineering

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Last Updated: May 31, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

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

Area of Science:

  • Artificial Intelligence in Medicine
  • Computational Linguistics
  • Neurodegenerative Disease Diagnostics

Background:

  • Alzheimer disease (AD) is a growing global health concern, necessitating early detection for effective intervention.
  • Current diagnostic methods for AD are often invasive and costly.
  • Speech analysis offers a noninvasive alternative, as AD impacts linguistic abilities.

Purpose of the Study:

  • To investigate the efficacy of linguistic features extracted by large language models (LLMs) for Alzheimer disease (AD) prediction.
  • To enhance the accuracy and clinical interpretability of automated AD detection using speech data.
  • To develop a transparent and clinically applicable AI framework for early AD identification.

Main Methods:

  • Proposed a framework leveraging LLMs to analyze linguistic features (readability, fluency, detail richness, keyword relevance) from transcribed speech for AD classification.
  • Integrated transcript and feature explanation embeddings to improve classification accuracy.
  • Conducted ablation studies, benchmarked against existing LLM methods, and assessed output stability and privacy-preserving deployment feasibility (Llama 3 8B + nomic-embed-text).

Main Results:

  • Achieved high performance on the ADReSSo 2021 dataset (91.52% precision, 91.08% sensitivity, 96.29% specificity, 91.05% F1-score) across three runs.
  • Demonstrated framework transferability to privacy-preserving environments with a fully local configuration achieving an 81.58% F1-score.
  • Keyword relevance emerged as the most influential feature; LLM-based explainability favored the proposed method over a benchmark (49/54 cases).

Conclusions:

  • Structured linguistic feature analysis using LLMs provides a robust and interpretable framework for preliminary Alzheimer disease (AD) detection.
  • The developed approach bridges AI-driven text analysis with clinical applications, supporting early cognitive decline detection.
  • Offers a scalable and accessible noninvasive method for early AD identification through speech assessment.