Detecting Alzheimer's Symptoms with AI and Bridging the Arabic Resource Gap

This proposal presents a comprehensive framework for creating the first unified Arabic speech dataset for early Alzheimer’s detection using AI. It addresses the critical research gap of missing Arabic resources, as current datasets focus only on English, Mandarin, and German. The work proposes building a dialect-aware speech pipeline that respects Arabic linguistic and cultural diversity to enable accurate and fair local diagnostic tools.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-23
DOI
https://doi.org/10.5281/zenodo.20355200
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
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article

Detecting Alzheimer's Symptoms with AI and Bridging the Arabic Resource Gap

Adam Emad Bakr
Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Healthcare
article

Detecting Alzheimer's Symptoms with AI and Bridging the Arabic Resource Gap

Adam Emad Bakr
article en

Abstract

This proposal presents a comprehensive framework for creating the first unified Arabic speech dataset for early Alzheimer’s detection using AI. It addresses the critical research gap of missing Arabic resources, as current datasets focus only on English, Mandarin, and German. The work proposes building a dialect-aware speech pipeline that respects Arabic linguistic and cultural diversity to enable accurate and fair local diagnostic tools.

Zenodo (CERN European Organization for Nuclear Research)
Boys Town (US)
Quality Education
Openalex Percentile: Top 4%
Machine Learning in Healthcare
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Detecting Alzheimer's Symptoms with AI and Bridging the Arabic Resource Gap — Adam Emad Bakr · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS