Currently submitted to: Journal of Medical Internet Research
Date Submitted: May 4, 2026
Open Peer Review Period: May 7, 2026 - Jul 2, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Multilingual Natural Language Processing for Alzheimer’s Disease Detection: A Scoping Review of Datasets, Modeling Approaches, and Research Opportunities
ABSTRACT
Background:
Spoken language analysis is a compelling tool for detecting signs of Alzheimer’s disease (AD). However, most language-based AD detection resources are only available in English, leaving multilingual or crosslingual AD detection understudied.
Objective:
We review the current state of the field with respect to this topic, compiling recent approaches, constraints, and potential solutions from original work published across engineering, natural language processing, and medical databases from 2004 to the present.
Methods:
From the 776 search results, we identified 43 articles meeting predefined eligibility criteria and summarized their findings.
Results:
Promising results are reported in almost all studies, but few are integrated into clinical practice. The main limitations of the field are poor standardization, the lack of benchmark data repositories (which in turn hinders direct results comparison), and some disconnect between the study goals and the clinical applications.
Conclusions:
Active efforts from the research community and industry to close these gaps would support robust integration of research across languages into clinical practice.
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Copyright
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