While the number of patients with dementia is rapidly increasing and becoming a social issue on a global scale, there is currently no effective cure for dementia. Hence the emphasis is on preventing its onset, especially at the early stages. This is the second volume of the special issue that introduces use of cutting-edge information technology such as Artificial Intelligence (AI), Machine Learning (ML) especially the Deep Learning, and sensing technologies such as Electroencephalogram (EEG) for the early diagnosis of dementia and its improvement of accuracy, which hopefully contributes to a decrease with some global impacts the number of the patients with dementia.
Six papers have been accepted in this volume, that makes eleven papers total with five from the previous one. Five of the six use machine learning with the three for detection and prediction purposes. Three of those five use the modern deep learning algorithms while the remaining two use the classic algorithms. One unique approach is with EEG and a simplified version (a shallow architecture) of the deep learning neural networks in order to detect an early stage of dementia. We notice more contributions using AI and ML at this time.
Advancement of AI never stops but rather accelerates further. Since 2020, generative AI, especially Generative Pre-trained Transformer (GPT) has arisen and made significant impacts. That has been changing the entire game of information technologies that centers natural languages for coding and various task performance. We are very excited that the wave of this new AI will reach the diagnosis of dementia soon and even the medicine in general. We believe that AI and medical staff collaboratively perform various medical operations and tasks very near future.
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Author contributions
AI: Writing – review & editing.
Conflict of interest
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Summary
Keywords
machine learning, diagnosis, dementia, prevention, prediction
Citation
Inoue A (2025) Editorial: Application of machine learning in the diagnosis of dementia, volume II. Front. Neurol. 16:1603844. doi: 10.3389/fneur.2025.1603844
Received
01 April 2025
Accepted
12 May 2025
Published
23 May 2025
Volume
16 - 2025
Edited and reviewed by
Bruce Miller, University of California, San Francisco, United States
Updates
Copyright
© 2025 Inoue.
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*Correspondence: Atsushi Inoue email@inoueatsushi.net
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.