Sep 2026· Current Signal Transduction Therapy· 0 citations
TL;DR
The combination of artificial intelligence with traditional dementia treatment methods enables medical professionals to diagnose patients earlier, make better treatment decisions, and deliver customised care to individual patients.
Abstract
Dementia is a progressive, neurodegenerative disease that involves a
gradual loss of functions of the brain, such as memory, reasoning, and attention. This review
will assess the association between Artificial Intelligence (AI) technologies and the pharmacological
treatment of dementia for diagnosis and treatment.
A narrative review was conducted using literature retrieved from Google Scholar,
ScienceDirect, and PubMed databases. Relevant peer-reviewed studies published between 2000
and 2025 related to dementia, AI, machine learning, neuroimaging, and pharmacotherapy were
analysed. A total of 141 eligible articles were included and thematically reviewed.
Artificial Intelligence methods such as Convolutional Neural Networks (CNNs), Generative
Adversarial Networks (GANs), Support Vector Machines (SVMs), and Natural Language
Processing (NLP) technologies have significantly improved the detection and classification
of dementia using neuroimaging, speech analysis, and cognitive assessment data in very
early or early stages of diagnosis. Newer advanced neuroimaging techniques, such as MRI and
PET, provide clinicians with more accurate test results and allow for more precise measurement.
The continued use of pharmacological approaches, such as donepezil, rivastigmine, galantamine,
memantine, piracetam, and nootropic agents, remains the mainstay of symptomatic treatment
and support for additional cognition. The combination of AI-assisted drug discovery and
predictive modelling showed promise for developing personalised treatment methods while
speeding up the development of new therapies.
The combination of artificial intelligence with traditional dementia treatment methods
enables medical professionals to diagnose patients earlier, make better treatment decisions,
and deliver customised care to individual patients. Medical professionals face major challenges
that prevent them from using the technology due to issues with data protection, system understanding,
and medical validation.
The use of AI in diagnostic and treatment methods is a new development with potential
for improving dementia care. The process of developing dementia care solutions needs
more interdisciplinary research to improve AI models, test their clinical use, and develop successful
personalised treatment methods.
Overall, artificial intelligence has substantially advanced prediction research in cognitive decline and dementia, but its routine clinical adoption will depend on more rigorous validation, improved transparency, and stronger integration between neuroscience, clinical practice, and data science.
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This survey provides a comprehensive synthesis of EEG-based dementia studies published between 2020 and 2025, with a primary focus on Alzheimer’s disease, frontotemporal dementia (FTD), mild cognitive impairment (MCI), and related dementia disorders.
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Recent progress in AI-driven approaches for AD detection is discussed and the importance of interdisciplinary strategies to facilitate earlier diagnosis, improve patient outcomes, and support more effective management of Alzheimer's disease is underscored.
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The timely identification of Alzheimer’s Disease (AD) is crucial for the provision of adequate treatment to the patients. The traditional diagnostic techniques such as neuroimaging, CSF examination, and neuropsychological testing might be relatively expensive, invasive, and sensitive. Due to the development of Artifici...
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INTRODUCTION/OBJECTIVE
The progressive nature of dementia is represented by a reduction in two or more cognitive functions, including language, memory, executive and visuospatial functioning, personality, and behavior. Only symptoms can be alleviated by traditional medication therapies. A strategy that shows promise fo...
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Artificial intelligence-enabled approaches show promise for earlier and more objective ALS detection, but the current literature is dominated by small, retrospective, single-site studies.
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