Abstract

THE ROLE OF ARTIFICIAL INTELLIGENCE IN ADVANCING ALZHEIMER'S DISEASE DIAGNOSTICS: A REVIEW

Siju Ellickal Narayanan, Hariraj Narayanan and Manju Sunder D.*

Abstract

Alzheimer’s disease (AD) is a leading cause of dementia, marked by gradual memory loss, cognitive deterioration, and behavioural changes. Traditional diagnostic approaches often identify the disease only in its advanced stages, limiting the effectiveness of available treatments. Recent developments in artificial intelligence (AI), especially in machine learning and deep learning techniques, have opened new avenues for the early and accurate detection of AD. These technologies can process and interpret complex biomedical data, such as brain imaging (MRI, PET), genetic markers, cerebrospinal fluid biomarkers, and cognitive test results, with remarkable precision. AI-driven systems have shown the ability to distinguish Alzheimer’s from other neurological conditions and to forecast disease progression more effectively than some conventional methods. However, widespread clinical adoption is still hindered by issues like data variability, lack of model transparency, and integration challenges. This paper highlights the growing role of AI in enhancing diagnostic accuracy and stresses the importance of using diverse, high-quality datasets and interpretable models. Integrating AI into diagnostic workflows may lead to earlier interventions, better patient care, and progress toward more individualized treatment strategies in Alzheimer’s disease.

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