ARTIFICIAL INTELLIGENCE IN EARLY NEUROLOGICAL DIAGNOSIS: A REVIEW OF MACHINE LEARNING APPLICATIONS IN ALZHEIMER’S AND PARKINSON’S DISEASE SCREENING
DOI:
https://doi.org/10.31435/ijitss.2(50).2026.5356Keywords:
Artificial Intelligence; Machine Learning; Voice Analysis; Alzheimer’s Disease; Parkinson’s Disease; Healthcare CostsAbstract
Neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD), pose a growing challenge to aging societies, placing a burden on healthcare systems and patients' families. Traditional diagnostic methods often rely on already visible clinical symptoms, delaying the possibility of effective interventions. This review article analyzes the use of artificial intelligence (AI) and machine learning (ML) in early neurological diagnosis, with a particular focus on multimodal analysis, including magnetic resonance imaging (MRI) and behavioral markers. AI algorithms demonstrate a remarkable ability to identify subtle micro-changes in brain structure, as well as deviations in speech, behavior, or breathing, doing so much faster and more accurately than standard clinical assessment by the human eye or ear. Early detection of these prodromal signals not only revolutionizes the medical approach to patients but also has profound social and economic implications. Faster, AI-assisted diagnosis has been shown to significantly reduce the direct and indirect medical costs burdening global healthcare systems. Equally important, early detection of disease allows for faster initiation of therapy and supportive care, which directly translates into a better quality of life for patients and a significant reduction in the psychological, physical, and financial burdens on their families. The paper systematizes current knowledge on the effectiveness of AI models, pointing to their key role in building sustainable and pro-social healthcare for the future.
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