Abstract

Neurodegenerative diseases, including Alzheimer’s, Parkinson’s, and ALS, present significant diagnostic challenges due to their complex pathology, overlapping symptoms, and reliance on late stage clinical evaluations. raditional diagnostic methods often suffer from invasiveness, subjectivity, and delays, leaving a critical gap in early detection and timely intervention. This review highlights the transformative role of artificial intelligence (AI) in addressing these limitations by leveraging its ability to process vast, complex datasets and uncover subtle patterns linked to disease onset and progression. AI techniques, including machine learning, deep learning, and natural language processing, are successfully applied to neuroimaging, omics analysis, and behavioral assessments, demonstrating remarkable potential for improving diagnostic accuracy and prediction. The paper also addresses the challenges of data quality, model interpretability, and ethical concerns, while emphasizing emerging solutions like explainable AI and federated learning. Through interdisciplinary collaboration and responsible innovation, AI offers a promising pathway to enhance early diagnosis, personalized care, and research in neurodegenerative diseases.

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Data Sharing Statement

Funding