(untitled) (Web of Teachers: Inderscience Research, 2026)
Neurodegenerative diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS), represent a growing global health burden, with millions of individuals affected worldwide and limited disease-modifying therapies available. Early and accurate diagnosis is critical for improving patient outcomes, yet conventional diagnostic approaches often fail to detect pathological changes prior to overt symptom onset. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) methodologies, has demonstrated remarkable promise in transforming early diagnostic paradigms through the analysis of neuroimaging, genetic, clinical, and digital biomarker data. This review synthesizes current evidence on AI applications in the early detection and differential diagnosis of major neurodegenerative conditions, examining the types of data modalities employed, algorithmic approaches, and reported diagnostic performance metrics. Convolutional neural networks (CNNs) applied to structural and functional MRI, PET imaging, and retinal scans have achieved diagnostic accuracies exceeding 90% in distinguishing AD from normal aging and mild cognitive impairment (MCI). Natural language processing (NLP) tools and speech analysis algorithms have been applied to detect subtle linguistic and acoustic markers predictive of neurodegeneration years before clinical diagnosis. Despite these advances, significant challenges persist, including limited dataset diversity, lack of prospective validation, interpretability concerns, and ethical issues surrounding data privacy and algorithmic bias. Federated learning and explainable AI frameworks offer potential solutions to enhance generalizability and clinical trustworthiness. The integration of multimodal AI platforms into clinical workflows requires interdisciplinary collaboration among neurologists, data scientists, ethicists, and policymakers. This review underscores the transformative potential of AI for early neurodegenerative disease diagnosis while emphasizing the necessity for rigorous validation, regulatory oversight, and equitable implementation to realize clinical benefits.