AI-Driven Multimodal Approaches for the Diagnosis and Progression Analysis of Neurodegenerative Diseases: A Systematic Survey
Abstract
Neurodegenerative diseases such as Alzheimer's, Parkinson's, and Amyotrophic Lateral Sclerosis (ALS) progress gradually but ultimately impair memory, movement, and daily functioning. Discovering these conditions at an early stage remains difficult because traditional clinical examinations and imaging techniques are usually slow, subjective, and not always sensitive to changes. In recent years, Artificial Intelligence (AI) has shown strong potential by combining information from different sources such as brain scans, speech, handwriting, gait, and cognitive assessments to provide more accurate and timely support for diagnosis and monitoring. This survey follows the PRISMA framework to ensure a clear and structured process of selecting literature. The review covers a wide spectrum of AI techniques, from classical machine learning to deep learning, transfer learning, and ensemble approaches, while also noting the growing exploration of emerging directions such as quantum computing. Widely used datasets including ADNI, PPMI, OASIS, and TADPOLE are summarized, along with recurring challenges such as imbalanced data, differences between datasets, and the computational cost of advanced models. We also highlight the increasing importance of multimodal fusion, data augmentation, and federated learning in building clinically practical systems. Finally, the paper outlines open issues and future opportunities, emphasizing the need for explainable and lightweight AI that can be…
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