Artificial intelligence and machine learning in neurodegenerative disease management: A 21st century paradigm

Neurodegenerative diseases represent a major and growing clinical challenge due to their progressive nature, biological heterogeneity, and limited therapeutic options. Recent advances in artificial intelligence (AI) have introduced new analytical strategies for extracting clinically relevant information from complex biomedical data, offering complementary tools to established diagnostic and research approaches. This review provides a critical and method-comparative synthesis of AI applications in neurodegenerative diseases, with emphasis on studies published between 2022 and 2025. Rather than cataloging algorithms, the review evaluates how specific AI methodologies are selected, implemented, and validated across diverse data modalities, including molecular profiles, neuroimaging, biosensors, speech, gait, and electronic health records. Across Alzheimer's disease, Parkinson's disease, and other neurodegenerative disorders, the reviewed evidence indicates that AI-based models can support early risk stratification, disease characterization, and monitoring when applied within clearly defined analytic and clinical contexts. Importantly, performance gains are shown to depend strongly on data quality, feature representation, validation design, and alignment between model architecture and biological signal, rather than on algorithmic complexity alone. Emerging paradigms, including multimodal integration and next-generation AI frameworks, are discussed in relation to their methodological contributions rather than clinical readiness. By systematically comparing analytical strategies and highlighting sources of variability across studies, this review underscores the importance of methodological transparency, uncertainty-aware evaluation, and biological interpretability. Collectively, the work positions AI as an enabling and adjunctive analytical framework that can enhance neurodegenerative disease research and clinical decision support when deployed with rigor and caution, providing a balanced perspective on current capabilities and future directions.