Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into biomarker research to improve the diagnosis, prognosis, and monitoring of neurodegenerative diseases (NDs), including Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, and related disorders. Advances in neuroimaging, fluid biomarkers, multi-omics technologies, and digital health platforms have generated complex datasets that are well suited for AI-driven analysis. Although numerous studies have reported promising diagnostic and predictive performance, the clinical translation of AI-enabled biomarker assays remains limited. This review critically examines the analytical and computational challenges that affect the reliability, reproducibility, and clinical applicability of AI-driven biomarker assays in neurodegenerative diseases. Key analytical challenges include pre-analytical variability, assay standardization, measurement error, inter-laboratory reproducibility, and disease-specific biomarker limitations. Computational barriers include high-dimensional data, limited sample sizes, overfitting, inadequate external validation, algorithmic bias, data imbalance, lack of transparency, and poor reproducibility of machine learning workflows. The review also discusses challenges associated with multimodal biomarker integration and the development of generalizable and interpretable predictive models. Emerging strategies to address these limitations are highlighted, including quality-by-design approaches, biomarker harmonization initiatives, robust validation frameworks, explainable AI, federated learning, privacy-preserving analytics, and regulatory-aligned model development. By integrating analytical, computational, and translational perspectives, this review provides a comprehensive framework for improving the reliability and clinical utility of AI-driven biomarker assays. Addressing these challenges will be essential for advancing precision neurology and enabling the deployment of clinically actionable biomarker technologies for neurodegenerative diseases.
Kumbhar et al. (Mon,) studied this question.