Evaluating the Role of AI Assistants in Accelerating Neurodegenerative Disease Research: Opportunities and Translational Limitations.

Neurodegenerative diseases including Alzheimer's disease and Parkinson's disease remain among the most challenging disorders to study, diagnose and treat. Despite rising prevalence with population aging, disease-modifying therapies remain scarce and research progress is hindered by biological complexity, patient heterogeneity, and incomplete experimental systems. Artificial intelligence (AI) has emerged as a transformative approach given the high-dimensional and multimodal data generated in this field. Traditional machine learning and deep learning have advanced imaging biomarker detection, disease trajectory prediction, drug target prioritization, and clinical data mining. More recently, foundation models and large language models (LLMs) have expanded AI from task-specific prediction tools to versatile assistants supporting literature retrieval, summarization, coding, data interpretation, and hypothesis generation. Although AI assistants promise to accelerate research workflows, their outputs are prone to dataset biases, poor interpretability, distribution shift, and hallucinations, which are particularly problematic in neurodegenerative research given subtle phenotypic variations, imperfect labeling, and protracted disease courses. This review evaluates the current applications of AI in neurodegenerative disease research, including drug discovery, biomarker identification, and multi-omics integration. We then discuss the transition from analytical AI models to general-purpose AI assistants and their potential to streamline scientific workflows. Critical limitations including bias, interpretability, reproducibility, and LLM hallucinations are highlighted, alongside ethical, regulatory and practical challenges. We argue that the most sustainable near-term model is human-AI collaboration rather than fully autonomous research, with its primary focus placed on research rather than clinical practice. Meaningful acceleration requires rigorous validation, transparent usage, and expert oversight to preserve scientific rigor and translational relevance.