Artificial intelligence (AI) is rapidly transforming research in neurodegenerative diseases, yet its clinical translation remains limited. We conducted a structured literature search across Google Scholar, PubMed, Scopus, and Web of Science, screening studies published between 2015 and April 2026 that applied machine learning (ML), deep learning (DL), and multimodal data integration to neuroimaging, biomarkers, and digital phenotyping. Our analysis revealed that AI models demonstrate strong potential for differentiating disease subtypes, predicting progression, and enhancing diagnostic accuracy, with notable advances in neuroimaging interpretation, fluid biomarker analysis, and wearable sensor data. In Parkinson’s disease (PD), digital phenotyping through gait, speech, and handwriting analysis has enabled sensitive monitoring, while in Alzheimer’s disease (AD), AI applied to imaging and plasma biomarkers has improved risk stratification. Despite these advances, barriers such as dataset heterogeneity, label noise, lack of external validation, and ethical concerns regarding bias, transparency, and patient trust persist. We conclude that while AI holds promise to revolutionize the care of PD and AD, real-world adoption requires multicenter validation, standardized reporting frameworks, regulatory guidance, and interdisciplinary collaboration, alongside prospective trials that embed AI tools into clinical workflows to ensure safety, equity, and effectiveness.
Pinnelli et al. (Thu,) studied this question.
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