ReviewCureus2026
Artificial Intelligence for the Diagnosis and Management of Neurodegenerative Diseases: A Comprehensive Review With an Emphasis on Parkinson's and Alzheimer's Diseases.
Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.