SynthesisFrontiers in neurology2024
The application of artificial intelligence in diagnosis of Alzheimer's disease: a bibliometric analysis.
Synthesis in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
- Article
- Artificial Intelligence in the Diagnosis of Neurological Diseases Using Biomechanical and Gait Analysis Data: A Scopus-Based Bibliometric Analysis.Neurology international · 2025Review
- Artificial intelligence in neurodegenerative diseases research: a bibliometric analysis since 2000.Frontiers in neurology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that severely impacts cognitive function, posing significant physical and psychological burdens on patients and substantial economic challenges to families and society, particularly in aging populations where its prevalence is rising. Current diagnostic and therapeutic strategies, including pharmacological treatments and non-pharmacological interventions, exhibit considerable limitations in early diagnosis, etiological treatment, and disease management. This study aims to investigate the application of artificial intelligence (AI) in the early diagnosis and progression monitoring of AD through a bibliometric analysis of relevant literature. A systematic search in the Web of Science Core Collection identified 530 publications related to AI and AD, consisting of 361 original research articles and 169 review articles, with a notable increase in annual publication rates, particularly between 2019 and 2024. The United States and China emerged as leading contributors, emphasizing the importance of international collaboration. Institutional analysis revealed that Harvard University and Indiana University System are at the forefront, highlighting the role of academic institutions in fostering interdisciplinary research. Furthermore, the Journal of Alzheimer's Disease was identified as the most influential publication outlet. Key highly cited papers provided essential theoretical foundations for ongoing research. This study underscores the growing relevance of AI in AD research and suggests promising avenues for future investigations, particularly in enhancing diagnostic accuracy and therapeutic strategies through advanced data analytics and machine learning techniques.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.