ReviewDigital health
AI-driven multimodal precision diagnosis and progression prediction of Alzheimer's disease: Data fusion mechanisms, clinical applications, and research trends (2017-2024).
Review in Digital health. 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: This study combines bibliometric and structured analyses to comprehensively examine the development, methodological characteristics, and application trends of multimodal artificial intelligence (AI) in Alzheimer's disease (AD) diagnosis. Materials and Methods: Literature from January 1, 2017 to December 31, 2024, was retrieved from the Web of Science Core Collection. Retrospective bibliometric and visual analyses were conducted using VOSviewer, CiteSpace, and the Bibliometrix R package. Results: A total of 234 papers were identified, showing a continuous increase in publication volume, with the United States and China as dominant contributors. The analysis focused on data modalities, fusion architectures, and clinical applications. Data trends highlight the fusion of imaging data with genetics, biomarkers, and clinical data. Methodologically, five fusion approaches were categorized, with intermediate fusion being the most widely used strategy for its ability to balance heterogeneous data integration. In application, multimodal AI demonstrated clear advantages in early diagnosis, disease classification, and progression prediction. Conclusion: Research on multimodal AI for AD has gained global attention and remains a key direction for diagnostic innovation. By synthesizing bibliometric insights with structured analyses of modalities and fusion strategies, this study offers a systematic understanding of current progress and provides valuable guidance for future methodological and translational research.
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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.