AI-driven multimodal precision diagnosis and progression prediction of Alzheimer’s disease: Data fusion mechanisms, clinical applications, and research trends (2017–2024)

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.