Article Details

Abstract

This research conducts bibliometric and visualization analysis of literature related to medical multimodal technologies from 2015 to 2025, based on the Web of Science and CNKI databases, with a focus on research hotspots and development trends in the fields of image segmentation and fusion. A total of 1,187 papers are included. CiteSpace is employed to analyze author and country/region collaborations, institutional distribution, annual publication trends, keyword co-occurrence, clustering, and burst features. Results indicate sustained rapid growth in publications over the past decade, with a marked acceleration after 2023. China, the United States, and India make prominent contributions. Core research themes include feature extraction, Deep Learning, image fusion, electronic health records, multimodal registration, lung cancer, and functional Magnetic Resonance Imaging (fMRI). Among these, “image fusion” emerges as the earliest and most intense burst term. Research focus is shifting from multimodal image fusion toward multi-source heterogeneous data fusion. Future development directions will likely center on constructing medical multimodal pre-training datasets and integrating electronic medical records with imaging data modeling.

Keywords

multimodal; image segmentation; image fusion; CiteSpace; Deep Learning

References

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Funding

Basic Research Funds for Central Universities (25CAFUC09011)

About the authors

Tang Anni (1999-), female, Han ethnicity, Chongqing native, pursuing a master's degree in medical big data and artificial intelligence; Xu Haiwen (1978-), male, Han nationality, from Heze, Shandong, professor, doctoral student, research direction: big data technology and models, optimization theory and algorithms.