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There is a scarcity of large-scale analysis of literature in this field based on quantitative approaches. This study performed a bibliometric and topic modeling examination on 683 articles from 2002 to 2022, focusing on research topics and trends, journals, countries\/regions, institutions, authors, and scientific collaborations. Results showed that, firstly, the number of articles has grown from 1 in 2002 to 220 in 2022, with a majority being published in interdisciplinary journals that link healthcare and medical research and information technology and AI. Secondly, the significant rise in the quantity of research articles can be attributed to the increasing contribution of scholars from non-English speaking countries\/regions and the noteworthy contributions made by authors in the USA and India. Thirdly, researchers show a high interest in diverse research issues, especially, cross-modality magnetic resonance imaging (MRI) for brain tumor analysis, cancer prognosis through multi-dimensional data analysis, and AI-assisted diagnostics and personalization in healthcare, with each topic experiencing a significant increase in research interest. There is an emerging trend towards issues such as applying generative adversarial networks and contrastive learning for multimodal medical image fusion and synthesis and utilizing the combined spatiotemporal resolution of functional MRI and electroencephalogram in a data-centric manner. This study is valuable in enhancing researchers\u2019 and practitioners\u2019 understanding of the present focal points and upcoming trajectories in AI-powered smart healthcare based on multimodal data analysis.<\/jats:p>","DOI":"10.1007\/s10462-024-10712-7","type":"journal-article","created":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T11:02:10Z","timestamp":1710500530000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":127,"title":["Artificial intelligence and multimodal data fusion for smart healthcare: topic modeling and bibliometrics"],"prefix":"10.1007","volume":"57","author":[{"given":"Xieling","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohui","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fu Lee","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingming","family":"Leng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baiying","family":"Lei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,15]]},"reference":[{"key":"10712_CR1","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1016\/j.inffus.2022.09.023","volume":"90","author":"M Abdar","year":"2023","unstructured":"Abdar M, Salari S, Qahremani S, Lam H-K, Karray F, Hussain S, Khosravi A, Acharya UR, Makarenkov V, Nahavandi S (2023) UncertaintyFuseNet: robust uncertainty-aware hierarchical feature fusion model with ensemble Monte Carlo dropout for COVID-19 detection. 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