Exploring Artificial Intelligence and Multimodal Data Integration in Smart Healthcare: A Bibliometric and Topic Modelling Approach

Abstract

Technological enhancements in intelligent computing or artificial intelligence (AI) have contributed immensely towards the enhancement of multimodal data fusion approaches, which are believed to transform smart healthcare. That is why, despite the increased interest in this sector, there are few studies that empirically examine organizational implementation of AI and multimodal data processing in large scale health care systems. This study employs bibliometric analysis and topic modelling to assess the trends, active research areas, impactful journals, countries, institutions, authors and collaboration network of 683 papers that have been published between 2002 and 2022. According to our findings, there has been a breakout of publications since 2013, and interdisciplinary journals are seen as central to growth in healthcare research in AI. Future research areas include intelligent diagnostics, multiple-modal neuroimaging. for brain tumor diagnosis, cancer prediction using data fusion techniques, and the fusion of fMRI and EEG data. This is also an increasing concern of countries like China, USA and India’s into the contribution of the field. In this paper, the authors provide a state-of-art review of existing and potential approaches as well as solutions to the multimodal data fusion in smart healthcare enabled by AI.