Projects per year
With the fast progress in information technologies and artificial intelligence (AI), smart healthcare has gained considerable momentum. By using advanced technologies like AI, smart healthcare aims to promote human beings’ health and well-being throughout their life. As smart healthcare develops, big healthcare data are produced by various sensors, devices, and communication technologies constantly. To deal with these big multi-source data, automatic information fusion becomes crucial. Information fusion refers to the integration of multiple information sources for obtaining more reliable, effective, and precise information to support optimal decision-making. The close study of information fusion for healthcare with the adoption of advanced AI technologies has become an increasingly important and active field of research. The aim of this is to present a systematic description and state-of-the-art understanding of research about information fusion for healthcare with AI. Structural topic modeling was implemented to detect major research topics covered within 351 relevant articles. Annual trends and correlations of the identified topics were also investigated to identify potential future research directions. In addition, the primary research concerns of top countries/regions, institutions, and authors were shown and compared. The findings based on our analyses provide scientific and technological perspectives of research on information fusion for smart health with AI and offer useful insights and implications for its future development. We also provide valuable guidance for researchers and project managers to allocate research resources and promote effective international collaborations.
Bibliographical noteFunding Information:
Funding: The research has been supported by Lam Woo Research Fund (LWP20019), Shenzhen University-Lingnan University Joint Research Programme (SZU-LU006/2122), the research grant entitled "Self-Supervised Learning for Medical Images" (871228), and the Faculty Research Grants (DB22B4) of Lingnan University, Hong Kong, and the Interdisciplinary Research Scheme of the Dean's Research Fund 2020-21 (FLASS/DRF/IDS-3) of The Education University of Hong Kong.
- Information fusion
- Artificial intelligence
- Smart healthcare
- Structural topic modeling
- Topic evolution
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- 1 Finished
Developing Diagnosis System based on Self-Supervised Adversarial Learning for Babies with Congenital Heart Defects
1/07/22 → 30/06/23
Project: Grant Research