A bibliometric and visual analysis of artificial intelligence technologies-enhanced brain MRI research

Xieling CHEN, Xinxin ZHANG, Haoran XIE, Xiaohui TAO, Fu Lee WANG, Nengfu XIE, Tianyong HAO*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

12 Citations (Scopus)


With the advances and development of imaging and computer technologies, the application of artificial intelligence (AI) in the processing of magnetic resonance imaging (MRI) data has become a significant research field. Based on 2572 research articles concerning AI-enhanced brain MRI processing, this study provides a latent Dirichlet allocation based bibliometric analysis for the exploration of the status, trends, major research issues, and potential future directions of the research field. The trend analyses of articles and citations demonstrate a flourishing and increasing impact of the research. Neuroimage is the most prolific and influential journal. The USA and University College London have contributed the most to the research. The collaboration between European countries is very close. Essential research issues such as Image segmentation, Mental disorder, Functional network connectivity, and Alzheimer’s disease have been uncovered. Potential inter-topic research directions such as Functional network connectivity and Mental disorder, Image segmentation and Image classification, Cognitive impairment and Diffusion imaging, as well as Sense and memory and Emotion and feedback, have been highlighted.
Original languageEnglish
Pages (from-to)17335-17363
JournalMultimedia Tools and Applications
Issue number11
Early online date11 Jun 2020
Publication statusPublished - May 2021


  • Artificial intelligence
  • Latent Dirichlet allocation
  • Magnetic resonance imaging
  • Research topics


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