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LI Anqi, ZHAO Peiran, ZHAO Yuqiang, WANG Rui, YANG Jing. Application and prospect of artificial intelligence in metabolic associated fatty liver disease based on bibliometrics[J]. Journal of Clinical Medicine in Practice, 2024, 28(5): 1-9, 16. DOI: 10.7619/jcmp.20233487
Citation: LI Anqi, ZHAO Peiran, ZHAO Yuqiang, WANG Rui, YANG Jing. Application and prospect of artificial intelligence in metabolic associated fatty liver disease based on bibliometrics[J]. Journal of Clinical Medicine in Practice, 2024, 28(5): 1-9, 16. DOI: 10.7619/jcmp.20233487
shu

Application and prospect of artificial intelligence in metabolic associated fatty liver disease based on bibliometrics

  • School of Basic Medicine of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, 150040

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  • Received Date: October 31, 2023
  • Revised Date: December 25, 2023
  • Available Online: March 21, 2024
    Graphical Abstract
    • Abstract
  • Objective 

    To explore the application and prospects of artificial intelligence (AI) in metabolic associated fatty liver disease (MAFLD) based on bibliometrics.

    Methods 

    Relevant literature on the application of AI technology in MAFLD was retrieved from the Web of Science Core Collection (WoSCC) database. Bibliometric analysis was conducted using CiteSpace, VOSviewer, R package "bibliometrix", and online bibliometric analysis was platformed to identify hotspots and trends in this field.

    Results 

    A total of 303 eligible articles were obtained. Since 2017, the number of papers in this field had experienced explosive growth. The United States was leading the research on the application of AI in MAFLD and was the most frequent participant in international cooperation. San Diego of University of California was the institution with the highest number of publications. Rohit Loomba was the author with the highest number of publications, having published 14 articles. The co-cited keyword clustering labels revealed 10 major clusters: digital image analysis, machine learning, computer-aided diagnosis, fibrosis stage, automated quantitative analysis, metaproteomics, non-invasive diagnosis, ultrasonography, electronic health records, and knowledge representation. Current research on the application of AI in MAFLD mainly focused on the diagnosis, differential diagnosis, and staging of MAFLD. Image recognition and analysis, intelligent assisted diagnosis, AI algorithms, and monitoring disease progression will be important research directions for AI in MAFLD.

    Conclusion 

    Research on the application of AI in MAFLD is experiencing exponential growth. Given enormous potential and clinical application prospects of this field, the application of AI in MAFLD-related areas will remain a research hotspot in the future.

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    • References (38)
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