BACKGROUND
The application of artificial intelligence (AI) has increasingly been used in various medical fields, including metabolic dysfunction-associated fatty liver disease (MAFLD). This study endeavors to undertake a bibliometric analysis to predict the research hotspots and current advancements in AI permeating the field of MAFLD, to provide valuable information that could serve for further precision medicine.
OBJECTIVE
Using bibliometric analysis to review the research hotspots and collaborative networks of the application of artificial intelligence in the field of MAFLD in the past years.
METHODS
Publications were on the application of AI within the MAFLD domain from the Web of Science Core Collection (WoSCC), encompassing the period between 2009 and 2022. After undergoing a manual selection process, the target variables were analyzed by using Microsoft Excel 2019. Bibliometrix, VOSviewer, and CiteSpace knowledge mapping tools were used to analyze the number of publications, countries, institutions, authors, journals, references, and keywords in this field while yielding the visualization results in the form of a map.
RESULTS
From 2009 to 2022, a total of 164 publications related to the application of AI in the field of MAFLD were published, which covered 1,237 authors from 42 different countries/regions, across 475 institutions. Specifically, the United States emerged as the leading contributor with 62 publications, followed by China (n=43) and the United Kingdom (n=26). PLoS ONE was the most published (n=6) and most cited journal (n=148). The most effective institution and authors in this field were the University of California, San Diego, and Loomba Rohit. Keywords analysis showed that AI, machine learning, and deep learning were hotspots in the field of MAFLD and non-alcoholic steatohepatitis (NAS).
CONCLUSIONS
The application of AI in the field of MAFLD has held significant potential and received increasing interest from scholars. It can be anticipated that non-invasive diagnosis and accurate minimally invasive treatment through AI, in particular, deep learning technologies, will still be the focus of research in the future.