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AI research trends in liver cancer bibliometric analysis
Tingxuan Zhang1, Youhui Zheng, Chi Zhang
1Affiliated Hospital of Qinghai University, Xining, China.
Background:
The pathogenesis of liver cancer is complex, leading to poor prognosis. Early diagnosis and metastasis monitoring are crucial. With advancements in medical concepts and technologies, this field faces challenges and opportunities. This study explores the progress, hot topics, and future trends of artificial intelligence (AI) in liver cancer diagnosis and treatment. Using bibliometric methods, a comprehensive report on AI in liver diseases is compiled for researchers and practitioners.
Methods:
The Web of Science Core Collection database was used to retrieve AI-related liver cancer research literature from 2005 to 2024. Analytical tools like VOSviewer, Citespace, and RStudio were used for bibliometric analysis and knowledge map construction.
Results:
A total of 2922 papers were collected in this study, including 2607 original research articles and 315 review documents. In terms of publication volume, China leads the way, while the United States exhibits significant influence in this field with the highest h-index and total citation count. At the institutional level, the top 3 most productive institutions are the University of California System, Harvard University, and the University of London. Regarding author contributions, Loomba, Rohit is the author with the highest number of published papers, while Younossi, Z.M. has the highest number of co-citations. At the journal level, Scientific Reports and Hepatology rank first in terms of the number of published papers and co-citations, respectively, reflecting their importance and influence in this field. From the collected literature, 4419 keywords were extracted, with 119 appearing >20 times. Clustering analysis revealed 3 major clusters. Frequent keywords included "classification," "diagnosis," "survival," and "prediction," highlighting current research hotspots. Computed tomography is the most common data type used in AI liver cancer research, followed by magnetic resonance imaging and ultrasonography.
Conclusion:
Research on AI in liver cancer is still exploratory. With rapid AI advancements, its applications in diagnosis, treatment, and prevention are growing. This bibliometric study aims to analyze the current status of AI in liver cancer research, revealing potential directions and hotspots for further exploration and development.
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