Published online Mar 27, 2025. doi: 10.4254/wjh.v17.i3.101721
Revised: January 1, 2025
Accepted: February 10, 2025
Published online: March 27, 2025
Processing time: 182 Days and 16.2 Hours
In recent years, the utilization of artificial intelligence (AI) technology has gained prominence in the field of liver disease.
To analyzes AI research in the field of liver disease, summarizes the current research status and identifies hot spots.
We searched the Web of Science Core Collection database for all articles and reviews on hepatopathy and AI. The time spans from January 2007 to August 2023. We included 4051 studies for further collection of information, including authors, countries, institutions, publication years, keywords and references. VOS viewer, CiteSpace, R 4.3.1 and Scimago Graphica were used to visualize the results.
A total of 4051 articles were analyzed. China was the leading contributor, with 1568 publications, while the United States had the most international collaborations. The most productive institutions and journals were the Chinese Academy of Sciences and Frontiers in Oncology. Keywords co-occurrence analysis can be roughly summarized into four clusters: Risk prediction, diagnosis, treatment and prognosis of liver diseases. "Machine learning", "deep learning", "convolutional neural network", "CT", and "microvascular infiltration" have been popular research topics in recent years.
AI is widely applied in the risk assessment, diagnosis, treatment, and prognosis of liver diseases, with a shift from invasive to noninvasive treatment approaches.
Core Tip: This study highlights the increasing annual publications on artificial intelligence in liver disease, with applications spanning risk assessment, diagnosis, treatment, and prognosis. China leads in publication output, whereas the United States remains a dominant force in the field. High-impact journals, authors, and institutions are identified, along with trends in international collaboration. Key research hotspots include "machine learning", "deep learning", "convolutional neural networks", "CT imaging", and "microvascular infiltration".