Current status and trends of machine learning applied in clinical research of gastric cancer from 2004 to 2023: global bibliometric and visual analysis

TL;DR

This study maps global research trends in machine learning for gastric cancer from 2004 to 2023, highlighting shifts toward clinical applications.

Contribution

The paper provides the most detailed bibliometric analysis of gastric cancer and machine learning research to date.

Findings

China leads in gastric cancer and machine learning research output and collaboration.

Research focus has shifted from etiology to clinical applications and treatment strategies.

AI-assisted early diagnosis and treatment may become key future research directions.

Abstract

Gastric cancer is a serious disease that threatens human life; early diagnosis and treatment have been the focus of many studies. With advancements in imaging evaluation and machine learning, early detection and treatment of gastric cancer have become feasible. This study aimed to explore research trends and hotspots in the field of gastric cancer and machine learning through bibliometric analysis and to provide new insights for related clinical applications.

Literature on gastric cancer and machine learning published from 2004 to 2023 was retrieved from the Web of Science database. Microsoft Excel 2019 was used for statistical analysis of influential articles, journals, authors, organizations, countries (regions), and co-citation references in this research domain. VOSviewer (version 1.6.16) and CiteSpace (version 5.8.R3) were utilized to visualize the corresponding data.

We analyzed and evaluated 425 articles authored by 2,899 researchers from 825 organizations across 52 countries (regions). The People’s Republic of China, the Chinese Academy of Sciences, and the University of the Chinese Academy of Sciences were identified as leaders in this field. The article “Genome-wide cell-free DNA fragmentation in patients with cancer,” published in Nature, was the most frequently cited work. The diagnosis and treatment of gastric cancer have consistently been research hotspots, with a shift in focus from laboratory-based studies to clinical applications. This trend highlights the transition from etiology-oriented research to studies emphasizing treatment and practical applications.

This study offers a comprehensive visual analysis of research on gastric cancer and machine learning, representing the most detailed bibliometric study in this domain. With the continuous advancement of research, artificial intelligence-assisted early diagnostic methods for gastric cancer and corresponding treatment strategies may emerge as a pivotal direction for future research in this area.

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