Abstract 7447: Global mapping of artificial intelligence applications in gastric cancer from 1993-2024

Abstract Background: Artificial intelligence (AI) emerged as a promising tool, including machine learning and deep learning, the Internet of Things, and blockchain technology have created new opportunities for improving healthcare delivery and outcomes for gastric cancer (GC) patients. This study aimed to analyze research development, patterns, and trends in AI applications on GC. Methods: Publications related to AI applications in GC from 1993 to 2024 were extracted from the Web of Science (WoS) database (assessed date: Sep 13, 2024). To explore the relationships among the most frequently occurring terms in titles and abstracts, we employed the text-mining functionality of VOSviewer to generate a co-occurrence network. Global collaborations, research disciplines, and frequently used terms were visualized using this software. This study applied a machine learning approach, Latent Dirichlet Allocation, and dendrogram analysis was employed to identify hidden topics and hierarchical clustering of key research domains. A linear regression model was utilized to determine research development trends. Results: The bibliometric analysis encompassed 1, 717 articles from 70 countries/regions, with China, Japan, and the United States making outstanding contributions. The study revealed an unequal distribution of publications, with low/middle-income countries (LMICs) facing the greatest burden of cancer yet receiving inadequate attention. AI has confirmed its role in addressing global challenges, especially during the most stressful period, such as the COVID-19 pandemic, leading to a dramatic increase in publications. Topic modeling identified that “Comparative Analysis of Robotic vs. Laparoscopic Gastrectomy” had the highest interest with the highest number of publications, however, it shows a consistent downward trend in the last 5 years. The six remaining topics included AI applications in Tumor Segmentation, Predictive Models for Neoadjuvant Chemotherapy Response, Global Research Trends, Dietary and Environmental GC Prevention, and Cell-Free DNA and Biomarkers were considered hot trends. Hierarchical clustering of research areas grouped Computer Science and AI-related fields, underscoring their importance in developing AI methods supporting GC research and treatment. Conclusion: Research on AI in GC is increasing attention. These findings suggest future studies should focus more on LMICs to provide a comprehensive overview of AI applications in GC. AI Application on GC highlights the interdisciplinary nature of research. Although AI has positive clear effects on the development of GC research, it still has certain limitations, there is a need to develop AI algorithms capable of supporting pre-surgical planning, establishing surgical protocols and post-operative monitoring to alleviate healthcare system burdens, enhancing surgical quality, and improve the quality of life for GC patients. Citation Format: Trong Anh Vu Dam, Thi Huyen Trang Nguyen, Jeongseon KIM. Global mapping of artificial intelligence applications in gastric cancer from 1993-2024 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7447.