The role of artificial intelligence in immune checkpoint inhibitor research: A bibliometric analysis - Ziqi Zhao; Kun Xu; Yizhuo Jiang; Xisheng Xu; Yuliang Liu (Human Vaccines & Immunotherapeutics)
Immune checkpoint inhibitors (ICIs) are revolutionizing cancer treatment, and Artificial Intelligence (AI) is a key player in this field. A comprehensive analysis of AI’s impact on these inhibitors was lacking, but this study addresses that by analyzing literature for trends and future predictions. It reveals rapid growth and international collaboration. We utilized analytical tools such as CiteSpace, VOSviewer, and PlotDB to analyze 774 documents from the Web of Science Core Collection from 2018 to May 2024, discovering a steady increase in annual publications, with China and the United States leading the way. Sun Yat Sen University and researchers like Ock Chan-young, Zhang Hao, and Newman AM are prominent. The most productive journal is Frontiers in Immunology, while the New England Journal of Medicine has the highest citation rate. The most cited reference is Newman, AM’s 2019 article in Nature Biotechnology. Keywords like “immunotherapy,” “pembrolizumab,” “cancer,” “machine learning,” and “expression” are central to the discourse. Research focuses on the application of inhibitors in non-small cell lung cancer, bioinformatics, and cancer immunotherapy, showing AI’s potential to improve oncology precision medicine. Although AI’s application in ICIs shows promise, significant challenges still demand exploration and resolution. Continued investment in AI research in this context could lead to significant advancements in cancer treatment. Global collaboration is needed to overcome these challenges and fully leverage AI’s potential. This study provides a foundation for future research and interdisciplinary collaboration in this critical field.