The global research landscape of artificial intelligence in precision oncology a comprehensive bibliometric analysis

Artificial Intelligence (AI) technology has made a tremendous contribution to precision oncology through data-driven approaches in diagnosis, prognosis, and customized treatment modalities. However, despite its increasing significance, there is a lack of comprehensive knowledge about the worldwide trends and intellectual structures within this scientific domain. This paper will offer a bibliometric analysis and visualization of Artificial Intelligence (AI) usage in precision oncology. A systematic search was carried out on the Scopus database from 2015 to 2025. Inclusion criteria were employed to screen a total of 8974 articles. The following bibliometric metrics: annual publications, citations, co-authors, co-citations, and keyword associations were studied using VOSviewer version 1.6.20. The results show a considerable exponential increase in publications, rising from 51 in 2015 to 3173 in 2025, along with a notable increase in citations. The most significant sources for their research contributions came from countries like the USA and China, and a number of international collaborations took place as well. Some high-impact journals included Scientific Reports, Cancers, and Frontiers in Immunology. The analysis of keywords showed three major themes emerging: Artificial intelligence methods (machine learning, deep learning), clinical applications (diagnosis, prognosis), and molecular oncology (genomics, tumor microenvironment). Co-citations showed a significant contribution to machine learning and bioinformatics studies. This study demonstrates the rapid expansion and multidisciplinary nature of AI-driven precision oncology research. The integration of computational methods with clinical and molecular data is shaping future cancer care, emphasizing the need for continued innovation and global collaboration. By integrating multiple bibliometric indicators and knowledge visualization techniques, this study offers a more comprehensive and contemporary understanding of the intellectual structure and evolving research landscape of AI-driven precision oncology than previous studies.