Exploring Lung Cancer Research Landscape: Bibliometric Analysis Integrating Artificial Intelligence and Machine Learing

This bibliometric study examines the emerging area of lung cancer research and its relationship with artificial intelligence (AI) and machine learning (ML) contributions. This research aims to comprehensively analyses and understand the changing landscape of AI and ML applications in the field of lung cancer by methodically reviewing a wide range of academic papers. The study examines several aspects including publishing trends, citation patterns, authorship networks, and theme material. It reveals the significant rise and effect of these technologies in transforming lung cancer diagnosis, treatment, and research methodology. This study utilizes R Studio to do a bibliometric analysis on the field of lung cancer research, specifically examining academic papers obtained from bibliographic databases. By using the functionalities of the R programming language, this research systematically investigates the trends in publications, patterns of citations, and thematic material within the domain of lung cancer. This study employs bibliometric methodologies and utilizes R Studio's analytical capabilities to examine the quantitative characteristics of literature on lung cancer. Through this analysis, valuable insights are gained on the development of research subjects, important publications, authorship networks, and patterns of collaboration. This study offers a detailed analysis of the academic landscape in the field of lung cancer research, using the libraries and functions available in R Studio. It employs quantitative measurements and visual representations to clarify the dynamics and trends that are influencing this area of study.