Mapping global research landscape of artificial intelligence and machine learning in lung cancer diagnosis and prognosis through bibliometric analysis

The use of artificial intelligence (AI) and machine learning (ML) in diagnosing and predicting lung cancer prognosis has changed the way images are interpreted, risks predicted, and decisions made for clinical purposes. Despite increased research in this area, there is little to no bibliometric analysis evaluating the global state of affairs. The purpose of this study was to examine this research area. In this study, a literature search was conducted by using the Scopus database, with emphasis on the role of AI and ML in the diagnosis and prognosis of lung cancer. Collaboration maps were analyzed using VOSviewer 1.6.20 and Biblioshiny. There was a significant growth in global scientific production with an exponential growth in number of publications during 2020–2024 period. The United States, China, and India became the major contributors to scientific production, and analysis of collaboration networks indicated the existence of effective international collaborations. Through citation analysis, the most impactful sources, authors, and institutions were found. Through keyword and thematic evolution analysis, the shift from computer-aided diagnosis and pulmonary nodule detection to radiomics, deep learning, convolutional neural networks, explainable AI, and precision medicine was identified. This paper is an exhaustive bibliometric analysis of the literature on the use of artificial intelligence and machine learning for diagnosing and prognosticating lung cancer. By identifying publication patterns, major contributors, collaboration networks, and research hot spots, this study has provided important insights into the field’s development and the future of research in this area. Bibliometric analysis mapped global AI and ML research trends in lung cancer. Scientific output increased exponentially, with rapid growth after 2021. Identified leading authors, institutions, journals, and influential studies. Highlighted deep learning and radiomics as a future direction for precision oncology.