Current Trends and Hotspots of Machine Learning in Lung Cancer Research: A Bibliometric Analysis

Abstract

To examine key areas and emerging trends in Machine learning (ML) research within the field of lung cancer. Methods Research literature on ML in the field of lung cancer was gathered from the Science Citation Index Expanded within the Scopus database. This data was then analyzed for insights on publication years, countries/regions, affiliation, funding sponsor, citations, and keywords. Co-occurrence network graphs were created using the VOSviewer library and CiteSpace tools on the online analysis platform. Result A total of 3,341 pertinent research articles from various nations were selected. From 2023 to 2024, there was a notable rise in the quantity of indexed documents, reaching a zenith of roughly 2000 in 2024. Nevertheless, this expansion was not maintained, and in 2025, the total declined precipitously to around 500. Geographically, India and China dominate research output, with India marginally in the lead. The United States occupies the third position but trails considerably behind the top two, with the United Kingdom in fourth place. Other nations, such as Italy, Canada, South Korea, Japan, Saudi Arabia, and Germany, provide relatively fewer documentation. The findings demonstrate significant research effort in Asia, especially in India and China, while North America and Europe also contribute considerably. Disparities in research output can be ascribed to elements such as funding, institutional backing, and national policies that affect publication trends, underscoring the necessity for additional exploration into the political and economic aspects of academic publishing. This research provides a comprehensive analysis of the challenges and prospective applications of machine learning in digital clinical diagnostic systems (DCDS) for the detection of LC. Conclusions The study identified key focus areas and emerging trends in ML for lung cancer diagnosis and indicating that this technology could greatly improve early detection.