Hotspots and Research Trends in Machine Learning for Prostate Cancer: A Bibliometric Analysis and Visualization (1997-2025)

Abstract

Objective: This bibliometric analysis examines the evolution of prostate cancer (PCa) research and evaluates the impact of machine learning and artificial intelligence (AI) on its diagnosis, classification, and treatment.Materials and Methods: Articles published between 1997 and 2025 were analysed using the Web of Science Core Collection database.VOSviewer and Bibliometrix software was utilized for bibliometric analysis.Terms such as "PCa", "machine learning (ML)", "deep learning" and "AI" were included in the search strategy.The number of publications, the most cited studies, author collaborations and country collaborations, thematic trends, and citation networks were visualised.Results: A total of 3,277 articles were analysed.The in augural article was published in 1997.Over the past five years, there has been a significant increase in the number of articles published.The United States and China are the countries with the highest number of publications, and the most influential authors and institutions are concentrated in these countries.A marked upward trend has been observed in ML applications for PCa diagnosis, risk stratification, and treatment planning. Conclusion:The use of AI and ML in PCa research has grown significantly over the last 20 years.However, most of the existing models have been tested with retrospective data, and more multicenter and prospective studies are needed for clinical applications.Comprehensive clinical validation is essential before AI-based systems can be reliably implemented.

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