Algorithms on the rise: a machine learning–driven survey of prostate cancer literature

Introduction Machine learning (ML) has shown significant potential in improving prostate cancer (PCa) diagnosis, prognosis, and treatment planning. Despite rapid advancements, a comprehensive quantitative synthesis of global research trends and the knowledge structure of ML applications in PCa remains lacking. This study aimed to systematically map the evolution, research hotspots, and collaborative landscape of ML-PCa research. Methods A systematic bibliometric review was performed on English-language articles and reviews published between January 2005 and December 2024. Publications were retrieved from the Web of Science (WOS) and Scopus databases. Analytical tools including CiteSpace, VOSviewer, and the R-bibliometrix package were employed to assess publication growth trends, country and institutional contributions, collaboration networks, author productivity, journal outlets, and keyword co-occurrence patterns. Results A total of 2,632 publications were identified. Annual output increased from fewer than 20 papers during 2005–2014 to 661 in 2024, with 82% of all studies published since 2021. Emerging frontiers included deep learning, radiomics, and multimodal data fusion. China (649 publications) and the United States (492 publications) led in research volume, while Germany demonstrated the highest proportion of multinational collaboration (39.29%). Leading institutions by output were the Chinese Academy of Sciences, the University of British Columbia, and Shanghai Jiao Tong University. In terms of citation impact, the University of Toronto, Case Western Reserve University, and the University of Pennsylvania ranked highest. The journals Cancers, Frontiers in Oncology, and Scientific Reports published the most ML-PCa studies, highlighting the cross-disciplinary nature of the field. Madabhushi Anant emerged as the most central author hub in global collaboration networks. Discussion ML applications in PCa research have experienced exponential growth, with methodological innovations driving interest in deep learning and radiomics. However, a persistent translational gap exists between algorithmic development and clinical implementation. Future directions should focus on fostering interdisciplinary collaboration, conducting prospective multicenter validation studies, and aligning with regulatory standards to accelerate the integration of ML models into clinical PCa workflows.