Global Research Landscape of Artificial Intelligence in Urology: A Systematic Analysis of Emerging Trends, Clinical Impact, and Collaborative Networks (1971–2024) Publisher



Hatampour K ; Keshtan S B ; Mohammadi G ; Akbarniakhaky H ; Faryabi A ; Aazami H ; Fattahi M R ; Seif F ; Dehghanbanadaki H
Source: Medical Journal of the Islamic Republic of Iran Published:2025

Abstract

Background: Despite the rapid integration of artificial intelligence (AI) in urological practice, a comprehensive understanding of research evolution and impact patterns remains unexplored. This analysis provides a systematic examination of its scientific development and future potential. Methods: We conducted a comprehensive analysis of AI-related urological publications through October 2024 using the Scopus database. The study incorporated English-language original articles and reviews, utilizing VOSviewer, GraphPad Prism, and Data Wrapper for analysis and visualization. Results: Our investigation encompassed 5755 publications, comprising 5109 original articles and 646 reviews, with 63.9% being open access. The field demonstrated exponential growth from a single publication in 1971 to 1337 publications in 2024, garnering 112,583 citations. The past decade has witnessed the emergence of the most influential articles, particularly those focusing on deep learning (DL) applications in urological cancer detection. The USA-led global contributions (31.1%), followed by China (23.7%) and India (8.2%). Scientific Reports emerged as the leading journal with 171 publications. Titles and abstracts analysis revealed key focuses on DL in imaging (n = 1067), chronic kidney disease (n = 801), and advanced DL methodologies (n = 794). The keyword analysis identified machine learning as the dominant theme (1331 occurrences), with prostate cancer (955) and deep learning (838) following closely. Contemporary trends show significant shifts toward ChatGPT applications, pharmacovigilance, and AI-assisted surgical planning. In terms of international collaboration, the USA demonstrated the strongest network with a link strength of 1543. Conclusion: This study traces AI's evolution in urology, from basic ML to advanced clinical tools, with particular advancement in radiomics, imaging, and biomarker analysis. Successful future implementation necessitates addressing ethical considerations, technical hurdles, and practical challenges while maintaining focus on patient safety and equitable healthcare access. Copyright © Iran University of Medical Sciences. This work has been published under CC BY-NC-SA 4.0 license.