Medical Journal of The Islamic Republic of Iran (MJIRI)
Iran University of Medical Sciences
Tue, Jan 6, 2026
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Volume 39, Issue 1 (1-2025)
Med J Islam Repub Iran 2025 |
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Hatampour K, Baghi Keshtan S, Mohammadi G, Akbarniakhaky H, Faryabi A, Aazami H, et al . Global Research Landscape of Artificial Intelligence in Urology: A Systematic Analysis of Emerging Trends, Clinical Impact, and Collaborative Networks (1971–2024). Med J Islam Repub Iran 2025; 39 (1) :1378-1396
URL: http://mjiri.iums.ac.ir/article-1-9681-en.html
URL: http://mjiri.iums.ac.ir/article-1-9681-en.html
Kasra Hatampour 
, Sina Baghi Keshtan , Ghazal Mohammadi , Hassan Akbarniakhaky , Ali Faryabi , Hossein Aazami , Mohammad Reza Fattahi , Farhad Seif , Hojat Dehghanbanadaki
, Sina Baghi Keshtan , Ghazal Mohammadi , Hassan Akbarniakhaky , Ali Faryabi , Hossein Aazami , Mohammad Reza Fattahi , Farhad Seif , Hojat Dehghanbanadaki
Department of General Surgery, Loghman Medical Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran , drkasrahatampour@gmail.com
Abstract: (25 Views)
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.
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.
Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Urology, Prostate Cancer, Bladder Cancer, Trend Analysis, Bibliometric, Scientometric
Type of Study: Systematic Review |
Subject:
Urology and Nephrology
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