Artificial Intelligence and Multiomics in Urology: A Bibliometric Analysis of Male Reproductive Outcomes
Rano Amirkhanovna Zhankina, Aigul Saparbekovna Botabayeva, Nader Tanideh, Ulanbek Zhanbyrbekuly
A
BSTRACT
Background:
Research at the intersection of reproductive biology, omics technologies, and computational approaches has expanded rapidly, reflecting growing interest in precision medicine and data-driven biomedical research.
Aims and Objectives:
This study aimed to conduct a comprehensive bibliometric analysis to identify publication trends, influential contributors, collaboration patterns, and thematic evolution in this interdisciplinary field.
Materials and Methods:
Data were retrieved from the Web of Science Core Collection and analyzed using bibliometric tools and visualization techniques. Scientific production, leading countries, journals, authors, institutions, collaboration networks, keyword patterns, thematic maps, and research trends were evaluated.
Results:
Publication output increased markedly in recent years, with China and the United States leading in productivity and international collaboration. Key journals, authors, and institutions showed strong contributions to the field. Keyword and thematic analyses revealed a shift from traditional biological topics, including fertility and seminal plasma, toward integrative research involving genomics, proteomics, biomarker discovery, and computational methods. Proteomics was identified as a motor theme, while machine learning emerged as a highly relevant but still developing research area. Trend analysis also highlighted increasing attention to clinically oriented topics such as prostate cancer and male infertility.
Conclusion:
The findings demonstrate a clear transition toward interdisciplinary, translational, and data-driven research in reproductive biology. Integration of omics technologies, machine learning, and experimental biomedical approaches is likely to shape future advances in precision medicine and reproductive health.