Objective: Chatbot applications powered by large language models (LLMs) have garnered growing interest in healthcare, including urology. Although recent studies suggest potential roles in patient education, decision support, and medical training, no bibliometric analysis has yet evaluated the research landscape within urology. This study aims to comprehensively reveal the current research trends and scientific contributions related to chatbots in urology.

Materials and Methods: A comprehensive bibliometric analysis was conducted using the Web of Science Core Collection (Urology and Nephrology section) to identify original articles on chatbot use in urology published between January 2023 and May 2025. Data were analyzed using the Bibliometrix R package and the Biblioshiny interface. Key metrics included publication trends, citation data, keyword networks, authorship patterns, and international collaboration rates.

Results: A total of 81 original articles met the inclusion criteria. The annual growth rate in publication output was 45.3%, with an average of 10.6 citations per article. Most articles appeared in Science Citation Index Expanded indexed journals. The United States (32.1%) and Türkiye (25.9%) were the most prolific countries. However, international collaboration remained low (23.5%). Urolithiasis, prostate cancer, and urinary incontinence were leading clinical themes. Keyword network analysis identified clusters focused on patient education, decision support, and chatbot performance.

Conclusions: This study offers a foundational understanding of chatbot-related research in urology and highlights the need for enhanced international collaboration, clinical validation, and data integration to fully realize their transformative potential.

Keywords : chatbot, large language models, artificial ıntelligence, urology, bibliometric analysis, ChatGPT