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Published on: July 11, 2025
Awareness, use, and perceived barriers to artificial intelligence in pediatric urology: a multicenter survey
Kursat Kucuker1, Aykut Akinci2, Mesut Berkan Duran2
1Urology Department, Pamukkale University, Third Floor, Denizli 20070, Turkey.
Background:
Artificial intelligence (AI) and large language models (LLMs) are increasingly integrated into healthcare, yet their adoption in pediatric urology remains insufficiently explored. Pediatric urology, with its complex and rare conditions, may particularly benefit from AI-based innovations.
Objectives:
This study aimed to assess pediatric urologists' awareness, usage patterns, and perceptions regarding AI and LLMs, while also identifying potential applications, barriers, and educational needs.
Design:
A cross-sectional, descriptive survey was conducted among pediatric urologists.
Methods:
Between May and July 2025, a 21-item questionnaire was distributed via professional networks and mailing lists. Items addressed demographics, knowledge of AI, frequency and purpose of AI and LLM use, perceived clinical and surgical applications, ethical concerns, and interest in AI training. Descriptive statistics were used for analysis.
Results:
Of 368 invited pediatric urologists, 103 (28%) responded. Most reported moderate (35.0%) or low (29.1%) knowledge of AI, yet more than half (51.5%) used AI tools daily. LLMs had been used by 96.1% of participants, mainly for scientific writing (78.8%), language editing (54.5%), and text summarization (46.5%). Surgical simulation (46.6%), imaging-based strategy planning (40.8%), and preoperative planning (36.9%) were identified as promising clinical applications. Barriers included lack of trust (52.4%), ethical concerns (43.7%), and insufficient knowledge (35.0%). A strong interest in structured AI training was expressed by 81% of participants. Although responses were obtained from multiple countries, the majority of participants were based in Turkey, and the findings should be interpreted accordingly.
Conclusion:
Pediatric urologists demonstrate substantial engagement with AI in academic work, while clinical integration is still limited. The findings highlight a strong demand for AI education and emphasize the need for regulatory clarity, ethical frameworks, and validated tools to enable safe and effective use of AI in pediatric urology.
