AI-Assisted Comparative Analysis of Pediatric Urology Guidelines (EAU-AUA-NICE): A Cross-Guideline NLP-Based Study

Background: Pediatric urology guidelines by EAU, AUA, and NICE differ in terminology and recommendations, leading to inconsistencies in clinical practice. Harmonizing these standards can enhance decision-making in global pediatric care. Objective: This study aimed to compare major pediatric urology guidelines using artificial intelligence (AI) and natural language processing (NLP) to identify areas of concordance and divergence. Methods: Full-text guidelines from EAU, AUA, and NICE (2023–2025) were analyzed across four domains: vesicoureteral reflux (VUR), enuresis, hydronephrosis, and imaging. NLP tools (BERT, GPT, SciSpacy) were applied to extract and classify recommendations. Semantic similarity metrics (Jaccard and cosine similarity) and expert panel validation were used to assess alignment. Results: High concordance was observed between EAU and AUA guidelines (Jaccard 0.82; cosine 0.91), while NICE diverged moderately. VUR recommendations showed the highest agreement (9 of 12 statements), whereas hydronephrosis had greater variation. Enuresis terminology varied, especially between AUA and NICE. Expert validation confirmed AI-derived findings in 90% of sampled statements. Visualizations clearly illustrated alignment and conflict areas. Conclusion: This study demonstrates the utility of AI in systematically comparing pediatric urology guidelines. The approach may support future harmonization efforts and inform clinical decision support tools by highlighting both consensus and variation across international recommendations.