Development and Validation of Urodynamic Report Writer: A Chat GPT App

ABSTRACT Objective Accurate interpretation and reporting of urodynamic studies are essential for diagnosis and management of lower urinary tract dysfunction, yet remain time‐consuming and operator‐dependent. To develop and validate the Urodynamic Report Writer App (URapp), a GPT‐4o‐based virtual assistant that supports interpretation of clinician‐supplied urodynamic data and draft report generation. Methods Multiphase development and validation study. URapp was modeled using scientific literature and international urodynamic guidelines. Training used urodynamic examinations from multiple referral centers, with supervised fine‐tuning by two expert urodynamicists. Validation was performed by functional urology specialists using a Likert scale (0–5) assessing clinical relevance, interpretative accuracy, diagnostic usefulness, clarity of urodynamic concept explanations, practical recommendations, and adherence to evidence‐based standards. In the final phase, URapp generated reports for a consecutive sample of 100 urodynamic studies, independently evaluated by two expert urologists. Results Development required approximately 6 months. Validation scores were high across domains: interpretative accuracy (4.5 ± 0.2), explanation of urodynamic concepts (5.0 ± 0), adherence to evidence‐based standards (4.8 ± 0.3), clarity and practicality of recommendations (4.2 ± 0.3), clinical relevance (3.8 ± 0.3), and diagnostic usefulness (3.5 ± 0.5). All 100 consecutive cases (median age 61 yr) received overall scores ≥4. Limitations include retrospective design, limited sample size, and absence of prospective clinical outcome assessment. Conclusions URapp may support standardized, guideline‐adherent urodynamic reporting in clinical practice. Prospective multicenter studies are required before routine clinical implementation.