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. 2025 Aug;44(6):1231-1237.
doi: 10.1002/nau.70075. Epub 2025 Jun 15.

Envisioning the Future of AI in Urodynamics: Exploratory Interviews With Experts

Catharina Margaretha van Leersum  1 , Andrew Gammie  2 , Enrico Finazzi Agrò  3   4 , Kevin Rademakers  5
Affiliations

Envisioning the Future of AI in Urodynamics: Exploratory Interviews With Experts

Catharina Margaretha van Leersum et al. Neurourol Urodyn. 2025 Aug.

Abstract

Background: Urodynamics monitors various parameters while the urinary bladder fills and empties, to diagnose functional or anatomic disorders of the lower urinary tract. It is an invasive and complex test with technical challenges, and it needs rigorous quality assessment and training of clinicians to avoid misdiagnosis. Applying AI to urodynamic pattern recognition and noisy data signals seems promising.

Aim: To understand desirable and appropriate applications, this study aims to explore the envisioned future of AI in urodynamics according to experts in AI and/or urodynamics.

Method: Ten semi-structured interviews were conducted to explore expectations, trust and possibilities of AI in urodynamics. Content analysis with an inductive approach was performed on all data.

Results: The analysis resulted in seven overarching themes: difficulties with urodynamics, quality of urodynamics, AI will be supportive, development and training of AI systems, desirable outcomes, challenges, and envisioning the future of AI in urodynamics.

Discussion and conclusion: In the vision of experts, urodynamics practices will change with the introduction of AI. In the beginning, clinicians will probably desire to check AI-made outcomes of UDS tests to gain trust the system. After experiencing the value of these systems, clinicians might let the system independently provide suggested UDS analyses, and they will use more of their time to spend on their patients.

Keywords: Artificial Intelligence (AI); Decision support; acceptance; future; urodynamics.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Coding matrix containing all main themes and sub‐themes after data analysis on AI in UDS.
Figure 2
Figure 2
The envisioned future of AI in UDS according to experts with the most important aspects needed to take into account before implementation.
See this image and copyright information in PMC

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