What Is Required for AI to Improve the Assessment and Treatment of Patients With Lower Urinary Tract Dysfunction? ICI-RS 2025
Neurourology and Urodynamics, vol.45, no.5, pp.922-928, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 45 Issue: 5
- Publication Date: 2026
- Doi Number: 10.1002/nau.70186
- Journal Name: Neurourology and Urodynamics
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, EMBASE, MEDLINE, Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest)
- Page Numbers: pp.922-928
- Keywords: artificial intelligence (AI), bladder outlet obstruction (BOO), EU AI Act, lower urinary tract dysfunction (LUTD), machine learning, overactive bladder (OAB), Software as a Medical Device (SaMD)
- Hacettepe University Affiliated: Yes
Abstract
Introduction: Artificial intelligence (AI) is poised to improve the diagnosis and management of lower urinary tract dysfunction (LUTD). Its effective deployment requires prioritization, regulatory oversight, rigorous validation, and clinician and patient engagement. Methods: The Think Tank at the International Consultation on Incontinence—Research Society (ICI-RS) 2025 evaluated key considerations for successful AI implementation into LUTD clinical care. The topics included clinical triage framework, regulatory and legal principles, levels of evidence required for validation, and clinician and patient engagement to guide development. The group developed a narrative of the pressing matters related to AI implementation and a list of proposed research questions, which, when addressed, will help shape the future of the field. Results: LUTD topics that should be prioritized for AI implementation include high-burden conditions with high unmet need such as neurogenic LUTD, bladder outlet obstruction, and overactive bladder. Regulatory frameworks such as the EU AI Act and the U.S. “Software as a Medical Device” and its associated guidance promote safety, transparency, and accountability. AI solutions should be as rigorously evaluated as other clinical devices or drug agents. Patient and clinician engagement are paramount to ensure innovation aligns with the pressing needs of patients and clinicians. Conclusions: AI's integration into LUTD care requires cross-disciplinary collaboration, prospective validation, and legal and ethical frameworks. AI must be developed and implemented with a strong focus on transparency, trust, and patient-centered care. Clinical Trial Registration: This study is not a clinical trial and thus does not warrant registration as such.