Generative AI in urology: rethinking patient counselling and shared decision-making – a scoping review from the European Association of Urology Patient Office

Abstract

Background: Shared decision-making (SDM) in urology faces challenges including limited health literacy, language barriers, and time constraints that can compromise informed consent and treatment adherence. Generative artificial intelligence (GAI), particularly large language models, offers opportunities to personalise patient education and enhance SDM. Objective: To evaluate the role of GAI applications in SDM for patients with urological conditions. Eligibility criteria: Peer-reviewed observational studies, validation studies, or mixed-methods studies evaluating GAI (e.g., large language models, AI chatbots) in patient communication, education, counselling, or SDM for urological conditions were included. Editorials, opinion pieces, conference abstracts, and non-English language publications were excluded. Source of evidence: PubMed, Embase, Cochrane Library, and Web of Science databases were comprehensively searched through June 2025. Study assessments: Newcastle-Ottawa Scale, the STROBE or the AGREE II as per study type. Charting methods: Charting methods was performed by using a standardised form. Outcomes of interest included accuracy of GAI-generated information, patient understanding, satisfaction, and decisional conflict. Results: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines, 18 observational studies (2023-2025) were included, comprising 310 patients in real-world settings plus hundreds of simulated queries across diverse urological conditions. GAI demonstrated moderate to high accuracy (52%-95%) for guideline-based information, with optimal performance in disease-specific patient education. A prospective comparative study showed 27% reduction in consultation time and improved patient understanding with ChatGPT-4 assistance. Limitations emerged including poor performance in emergencies and complex oncological counselling, and readability issues with content written at a college level (mean Flesch-Kincaid Grade Level 13.5). Most studies evaluated ChatGPT versions, limiting generalizability. Conclusions: GAI could enhance and potentially transform SDM in urology with appropriate clinical oversight and human-in-the-loop governance. Currently, GAI is useful for consultation preparation and patient education, while maintaining physician expertise for complex scenarios. Future implementation should prioritise patient safety, equitable access, and environmental sustainability while developing speciality-specific models and clinician education programmes.

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