Generative artificial intelligence in the daily urology unit: A practical,patient-centered framework

Abstract

Objective:

To synthesize current evidence on generative artificial intelligence (AI) integration in urology and propose a structured, patient-centered framework for its responsible implementation in daily urological workflows.

Methods:

A narrative review was conducted examining applications of large language models (LLMs), ambient documentation platforms, retrieval-augmented generation (RAG) systems, and multimodal clinical decision-support tools across the urology care continuum, including pre-visit triage, patient education, clinical decision support, surgical planning, and academic productivity.

Results:

A four-agent model was developed defining complementary roles for the digital AI agent, staff urologist, resident or fellow, and institutional AI champion. RAG systems reduced hallucinations by anchoring outputs to guideline-based sources. AI-assisted documentation decreased administrative burden and clinician burnout, while validated predictive models improved risk stratification and reduced unnecessary interventions. Safe deployment requires continuous human oversight, systematic bias auditing, transparent patient opt-out mechanisms, and ongoing guideline-aligned validation.

Conclusion:

The proposed framework demonstrates that generative AI can enhance efficiency and clinical quality across urological care when governed responsibly. Generative AI should function as a supervised clinical co-pilot rather than an autonomous decision-maker. A governance-first, patient-centered approach is essential to preserve safety, equity, and scientific integrity in modern urology.