[Large language models as a communication and organizational infrastructure in urology: evidence, limitations, and clinical responsibility].

This article is a narrative review on how large language models (LLMs) are reshaping communication and information flow in urologic care.

Key points:

• Shift from technical feasibility to communication redesign: The focus is no longer whether LLMs can function in medicine, but how they should be positioned within clinical communication structures in urology, which relies heavily on explanation, shared decision-making, and coordination across care settings.

• Performance vs physician communication: Across comparative studies, AI-generated answers to patient questions are often rated by patients as comparable or superior to physician responses, especially for clarity, perceived empathy, and overall satisfaction. However, professional evaluations emphasize different criteria (e.g., nuance, risk framing, medico-legal safety), exposing a gap between patient-centered and professional standards.

• Patient-facing explanations in urology: Urology-specific work on automated lay summaries shows that LLMs can markedly improve readability and formal quality (language level, structure) while maintaining acceptable factual accuracy, supporting their use for discharge letters, educational materials, and visit summaries—provided there is appropriate oversight.

• Documentation and ambient scribing: Early data on AI-assisted documentation and real-time “ambient” scribe systems indicate potential benefits in efficiency and reduced clerical burden. Yet these systems still require active physician review and are tightly coupled to governance questions (data protection, accountability, error management).

• Conceptual reframing: The authors argue LLMs should not be viewed as replacements for medical expertise but as a new mediating layer between:
  • clinical knowledge,
  • organizational and documentation requirements, and
  • patient understanding and expectations.

• Implications for urology as a specialty: Effective use of generative AI will depend less on individual tools and more on institutional strategies: defining roles, oversight processes, training, and ethical frameworks. This “communication infrastructure” perspective frames AI integration as a core professional and organizational task that will shape the future of urologic care delivery.