ReviewUrologie (Heidelberg, Germany)2026
[Large language models as a communication and organizational infrastructure in urology: evidence, limitations, and clinical responsibility].
Review in Urologie (Heidelberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) mark a structural turning point in digital medicine, as they are now capable not only of processing medical information but of conveying it in ways that are context-sensitive, linguistically coherent, and tailored to the needs of specific audiences. In urology, a specialty defined by high communication demands, explanation-intensive disease entities, and complex organizational interfaces, the central question is no longer one of technical feasibility but of the fundamental repositioning of clinical communication. This article analyzes the role of LLMs as a communication and organizational infrastructure within urologic care, grounded in current empirical evidence. Systematic comparisons demonstrate that artificial intelligence (AI)-generated responses to patient questions are perceived in most studied contexts as equivalent or superior to physician responses, particularly with respect to clarity, empathy, and overall satisfaction, while revealing a pronounced divergence between patient-centered and professional evaluative standards. Urology-specific data on the automated generation of layperson summaries further show that LLMs can achieve significantly improved readability and formal quality without compromising factual accuracy. At the same time, studies on AI-assisted documentation and ambient scribe systems underscore that gains in efficiency and reductions in documentation burden remain inseparable from the need for physician oversight and institutional governance. These findings are not interpreted as a substitution of medical expertise but as the emergence of a new mediating layer between clinical knowledge, organizational requirements, and patient perception. The integration of generative AI, thus, becomes a professional and institutional challenge that extends beyond the deployment of individual tools and shapes the future communication architecture of urology.
Indexed as
Identifiers
41946949What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.