ArticleUrologie (Heidelberg, Germany)2026
[Artificial intelligence in daily urological care: results of the AI barometer].
Article in Urologie (Heidelberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- [Use of artificial intelligence in clinical practice and hospitals].Urologie (Heidelberg, Germany) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUrological care in Germany is undergoing transformation, while novel technologies with artificial intelligence (AI) have the potential to automate processes and support caregivers. The aim was therefore to systematically describe workload, current AI use, as well as trust and expectations within the context of the current healthcare landscape.
methodsNationwide anonymous cross-sectional survey conducted from January to April 2026 among professionals in urological care. Analysis was exploratory and descriptive.
resultsA total of 433 participants responded, predominantly physicians. Increased patient volume was reported by 79%, and 69% stated that available time was insufficient for high-quality care. Administrative and documentation-related burden was identified as the main driver of time pressure by 93,1%. General-purpose AI tools were used substantially more often than clinic-specific applications. While 74,6% reported both private and/or professional AI use, certified medical AI applications were rarely used. Current use focused on information- and text-based tasks such as information retrieval, translation, and text drafting. At the same time, substantial unmet demand was observed, particularly for coding, documentation, imaging analysis, and medication management. Key prerequisites included verifiable output quality (82,4%), evidence-based data sources (72,1%), clear regulatory frameworks (70,0%), and integration into information infrastructures (63,7%). In all, 75,1% expected future time savings through AI.
conclusionUrological care is perceived by respondents as increasingly intensified. AI is already widely used, but currently mainly in the form of general-purpose applications. The findings also indicate an unmet need for AI applications for selected small-scale routine tasks, particularly if requirements for quality, transparency, and governance are met.
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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.