ReviewWorld journal of urology2026
From data to decision: integrating causality AI and predictive analytics in endourological practice-a descriptive guide for clinicians from EAU Endourology.
Review in World journal of urology, 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
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe proposed review aims to provide a guide on current developments of artificial intelligence in Endourological practice, with an insight and descriptive guide on the potential integration of advanced technologies into Endourology. The purpose of this review article is also to gather the recent advances in artificial intelligence applications in urology, and to highlight the potential applications of novel trends and technologies being developed in artificial intelligence.
methodsArtificial intelligence is conquering the scientific landscape, and the medical field is not an exception. The work starts with a concise review of the state of the art and recent development of artificial intelligence and machine learning in urology and endourology. Moreover, an advanced description of novel technologies is presented in a clear manner, easy to follow by clinicians. The novel technologies include the causal artificial intelligence modeling, based on scientific constraints and directed acyclic graphs, as well as solving inverse problems and optimal decision making through reinforcement learning.
resultsThe proposed manuscripts showcase potential applications of novel technologies in artificial intelligence, leading to democratizing its adoption. Theses novel technologies ease the explanation of the predictions performed by artificial intelligence algorithms, and follow causality and time sequencing constraints. Moreover, they can be useful to integrate expert's partial knowledge of complex medical phenomenon into their architecture by construction.
conclusionsThe guide also showcases the potential applications and limitation in the field of urology. The proposed work ends in the current challenges hindering the democratization of artificial intelligence in Endourology.
Indexed as
Identifiers
41764677What 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.