SynthesisBMC medical education2026
Artificial intelligence in urological education: a systematic review.
Synthesis in BMC medical education, 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
5 authors.
Funding
Abstract
backgroundArtificial intelligence (AI) is currently widely applied across various fields, especially in the field of education where it holds significant implications. This systematic review aims to explore the specific role of AI in urological education.
methodsFollowing the PRISMA 2020 guidelines, we searched the PubMed, Scopus, and Web of Science databases for articles related to AI in urological education published up to February 10, 2026. The quality of included studies was assessed using the Medical Education Research Study Quality Instrument (MERSQI), and educational outcomes were classified based on the modified Kirkpatrick framework.
resultsA total of 15 eligible study records were finally included. The use of AI in urology education can be categorized into two groups: interventions that are used directly in the educational process, and enabling technologies that provide support for education. It was showed that combining AI in urology education had a positive impact on both learners and teachers. For learners such as medical students, urology residents, or interns, the AI-based hybrid teaching model increased learning efficiency and improved students' theoretical exam scores. Meanwhile, the combination of high-fidelity virtual reality simulation training and intelligent AI feedback showed potential in facilitating the acquisition of clinical skills, in which simulation scores were positively correlated with surgical potential. On the other hand, as the supporting or enabling technology, AI assisted teachers in designing teaching materials and developing assessment programs. In terms of evaluating learners' performance, AI provided fair and objective evaluation results. In addition, machine learning and computer vision algorithms incorporating AI could automatically recognize surgical steps with high precision.
conclusionThe application of AI possesses the capacity to drive transformative changes in urological education. AI can serve as an effective tool to improve students' learning efficiency, theoretical knowledge level and clinical skills through assisted teaching. Moreover, as the enabling technology, the application of AI may be potential in providing support during the teaching process. But AI should be utilized in a responsible and ethical manner.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.