SynthesisCurrent urology reports2021
The Ascent of Artificial Intelligence in Endourology: a Systematic Review Over the Last 2 Decades.
Synthesis in Current urology reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Can AI and predictive models accurately predict stone-free status? a systematic review and meta-analysis.The Canadian journal of urology · 2026Pooled it
- Use of artificial intelligence for sepsis risk prediction after flexible ureteroscopy: a systematic review.Revista do Colegio Brasileiro de Cirurgioes · 2023Pooled it
- Review
- Agreement Between Visual Assessment of Urinary Stones Morphology During Endo-Urological Procedure and Fourier Transform Infrared Spectroscopy-based Composition Analysis: An Observational Study.JNMA; journal of the Nepal Medical Association · 2025Observational
- Role of suction in revolutionising endourology: Is it the final frontier - an overview from EAU Endourology.Central European journal of urology · 2025Article
- A Novel Machine-Learning Algorithm to Predict Stone Recurrence with 24-Hour Urine Data.Journal of endourology · 2024Article
- Artificial Intelligence: Ready To Pass the European Board Examinations in Urology?European urology open science · 2024Article
- The impact of artificial intelligence in revolutionizing all aspects of urological care: a glimpse in the future.Central European journal of urology · 2024Article
- Use of Artificial Intelligence for Analyzing Kidney Stone Composition: Are We There Yet?Mayo Clinic proceedings. Digital health · 2023Article
- On the rocks: can urologists identify stone composition based on endoscopic images alone? A worldwide survey of urologists.World journal of urology · 2023Article
- Theranostic roles of machine learning in clinical management of kidney stone disease.Computational and structural biotechnology journal · 2023Review
- Innovations in Kidney Stone Removal.Research and reports in urology · 2023Review
- Reporting quality of abstracts of systematic reviews/meta-analyses: An appraisal of Arab Journal of Urology across 12 years: the PRISMA-Abstracts checklist.Arab journal of urology · 2023Article
- Current Applications of Artificial Intelligence in Benign Prostatic Hyperplasia.Turkish journal of urology · 2022Article
- Review
- The role of 'artificial intelligence, machine learning, virtual reality, and radiomics' in PCNL: a review of publication trends over the last 30 years.Therapeutic advances in urologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewTo highlight and review the application of artificial intelligence (AI) in kidney stone disease (KSD) for diagnostics, predicting procedural outcomes, stone passage, and recurrence rates. The systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist. RECENT
findingsThis review discusses the newer advancements in AI-driven management strategies, which holds great promise to provide an essential step for personalized patient care and improved decision making. AI has been used in all areas of KSD including diagnosis, for predicting treatment suitability and success, basic science, quality of life (QOL), and recurrence of stone disease. However, it is still a research-based tool and is not used universally in clinical practice. This could be due to a lack of data infrastructure needed to train the algorithms, wider applicability in all groups of patients, complexity of its use and cost involved with it. The constantly evolving literature and future research should focus more on QOL and the cost of KSD treatment and develop evidence-based AI algorithms that can be used universally, to guide urologists in the management of stone disease.
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.