ReviewDiagnostics (Basel, Switzerland)2023
Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement.
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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
55 citing papers in PubMed.
- Multimodal large language models for bladder tumor detection in cystoscopy: a retrospective benchmarking study.World journal of urology · 2026Article
- Computational pathology model to predict recurrence-free survival in NMPUC patients on BCG-therapy.NPJ precision oncology · 2026Article
- Article
- A Novel Radiomics-based Interpretable Model for Bladder Cancer Grade Prediction Using White-Light Cystoscopy Images.European urology open science · 2026Article
- Review
- The role of artificial intelligence in advancing urologic care: From diagnostics to therapeutics.Surgery in practice and science · 2026Review
- Use of Artificial Intelligence Model Associated with Masson's Trichrome Staining as a Predictor of Muscle Invasion in Bladder Cancer.International journal of molecular sciences · 2026Article
- Exploring advancements in the management of penile cancer in the era of artificial intelligence and machine learning: a narrative review.Annals of medicine and surgery (2012) · 2026Article
- MRI-Based Bladder Cancer Staging via YOLOv11 Segmentation and Deep Learning Classification.Diseases (Basel, Switzerland) · 2026Article
- An explainable machine learning model for predicting bladder tumor aecurrence risk.Frontiers in oncology · 2026Article
- Epigenomics-Guided Multi-Omics Integration Uncovers a Lipid-Metabolic Signature with Translational Utility in Bladder Cancer.Computational and structural biotechnology journal · 2026Article
- AI-BLADE toolbox: AI-powered BLADdEr multiparametric MRI analysis for clinical application.BJR artificial intelligence · 2026Article
- Machine Learning Integration Framework Constructs a Lactylation-Associated Gene Signature to Improve Prognosis in Bladder Cancer.Cancer medicine · 2026Article
- Explainable machine learning predicts overall survival in female bladder cancer patients after radical cystectomy.Discover oncology · 2025Article
- Explainable Computational Imaging for Precision Oncology: An Interpretable Deep Learning Framework for Bladder Cancer Histopathology Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
- Explainable and likelihood aware AI framework for MRI-based pixel-level bladder tumour prediction.Scientific reports · 2025Article
- Urinary Biomarkers in Bladder Cancer: FDA-Approved Tests and Emerging Tools for Diagnosis and Surveillance.Cancers · 2025Review
- Liquid Biopsy: Current advancements in clinical practice for bladder cancer.The journal of liquid biopsy · 2025Review
- Blood and urine-based biomarkers in prostate cancer: Current advances, clinical applications, and future directions.The journal of liquid biopsy · 2025Review
- SGNDV-001: disitamab vedotin with pembrolizumab in HER2-expressing locally advanced or metastatic urothelial carcinoma.Future oncology (London, England) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
21 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence is highly regarded as the most promising future technology that will have a great impact on healthcare across all specialties. Its subsets, machine learning, deep learning, and artificial neural networks, are able to automatically learn from massive amounts of data and can improve the prediction algorithms to enhance their performance. This area is still under development, but the latest evidence shows great potential in the diagnosis, prognosis, and treatment of urological diseases, including bladder cancer, which are currently using old prediction tools and historical nomograms. This review focuses on highly significant and comprehensive literature evidence of artificial intelligence in the management of bladder cancer and investigates the near introduction in clinical practice.
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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.