ReviewFrontiers in oncology2024
Artificial intelligence application in the diagnosis and treatment of bladder cancer: advance, challenges, and opportunities.
Review in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
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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
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic value of lymphocyte to monocyte ratio for the patients with bladder cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Deep Learning-Assisted Accuracy Improvement in Bladder Cancer Staging of Spectrum-Aided Visual Enhanced Cystoscopy Images.Biosensors · 2026Article
- A multi-modal approach for decision making in bladder cancer.Nature reviews. Urology · 2026Review
- Artificial intelligence and predictive tools in non-muscle invasive bladder cancer: a narrative review of current insights and advances.Translational andrology and urology · 2026Review
- Review
- Artificial intelligence in urological malignancy diagnosis and prognosis: current status and future prospects.The Canadian journal of urology · 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
- MRI-Based Bladder Cancer Staging via YOLOv11 Segmentation and Deep Learning Classification.Diseases (Basel, Switzerland) · 2026Article
- Emerging Molecular and Computational Biomarkers in Urothelial Carcinoma: Innovations in Diagnosis, Prognosis, and Therapeutic Response Prediction.Journal of personalized medicine · 2026Review
- Multimodal artificial intelligence in urologic precision oncology: from algorithm to translational medicine (a systemized narrative review).Frontiers in oncology · 2026Review
- Artificial intelligence in genitourinary pathology.Histopathology · 2026Review
- Artificial intelligence and machine learning in neurogenic lower urinary tract dysfunction and spinal cord injury: current status, emerging technologies, and ethical perspectives.Frontiers in urology · 2026Article
- Artificial intelligence-assisted diagnosis and histopathological grading of bladder cancer: current status, challenges, and future directions.Frontiers in digital health · 2026Review
- Innovative AI model for bladder cancer diagnosis.Discover oncology · 2025Article
- Artificial intelligence-driven digital pathology in urological cancers: current trends and future directions.Prostate international · 2025Review
- Critical review of patient outcome study in head and neck cancer radiotherapy.Meta-radiology · 2025Article
- Appendiceal neuroendocrine tumors: a case series and literature review.Journal of surgical case reports · 2025Article
- Use of Artificial Intelligence in Vesicoureteral Reflux Disease: A Comparative Study of Guideline Compliance.Journal of clinical medicine · 2025Article
- Article
- Advances in bladder preservation therapy for muscle-invasive bladder cancer.Frontiers in oncology · 2025Review
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
Bladder cancer (BC) is a serious and common malignant tumor of the urinary system. Accurate and convenient diagnosis and treatment of BC is a major challenge for the medical community. Due to the limited medical resources, the existing diagnosis and treatment protocols for BC without the assistance of artificial intelligence (AI) still have certain shortcomings. In recent years, with the development of AI technologies such as deep learning and machine learning, the maturity of AI has made it more and more applied to the medical field, including improving the speed and accuracy of BC diagnosis and providing more powerful treatment options and recommendations related to prognosis. Advances in medical imaging technology and molecular-level research have also contributed to the further development of such AI applications. However, due to differences in the sources of training information and algorithm design issues, there is still room for improvement in terms of accuracy and transparency for the broader use of AI in clinical practice. With the popularization of digitization of clinical information and the proposal of new algorithms, artificial intelligence is expected to learn more effectively and analyze similar cases more accurately and reliably, promoting the development of precision medicine, reducing resource consumption, and speeding up diagnosis and treatment. This review focuses on the application of artificial intelligence in the diagnosis and treatment of BC, points out some of the challenges it faces, and looks forward to its future development.
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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 OpenQuestion graph.