ReviewTherapeutic advances in urology
A review of artificial intelligence in prostate cancer detection on imaging.
Review in Therapeutic advances in urology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06360523 (ProstateAI - A Prospective Paired Validating Cohort Study to Evaluate an MRI-based Optimized Prostate Cancer Diagnostic Pathway Powered by Artificial Intelligence), which is not on this map. Cited by 45 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.
ProstateAI - A Prospective Paired Validating Cohort Study to Evaluate an MRI-based Optimized Prostate Cancer Diagnostic Pathway Powered by Artificial Intelligence
Who cites it
45 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence-Enabled Imaging for Predicting Preoperative Extraprostatic Extension in Prostate Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Exploring the role of artificial intelligence in chemotherapy development, cancer diagnosis, and treatment: present achievements and future outlook.Frontiers in oncology · 2025Pooled it
- Clinical Feasibility of 3-D Acoustic Radiation Force Impulse (ARFI) Imaging for Targeted Prostate Biopsy Guidance.Ultrasonic imaging · 2025Trial
- Present status of prostate cancer diagnosis, limitations, challenges, and future endeavors.Annals of medicine · 2026Review
- Deep learning for automatic detection of prostatectomy in CT images of prostate cancer patients.Physical and engineering sciences in medicine · 2026Article
- Explainable artificial intelligence reveals key surgical parameters in robot-assisted and open radical prostatectomy.Scientific reports · 2026Article
- Comprehensive biochemical insights into derivatives of PSA and glycosylation-specific changes in PSA: role in diagnosis and management of prostate cancer.Clinical proteomics · 2026Review
- Reasoning in machine vision by learning fast and slow thinking.Nature communications · 2026Article
- Enhancing foundation model transfer for prostate cancer detection with patch-level contrastive learning.NPJ digital medicine · 2026Article
- Development and Validation of a Multimodal AI-Based Model for Predicting Post-Prostatectomy Treatment Outcomes from Baseline Biparametric Prostate MRI.medRxiv : the preprint server for health sciences · 2026Article
- Artificial intelligence in urological malignancy diagnosis and prognosis: current status and future prospects.The Canadian journal of urology · 2026Review
- Engineering Multimodal Nanomaterials for Prostate Cancer Theranostics: Design Principles, Recent Advances, and Translational Challenges.International journal of nanomedicine · 2026Review
- Clinical and MRI features for predicting maximum cancer involvement of >50% in a single biopsy core among patients diagnosed with prostate cancer at initial systematic biopsy: a machine learning study.American journal of cancer research · 2026Article
- Decade-long landscape of transrectal ultrasound (TRUS) in prostate cancer research: trends, collaborations, and emerging frontiers.Journal of ultrasound · 2025Article
- Next-Generation Advances in Prostate Cancer Imaging and Artificial Intelligence Applications.Journal of imaging · 2025Review
- Artificial Intelligence Across the Prostate Cancer Pathway: Screening, Imaging, Pathology, and Biomarkers.Cureus · 2025Review
- Review
- Socioeconomic impact of artificial intelligence-driven point-of-care testing devices for liquid biopsy in the OncoCheck system.Cancer metastasis reviews · 2025Review
- Micro-Ultrasound in the Detection of Clinically Significant Prostate Cancer: A Comprehensive Review and Comparison with Multiparametric MRI.Tomography (Ann Arbor, Mich.) · 2025Review
- Diagnostic performance of a fully automated AI algorithm for lesion detection and PI-RADS classification in patients with suspected prostate cancer.La Radiologia medica · 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
13 authors.
Funding
Abstract
A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care.
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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.