ReviewCurrent opinion in urology2026
Modern integrative prostate cancer diagnostics.
Review in Current opinion in urology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.Histochemistry and cell biology · 2026Review
- From AI-based image analysis to surgical decision support in prostate cancer: interdisciplinary application of the international radiomics platform.Frontiers in oncology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewTo review contemporary applications, performance, and implementation challenges of artificial intelligence (AI) in the radiological and pathological diagnosis of prostate cancer, and to highlight emerging multimodal AI biomarkers for prognosis and treatment selection. RECENT
findingsIn radiology, large multicenter studies demonstrate that MRI-based AI can detect clinically significant prostate cancer with accuracy comparable to, and in some contexts surpassing, expert radiologists, while reducing inter-reader variability and improving workflow efficiency. In surgical pathology, AI systems show high concordance with pathologists in cancer detection and Gleason grading, helping standardize challenging features such as Gleason pattern 4 and supporting triage or second-reader workflows. However, emerging transformative potential lies in multimodal AI systems that integrate digital histopathology with clinical and molecular data to deliver prognostic and predictive biomarkers. These tools are now being validated in randomized trials and real-world cohorts and are beginning to be recognized in clinical guidelines. SUMMARY: AI is a powerful assistive technology that can enhance diagnostic accuracy, reproducibility, and efficiency across MRI and pathology. The integration of multimodal data is catalyzing validated biomarkers to guide risk stratification and treatment decisions - the next frontier in personalized prostate cancer care. But broad adoption still requires rigorous external validation, quality assurance, and ongoing postdeployment monitoring.
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.