ReviewAmerican journal of clinical and experimental urology2025
Artificial intelligence in prostate cancer: navigating the new frontier of precision uro-oncology.
Review in American journal of clinical and experimental urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Article
- PSMA PET/CT improves precision diagnosis, treatment, and clinical decision-making in prostate cancer.American journal of translational research · 2026Review
- Multimodal artificial intelligence in urologic precision oncology: from algorithm to translational medicine (a systemized narrative review).Frontiers in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) is revolutionizing prostate cancer (PCa) care, addressing the major clinical challenges of subjectivity and overtreatment. Our traditional tools - like PSA, DRE, mpMRI, and Gleason scoring - often lack the precision needed to distinguish truly aggressive tumors from indolent disease, leading to unnecessary morbidity in up to 50% of low-risk men. This review explains how AI, specifically machine learning (ML) and deep learning (DL), is poised to solve this. We cover AI's role from initial diagnosis, where radiomics and digital pathology boost grading accuracy and reduce inter-reader variability, to treatment selection and surgical precision through predictive models and Augmented Reality (AR) guidance. We also detail its utility in predicting biochemical recurrence (BCR) and managing long-term side effects. Finally, we address the critical barriers to adoption, including the need for large, diverse datasets (to combat algorithmic bias), the "black box" problem (solved by Explainable AI, XAI), and navigating FDA regulation. The future of PCa care hinges on this precise, data-driven approach.
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.