ReviewResearch and reports in urology2026
Artificial Intelligence as a Diagnostic Tool for Benign Prostatic Hyperplasia (BPH): A Narrative Review.
Review in Research and reports in urology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Benign prostatic hyperplasia (BPH) is a highly prevalent condition among aging men and represents a significant clinical and economic burden. Current diagnostic approaches, including prostate-specific antigen (PSA) testing and imaging modalities, remain limited by low specificity, false-positive findings, and variability in interpretation, particularly in differentiating BPH from prostate cancer. Objective: This narrative review aims to evaluate the role of artificial intelligence (AI) as a diagnostic tool for BPH, focusing on its performance, clinical applications, and potential to address current diagnostic limitations. Methods: A narrative review was conducted using PubMed, Scopus, and ScienceDirect. The search strategy included studies published between 2021 and 2025 using keywords such as ("benign prostatic hyperplasia" OR "BPH") AND ("artificial intelligence" OR "machine learning" OR "deep learning"). A total of 10 studies meeting predefined inclusion criteria were analyzed, focusing on diagnostic applications of AI in urology. Results: AI-based models demonstrated promising performance across multiple diagnostic domains, including imaging (mpMRI, ultrasound), histopathology, and biomarker-based analysis. Several studies demonstrated improved diagnostic accuracy and reduced interobserver variability compared to conventional methods. AI also showed potential in differentiating BPH from prostate cancer and supporting clinical decision-making. However, significant challenges remain, including heterogeneity of datasets, limited external validation, potential algorithmic bias, and lack of standardized evaluation frameworks. Conclusion: Artificial intelligence represents a promising adjunct in the diagnosis of BPH, with the potential to enhance diagnostic accuracy and optimize clinical workflows. Nevertheless, further high-quality studies, standardized validation, and integration into real-world clinical settings are required before widespread clinical adoption can be achieved.
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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.