GeSn QD decorated graphene/n-Ge mixed-dimensional heterostructure for extended shortwave infrared detection.

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.