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Artificial Intelligence as a Diagnostic Tool for Benign Prostatic Hyperplasia (BPH): A Narrative Review
1Department of Surgery, College of Medicine, King Faisal University, Al Ahsa, Saudi Arabia.
Research and Reports in Urology
|June 4, 2026
Summary
Artificial intelligence (AI) shows promise in improving benign prostatic hyperplasia (BPH) diagnosis by enhancing accuracy and reducing variability. Further validation and integration are needed for widespread clinical use of AI in BPH management.
Area of Science:
- Urology
- Medical Imaging
- Artificial Intelligence
Background:
- Benign prostatic hyperplasia (BPH) is a common condition in aging men, posing significant clinical and economic challenges.
- Current diagnostic methods for BPH, such as PSA testing and imaging, suffer from low specificity and interpretation variability, complicating differentiation from prostate cancer.
Purpose of the Study:
- This review evaluates artificial intelligence (AI) as a diagnostic tool for BPH.
- The study focuses on AI's performance, clinical applications, and potential to overcome existing diagnostic limitations in BPH.
Main Methods:
- A narrative review of studies published between 2021 and 2025 was conducted.
- Databases searched included PubMed, Scopus, and ScienceDirect using keywords related to BPH and AI (machine learning, deep learning).
- Ten studies focusing on AI's diagnostic applications in urology were analyzed.
Main Results:
- AI models demonstrated strong performance in diagnosing BPH across imaging, histopathology, and biomarker analyses.
- AI improved diagnostic accuracy and reduced interobserver variability compared to traditional methods.
- AI showed potential in distinguishing BPH from prostate cancer and aiding clinical decisions, though challenges like data heterogeneity and validation exist.
Conclusions:
- AI offers a promising tool to augment BPH diagnosis, potentially improving accuracy and streamlining clinical workflows.
- Widespread adoption of AI for BPH diagnosis requires further high-quality research, standardized validation, and integration into clinical practice.
