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Updated: Jan 14, 2026

Ultrasonography of the Adult Male Urinary Tract for Urinary Functional Testing

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Published on: August 14, 2019

Artificial Intelligence in Benign Prostatic Hyperplasia.

Liam Murad¹, David Bouhadana², Aalya Hamouda¹

¹McGill University, Montreal, Québec H3A 1G1, Canada.

The Urologic Clinics of North America

|October 17, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) enhances benign prostatic hyperplasia (BPH) care through improved diagnostics and treatment planning. Future AI applications may further refine image-guided surgeries and streamline clinical workflows for better patient outcomes.

Keywords:

Artificial intelligence BPH Benign prostatic hyperplasia LUTS Lower urinary tract symptoms Machine learning Prostate cancer

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Area of Science:

Urology
Medical Informatics
Artificial Intelligence in Healthcare

Background:

Benign prostatic hyperplasia (BPH) is a common condition affecting aging men.
Current BPH management involves diagnostics, treatment planning, and outcome prediction.
Artificial intelligence (AI) offers advanced tools for analyzing health data and supporting clinical decisions.

Purpose of the Study:

To review current applications of AI in benign prostatic hyperplasia (BPH).
To focus on AI's role in diagnostic imaging, multimodal data integration, and therapeutic management for BPH.
To identify barriers and future research directions for AI in BPH.

Main Methods:

Review of current literature on AI applications in BPH.
Analysis of AI's utility in BPH diagnostics, treatment, and outcome prediction.
Identification of implementation challenges and future research avenues.

Main Results:

AI demonstrates significant promise in improving diagnostics and treatment planning for BPH.
AI applications are being explored in diagnostic imaging and multimodal data integration.
Barriers include small sample sizes, homogenous data, and non-standardized performance reporting.

Conclusions:

AI holds substantial potential to advance BPH care, from diagnosis to treatment.
Overcoming implementation barriers is crucial for widespread AI adoption in BPH.
Future research should focus on AI-enhanced image-guided surgeries and natural language processing for clinical workflows.