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AI for BPH Surgical Decision-Making: Cost Effectiveness and Outcomes

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Abstract

Purpose of Review

Benign prostatic hyperplasia (BPH) is prevalent in nearly 70% of men over the age of 60, leading to significant clinical challenges due to varying symptom presentations and treatment responses. The decision to undergo surgical intervention is not straightforward; the American Urological Association recommends consideration of surgical treatment after inadequate or failed response to medical therapy. This review explores the role of artificial intelligence (AI), including machine learning and deep learning models, in enhancing the decision-making processes for BPH management.

Recent Findings

AI applications in this space include analysis of non-invasive imaging modalities, such as multiparametric Magnetic Resonance Imaging (MRI) and Ultrasound, which enhance diagnostic precision. AI models also concatenate serum biomarkers and histopathological analysis to distinguish BPH from prostate cancer (PC), offering high accuracy rates. Furthermore, AI aids in predicting patient outcomes post-treatment, supporting personalized medicine, and optimizing therapeutic strategies.

Summary

AI has demonstrated potential in differentiating BPH from PC through advanced imaging and predictive models, improving diagnostic accuracy, and reducing the need for invasive procedures. Despite promising advancements, challenges remain in integrating AI into clinical workflows, establishing standard evaluation metrics, and achieving cost-effectiveness. Here, we underscore the potential of AI to improve patient outcomes, streamline BPH management, and reduce healthcare costs, especially with continued research and development in this transformative field.

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Data Availability

No datasets were generated or analysed during the current study.

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Funding

There was no funding for this review.

Author information

Author notes

  1. John Lama and Joshua Winograd authors contributed equally to this work.

Authors and Affiliations

  1. Weill Cornell Medical College, New York City, NY, USA

    John Lama & Joshua Winograd

  2. Department of Urology, Northwell Health, Manhasset, NY, USA

    Alia Codelia-Anjum & Bilal Chughtai

  3. Department of Urology, University of Montreal, Montreal, Canada

    Naeem Bhojani & Kevin C. Zorn

  4. Division of Urology, University of Toronto, Toronto, Canada

    Dean Elterman

  5. BPHCanada Prostate Surgery Institute, Montreal, QC, Canada

    Kevin C. Zorn

Authors
  1. John Lama
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  2. Joshua Winograd
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  4. Naeem Bhojani
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Contributions

J.L. and J.W. wrote the main manuscript text and prepared Table 1. All authors helped with conceptualization, methodology, and manuscript review.

Corresponding author

Correspondence to Bilal Chughtai.

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Lama, J., Winograd, J., Codelia-Anjum, A. et al. AI for BPH Surgical Decision-Making: Cost Effectiveness and Outcomes. Curr Urol Rep 26, 4 (2025). https://doi.org/10.1007/s11934-024-01240-6

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