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Digital pathology-based artificial intelligence algorithms in prostate cancer: inside the 'black box'.

Claire M de la Calle1,2, Alexander S Baras3, Tamara L Lotan3,4,5

  • 1Department of Urology, University of Washington, Seattle, WA, USA.

BJU International
|February 16, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) in digital pathology is revolutionizing prostate cancer diagnosis and grading. AI algorithms offer improved accuracy, reduced variability, and enhanced prognostic information compared to traditional methods.

Keywords:
artificial intelligencepathologypredictionprognosisprostate cancerwhole slide images

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Area of Science:

  • Digital pathology and artificial intelligence (AI) applications in oncology.
  • Histopathology and computational pathology for cancer research.

Background:

  • AI algorithms analyzing digital pathology slides are transforming urological cancer diagnosis and grading.
  • These systems provide prognostic, predictive, and molecular subtyping information beyond traditional methods.

Purpose of the Study:

  • To review recent advances in histopathology-based AI systems for prostate cancer.
  • To evaluate AI performance against pathologists for diagnosis and grading.
  • To highlight AI's role in prognostic prediction and therapy response.

Main Methods:

  • Review of current literature on AI in prostate cancer histopathology.
  • Comparative analysis of AI algorithms versus pathologists for tumor diagnosis and grading.
  • Benchmarking of prognostic AI algorithms against patient outcomes (metastasis, death).

Main Results:

  • AI algorithms demonstrate comparable or superior performance to pathologists in tumor diagnosis and grading.
  • AI significantly reduces inter-observer variability and provides quantified tumor metrics.
  • Emerging AI capabilities include predicting therapy response and molecular alterations.

Conclusions:

  • AI in digital pathology holds significant potential for standardizing prostate cancer assessment.
  • AI can guide clinical management and improve patient outcomes.
  • Implementation of AI in clinical practice faces advantages and barriers that require consideration.