Artificial intelligence–driven digital pathology in urological cancers: current trends and future directions

Artificial intelligence (AI) in digital pathology has gained attention owing to its potential in urological cancer diagnosis and management. This review highlights AI's applications and challenges in three major urological cancers. Prostate cancer studies have demonstrated reliable diagnostic performance and promising prognosis prediction. Renal cancer study shows potential but faces challenges in generalizability and prognosis. Bladder cancer studies are limited by the lack of large-scale datasets. Despite of these active studies, challenges remain regarding data availability, prognosis, and generalizability. Future efforts should emphasize multimodal approaches and multi-institutional collaboration with larger datasets to fully realize the potential of AI in urological cancers.

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Publication Details

Journal
Prostate International
Published
2025-02-16
DOI
https://doi.org/10.1016/j.prnil.2025.02.002
Citations
4
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
5.44
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article

Artificial intelligence–driven digital pathology in urological cancers: current trends and future directions

Inyoung Paik, Geongyu Lee, Tae-Yeong Kwak, Hong Koo Ha et al.
4 citations
Prostate International
Radiomics and Machine Learning in Medical Imaging
5.44
article

Artificial intelligence–driven digital pathology in urological cancers: current trends and future directions

Inyoung Paik, Geongyu Lee, Tae-Yeong Kwak, Hong Koo Ha, Joon-Ho Lee
article en
4 citations

Abstract

Artificial intelligence (AI) in digital pathology has gained attention owing to its potential in urological cancer diagnosis and management. This review highlights AI's applications and challenges in three major urological cancers. Prostate cancer studies have demonstrated reliable diagnostic performance and promising prognosis prediction. Renal cancer study shows potential but faces challenges in generalizability and prognosis. Bladder cancer studies are limited by the lack of large-scale datasets. Despite of these active studies, challenges remain regarding data availability, prognosis, and generalizability. Future efforts should emphasize multimodal approaches and multi-institutional collaboration with larger datasets to fully realize the potential of AI in urological cancers.

Prostate InternationalVol. 13(4)
Biosolution (South Korea) (KR), Pusan National University Hospital (KR)
Good health and well-being
Openalex Percentile: Top 5%
Radiomics and Machine Learning in Medical Imaging
5.44
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Artificial intelligence–driven digital pathology in urological cancers: current trends and future directions — Inyoung Paik, Geongyu Lee, et al. · Prostate International (2025) | TGRS Research Map | TGRS