Open Access

REVIEW

Artificial intelligence in urological malignancy diagnosis and prognosis: current status and future prospects

Mingwei Zhan^1,#, Zhaokai Zhou^2,#, Jianpeng Zhang^3,#, Xin Wang⁴, Canxuan Li⁵, Bochen Pan⁶, Zhanyang Luo⁷, Wenjie Shi⁸, Yongjie Wang⁹, Minglun Li¹⁰, Weizhuo Wang^11,*, Run Shi^12,*, Jingyu Zhu^1,13,*

1 Department of Urology, Hangzhou TCM Hospital of Zhejiang Chinese Medical University (Hangzhou Hospital of Traditional Chinese Medicine), Hangzhou, China
2 Department of Urology, The Second Xiangya Hospital of Central South University, Changsha, China
3 Department of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
4 Department of Oncology, Jiangsu Cancer Hospital, Nanjing, China
5 Department of Urology, Shenshan Medical Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Shanwei, China
6 Department of Urology, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
7 Department of Pharmacy, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai, China
8 Molecular and Experimental Surgery, Clinic for Visceral, General, Vascular and Transplantation Surgery, University Medicine Magdeburg, Otto-von Guericke University Magdeburg, Magdeburg, Germany
9 Proteomics and Cancer Cell Signaling Group, German Cancer Research Center, Heidelberg, Germany
10 Department of Radiation Oncology, Lueneburg Hospital, Lueneburg, Germany
11 Department of Urology, Suzhou Municipal Hospital, Suzhou, China
12 Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
13 Department of Urology, Hangzhou Integrative Medicine Hospital Affiliated to Zhejiang Chinese Medical University (Hangzhou Red Cross Hospital), Hangzhou, China

* Corresponding Authors: Weizhuo Wang. Email: ; Run Shi. Email: ; Jingyu Zhu. Email:
# These authors contributed equally to this work

Canadian Journal of Urology 2026, 33(1), 35-49. https://doi.org/10.32604/cju.2026.076084

Received 13 November 2025; Accepted 13 January 2026; Issue published 28 February 2026

Abstract

Artificial intelligence (AI) is transforming the diagnostic landscape of malignant tumors in the urinary system, including prostate cancer, bladder cancer, and renal cell carcinoma (RCC). By integrating imaging, pathology, and molecular data, AI enhances the precision and reproducibility of tumor detection, grading, and risk stratification. In prostate cancer, AI-assisted multiparametric Magnetic resonance imaging (MRI) and digital pathology systems improve lesion localization and Gleason scoring. For bladder cancer, deep learning-based cystoscopy and radiomics models from Computed tomography/magnetic resonance imaging (CT/MRI) enable real-time lesion segmentation and non-invasive biomarker prediction, such as Programmed Cell Death-Ligand 1 (PD-L1) expression. In RCC, AI, combined with CT/MRI and multi-omics data, aids in subtype classification and prognostic prediction, supporting personalized therapy. However, despite these promising advances, challenges such as data standardization, model generalizability, interpretability, and regulatory compliance hinder AI’s clinical translation. This review outlines the current state of AI in urological cancer diagnosis and prognosis, its technological innovations, and the clinical challenges and opportunities that lie ahead.

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