Guest Editor(s)
Dr. Lei Peng
Email: penglei933@163.com
Affiliation: Department of Urology, South China Hospital, Shenzhen University, Shenzhen, China, China
Homepage:
Research Interests: urology, medical artificial intelligence, development and clinical evaluation of AI models
Dr. Jinze Li
Email: dr_lijinze@163.com
Affiliation: Department of Urology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
Homepage:
Research Interests: urologic diseases, chronic inflammation, chinese and western integrative medicine, bioinformatics
Dr. Rui Liang
Email: liangrui@szu.edu.cn
Affiliation: Department of Urology, Shenzhen University, Shenzhen, China
Homepage:
Research Interests: AI-assisted urology, wearable clinical monitoring, bionanotechnology for bladder cancer therapy, and translational medicine-engineering research.
Dr. Guocheng Huang
Email: guochen4@ualberta.ca
Affiliation: Department of Surgery, Division of Urology, University of Alberta, Edmonton, Canada
Homepage:
Research Interests: urology, cancer diagnosis and treatment, ai-assisted clinical decision-making, machine learning for urologic oncology, artificial intelligence in urologic imaging.
Summary
Artificial intelligence is rapidly reshaping the landscape of modern urology, offering powerful tools to improve disease diagnosis, risk prediction, treatment planning, surgical assistance, and long-term patient management. The increasing availability of diverse data sources—including medical imaging, digital pathology, electronic health records, laboratory results, genomic and multi-omics datasets, surgical videos, and patient-reported outcomes—has created new opportunities for the development of data-driven and clinically meaningful AI applications in urological care.
This Special Issue aims to highlight innovative research on the development, validation, and implementation of artificial intelligence in urology. We particularly encourage submissions that apply advanced approaches such as machine learning, deep learning, radiomics, natural language processing, digital pathology, bioinformatics, and large language models, while emphasizing clinical relevance, external validation, interpretability, and translational value. By bringing together urologists, oncologists, radiologists, pathologists, biomedical engineers, data scientists, and bioinformaticians, this Special Issue seeks to promote responsible, transparent, and clinically useful AI technologies for improving outcomes in urological diseases.
Topics of interest include, but are not limited to:
• AI-assisted diagnosis, prognosis prediction, and risk stratification in urological diseases
• Radiomics, medical imaging, and computer-aided detection in prostate, bladder, kidney, and other urological conditions
• Digital pathology and AI-based histopathological assessment in urologic oncology
• Predictive modeling for treatment response, recurrence, survival, and personalized therapy
• AI-assisted robotic surgery, surgical video analysis, and surgical decision support
• Large language models and natural language processing for clinical documentation, decision support, patient education, and knowledge management
• Multi-omics integration, bioinformatics analysis, and AI-driven biomarker discovery
• Clinical validation, real-world implementation, ethical considerations, and regulatory issues of AI tools in urology
Keywords
artificial intelligence, urology, machine learning, deep learning, clinical translation
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