Guest Editor(s)
Dr. Yadong Guo
Email: guoyadong@tongji.edu.cn
Affiliation: Department of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
Homepage:
Research Interests: urological oncology, tumor immunology, immune escape mechanisms, biomarker discovery, multi-omics analysis, artificial intelligence in urology, and precision patient care.
Dr. Doblin Sandai
Email: doblin@usm.my
Affiliation: Department of Biomedical Sciences, Advanced Medical and Dental Institute, Universiti Sains Malaysia, 13200 Kepala Batas, Penang, Malaysia
Homepage: https://scholar.google.com/citations?user=fDo1dR8AAAAJ&hl=en&oi=ao
Research Interests: molecular medical mycology, pathogen-host interactions, biofilm-related oncology, and AI-assisted microbial/cellular image analysis
Dr. Radka Michalková
Email: radka.michalkova@upjs.sk
Affiliation: Josep Carreras Leukaemia Research Institute / University of Barcelona, Barcelona, Spain
Homepage:
Research Interests: cancer epigenetics, epigenetic biomarkers, multi-omics data integration, precision oncology, and clinical epigenomics
Summary
Background: Artificial intelligence is rapidly reshaping clinical medicine, and urology is one of the most promising fields for its translational application. AI-based approaches are increasingly being explored in prostate cancer diagnosis, bladder cancer surveillance, renal tumor characterization, urological imaging, digital pathology, robotic surgery, perioperative risk prediction, treatment selection, and long-term follow-up.
However, many AI-related studies remain limited to algorithm development or retrospective computational modeling without sufficient clinical validation. For AI to truly benefit urological practice, future studies must move beyond technical performance metrics and demonstrate clinical relevance, external validation, interpretability, and real-world applicability.
This Special Issue aims to provide a focused platform for clinically meaningful AI research in urology, with particular emphasis on translational studies supported by clinical cohorts, real-world datasets, multi-center validation, prospective evaluation, or clearly defined clinical decision-making scenarios.
Aim and Scope: The proposed Special Issue will focus on the clinical and translational application of artificial intelligence in urology.
Priority will be given to studies that address practical clinical questions and demonstrate potential value for urological diagnosis, prognosis, treatment planning, surgical decision-making, follow-up, and patient management.
Submissions based solely on algorithm development, public database mining, or purely computational modeling without clinical cohort validation or translational relevance will not be prioritized.
Suggested Topics include, but are not limited to:
1. AI-assisted diagnosis and risk stratification in prostate cancer, bladder cancer, renal cancer, and other urological diseases
2. Radiomics, radiogenomics, and imaging-based AI models in urology
3. AI-assisted pathology and digital pathology for urological malignancies
4. Clinical decision-support systems for urological cancer management
5. AI-guided prediction of treatment response, recurrence, progression, and survival
6. Integration of AI with clinical, imaging, pathological, genomic, and multi-omics data
7. AI applications in robotic surgery, surgical planning, and perioperative risk prediction
8. Real-world data and electronic health record-based AI models in urology
9. AI-assisted surveillance and follow-up strategies for urological diseases
10. Explainable AI, model calibration, external validation, and clinical implementation in urological practice
11. Ethical, regulatory, and practical considerations for AI adoption in urology
Keywords
Artificial Intelligence, clinical urology, precision medicine, machine learning, deep learning, radiomics, clinical validation, biomarkers, robotic surgery, explainable AI
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