Background: Urological cancers, such as prostate, bladder and renal cell carcinoma, contribute substantially to the global cancer burden. Their management remains challenging due to extensive molecular and clinical heterogeneity. Conventional single-omics approaches (e.g., genomics and transcriptomics) have led to important discoveries but provided only partial views of tumour biology, which limit the robustness of biomarkers and therapeutic precision. Multi-omics integration offers a systems-level perspective that captures the complex regulatory networks underlying tumour initiation, progression and treatment resistance.

Conclusions: Multi-omics integration is rapidly evolving from an exploratory research tool into a cornerstone of precision urology. Through mechanistically grounded, clinically interpretable models of disease, multi-omics holds the potential to improve individualised diagnosis, prognostication and therapy selection. Translation of multi-omics into routine clinical practice will hinge on overcoming current limitations through standardisation, collaborative consortia and explainable artificial intelligence.

Background: Pediatric urolithiasis is an increasingly important health concern, and affected children and their families require information that is both accurate and easily understandable. Artificial intelligence (AI)-powered chatbots have become widely used sources of health information; however, the readability, quality, and reliability of their outputs remain insufficiently evaluated. This study aimed to assess the effectiveness and reliability of AI chatbots in providing patient-oriented information on pediatric kidney stone disease and to identify factors influencing the quality and readability of their responses.

Methods: Four AI chatbots (ChatGPT-5, Google Gemini, Claude 3 Opus, and DeepSEEK) were queried with 30 standardized questions related to pediatric kidney stones. Readability was evaluated using the Average Reading Level Consensus (ARLC), Automated Readability Index (ARI), and Simple Measure of Gobbledygook (SMOG). Response quality and reliability were asssessed using the Ensuring Quality Information for Patients (EQIP) tool and Modified DISCERN score. Statistical analyses included one-way analysis of variance ANOVA, Kruskal-Wallis tests, and appropriate post hoc comparisons.

Results: Readability differed significantly among the chatbots. Google Gemini demonstrated the highest reading levels across all metrics (ARLC: 14.93, ARI: 16.2, and SMOG: 13.32), whereas ChatGPT, Claude, and DeepSEEK produced less complex test (p < 0.001; large effect sizes, η⊃2; = 0.195–0.512). EQIP scores did not differ significantly between models (p = 0.491, ε⊃2; = 0.021, negligible effect), indicating comparable informational quality. In contrast, reliability varied significantly: ChatGPT and Google Gemini achieved higher Modified DISCERN scores (median 4.00) than Claude and DeepSEEK (median 3.00; p = 0.001, ε⊃2; = 0.318, large effect). Subgroup analyses by question category revealed notable differences in performance, highlighting model-specific strenghts and limitations.

Background: Artificial intelligence (AI) and big data are transforming urological oncology by enhancing diagnostic precision, prognostic assessment and treatment personalisation for prostate, bladder and kidney cancer.

Methods: We searched PubMed and MEDLINE up to September 2025 for English-language, peer-reviewed human studies using terms including “artificial intelligence”, “deep learning”, “radiomics”, “real-world evidence” and “urological oncology”.

Results: AI-driven radiomics and deep learning models have demonstrated high accuracy in detecting and characterising urological malignancies by using magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET) and histopathology. In prostate, bladder and kidney cancers, AI-driven radiomics and deep learning models have demonstrated high diagnostic performance, with reported area under the curves (AUCs) typically ranging from 0.80 to 0.95 for lesion detection, staging and risk stratification. Sensitivities and specificities in cystoscopic image analysis often exceed 90%, but radiogenomic models for renal cancer achieve mutation prediction accuracies of 85%–95%.

Conclusions: AI and big data are reshaping urological oncology by integrating diagnostic imaging, pathology and real-world practice. Their continued integration promises a precise, equitable and adaptive model of cancer care. Despite these robust results, most studies rely on retrospective or single-centre datasets with limited external validation, raising concerns about generalisability. Future progress will depend on multicentre standardisation, federated learning frameworks and incorporation of multimodal real-world data to facilitate clinically robust and implementable AI systems.