Artificial intelligence (AI) has emerged as a promising technology, with integration of machine learning (ML), deep learning (DL), and other similar models to address critical challenges in urological practice including early cancer detection, diagnostic accuracy, and treatment selection. However, a consensus on the clinical utility of AI remains elusive.

We conducted a systematic search across multiple electronic databases (MEDLINE, Embase, Cochrane Library) for studies published between January 2010 and October 2024 that evaluated AI applications in urological diagnostics. Studies were assessed using an adapted QUADAS-2 tool and AI-specific quality assessment criteria; and pooled diagnostic accuracy measures were calculated.

The current meta-analysis identified 47 studies implementing machine learning for prostate cancer detection using mpMRI data, with a pooled sensitivity of 0.87 (95% CI: 0.83 - 0.91) and specificity of 0.83 (95% CI: 0.79 - 0.87) for clinically significant prostate cancer detection. Another 23 studies utilized deep learning models and convolutional neural networks (CNNs) for bladder cancer diagnosis, demonstrating a pooled sensitivity of 0.91 (95% CI: 0.88-0.94) and specificity of 0.87 (95% CI: 0.83-0.90). Finally, 29 studies undertook evaluation of use of AI in characterization of renal masses, with a focus on distinguishing benign from malignant lesions. These studies demonstrated a pooled accuracy of 87.3% (95% CI: 84.1-90.5%) for differentiating renal cell carcinoma subtypes, exceeding the reported accuracy of conventional radiological assessment (71.2%, 95% CI: 67.5-74.9%). Sub-group analyses revealed that studies with larger sample sizes (>500 patients) reported more modest performance metrics compared to smaller studies (AUC 0.89 vs. 0.94, p=0.003), suggesting a possible publication bias or overfitting in smaller datasets. Additionally, studies employing deep learning approaches demonstrated superior performance compared to traditional machine learning methods (pooled AUC 0.93 vs. 0.88, p<0.001). 67.4% of studies utilized fewer than 500 patients for algorithm development, mirroring concerns raised previously, regarding the statistical power and generalizability of AI models in healthcare. The prevalence of small sample sizes and a limited demographic diversity, hence, represents a substantial methodological limitation.

Artificial intelligence applications in urological diagnostics demonstrate significant potential to enhance clinical care through improved accuracy, efficiency, and standardization.