Artificial Intelligence in the Diagnosis of Urological Diseases: A Narrative Review

Abstract

AI has made progress quicker in urology compared to other surgical fields due to prostate imaging, bladder cytology, stone classification, and urodynamic assessment producing the types of structured data that machine learning algorithms work best on. This narrative review takes stock of where diagnostic AI actually stands across four areas of urological practice prostate cancer, bladder cancer, urolithiasis, and functional or infectious disorders of the lower urinary tract and asks a question that is often glossed over in enthusiastic overviews of the field: how much of this evidence has actually been tested prospectively against clinicians, rather than only benchmarked against a retrospective dataset? Prostate cancer detection on MRI has the strongest claim to an answer, following an international confirmatory study (PI-CAI) that pitted AI systems directly against a large panel of radiologists. Bladder cancer has a comparable, if smaller, body of prospective evidence in AI-assisted urinary cytology. Stone disease and functional urology, by contrast, remain almost entirely at the retrospective, single- or two-center stage. We argue that the central obstacle to clinical adoption is no longer diagnostic accuracy, which is already respectable in most published models, but the slower and less glamorous work of external validation, interpretability, and regulatory clearance.