The Translation Gap in Artificial Intelligence-Based Renal Mass Characterization: A Narrative Review

Artificial intelligence (AI) models for solid renal mass characterization show promising discrimination in multicenter and some prospective test sets, yet little published evidence supports their routine clinical use. We examine four translation gaps. Independent, representative validation remains limited, and most studies use resected, pathologically confirmed masses, underrepresenting small indeterminate lesions managed by biopsy or surveillance. Recent multicenter models, including RenalCLIP and MPANet, broadened tasks and external testing but remain retrospective. Four retrospective reader experiments suggest that AI assistance can improve readers’ accuracy or consistency, but no study identified measured effects on management or patient outcomes. Regulatory evidence is limited: one renal tool reported by its manufacturer as certified under the European Union Medical Device Regulation rests on a single-center retrospective reader study of detection and reporting, and no characterization device was identified in the U.S. Food and Drug Administration AI-enabled device list. The 2026 European Association of Urology guidelines discuss AI as an emerging technology without a graded recommendation. Cost, information technology integration, trust, and interpretability further restrict adoption. We propose a roadmap built around one intended use, the small indeterminate solid mass considered for biopsy or surveillance, with prespecified thresholds, target-specific reference standards, and paired management and safety endpoints.