Systematic analysis traces AI's growth in urology, highlighting clinical impact and collaborative efforts.
This study traces AI's evolution in urology, from basic ML to advanced clinical tools, with particular advancement in radiomics, imaging, and biomarker analysis. Successful future implementation necessitates addressing ethical considerations, technical hurdles, and practical challenges while maintaining focus on patient safety and equitable healthcare access.
No takes yet. Share an insight, caveat, or question.
Hatampour et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: