Global trends in the use of artificial intelligence for urological tumor histopathology: A 20-year bibliometric analysis

Abstract

The integration of AI into urological tumor pathology demonstrates transformative potential, significantly enhancing diagnostic accuracy and efficiency through automated analysis of whole-slide imaging and Gleason grading, comparable to pathologist-level performance. However, clinical translation encounters critical challenges, including data bias, model interpretability ("black-box" limitations), and regulatory-ethical complexities. Future advancements hinge on developing explainable AI frameworks, multimodal systems integrating histopathology, radiomics, and genomics and establishing global collaborative networks to address resource disparities. Prioritizing standardized data protocols, fairness-aware algorithms, and dynamic regulatory guidelines will be essential to ensure equitable, reliable, and clinically actionable AI solutions, ultimately advancing precision oncology in urological malignancies.

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