Utilizing Artificial Intelligence in Digital Pathology for Urological Applications: A Comprehensive Review

Abstract

Purpose: To conduct a scoping review of the literature identifying clinical scenarios for the use of digital pathology (DP) in urology, its advantages, and obstacles to clinical adoption. Material and Methods: A comprehensive literature search was conducted using PubMed/MEDLINE and Google Scholar databases. Search terms included “digital pathology,” “artificial intelligence,” “urology,” “whole slide imaging,” “telepathology,” and “pathology diagnostics.” The PubMed/MEDLINE search yielded 173 articles; supplementary Google Scholar searches and reference-list screening broadened the scope. Inclusion criteria required peer-reviewed studies or reviews addressing AI or DP in a urological context. Studies lacking clinical relevance to genitourinary pathology were excluded. The search strategy and selection process are summarized in Figure 1. Results: In urological pathology, DP enables three-dimensional (3D) pathomorphological reconstruction, improves the accuracy of grading and staging, facilitates intraoperative real-time slice assessment, supports specialist training, and allows large-scale information storage—opening new horizons in telepathology. Artificial intelligence algorithms, difficult to apply in classical light microscopy, offer further significant prospects for pathologists. Despite these advantages, limitations including high equipment costs, large image file sizes, server dependency, data privacy concerns, and the need for specialist training continue to slow widespread adoption. Conclusion: DP empowers urologists, pathologists, and healthcare professionals with tools to achieve more precise diagnoses, personalize treatments, and deliver a new standard of patient care. While challenges accompany this digital transformation, the potential benefits are profound and far-reaching. Future efforts should prioritize standardization, regulatory approval, ethical frameworks, and training to accelerate clinical adoption.