Global trends in the use of artificial intelligence for urological tumor histopathology: A 20-year bibliometric analysis - Fazhong Dai; Yifeng He; Juan Duan; Kangjian Lin; Qian Lv; Zhongxiang Zhao; Yesong Zou; Jianhong Jiang; Zongtai Zheng; Xiaofu Qiu (Digital Health)
Background The field of urological tumor histopathology has long relied on subjective pathologist expertise, leading to diagnostic variability. Recent advances in digital pathology and artificial intelligence (AI) offer transformative potential by standardizing diagnoses, improving accuracy, and bridging healthcare disparities. This study conducted a 20-year bibliometric analysis to map global research trends and innovations in AI-driven urological pathology. Methods For this bibliometric analysis, literature from 2004 to 2024 was retrieved from the Web of Science Core Collection. CiteSpace, VOSviewer, and Microsoft Excel were used to visualize coauthorship, cocitation, and co-occurrence analyses of countries/regions, institutions, authors, references, and keywords in the field of AI for urological tumor histopathology. Results A total of 199 papers were included. Research on AI-driven urological tumor pathology has steadily increased since 2005, with a significant surge between 2020 and 2023. The United States made the largest contribution in terms of publications (131), citations (4725), and collaborations. The most productive institution was the University of Southern California, while Patel et al. and Epstein et al. were identified as the most active and most cocited authors, respectively. European Urology led in both publication volume and impact. Keyword analysis identified “machine learning,” “prostate cancer,” “deep learning,” and “diagnosis” as major research foci. Conclusions 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.