From robot assistance to surgical intelligence: global research trends and emerging frontiers of artificial intelligence-enhanced robotic surgery in urology.

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not applicable (bibliometric study; no clinical effect size calculated). 253 publications included from 401 retrieved; rapid growth observed post-2018 with a peak of 53 publications in 2025

• Primary Outcome: Characterisation of global publication trends, collaboration networks, and research hotspots in AI-enhanced robotic urological surgery from 2004 to 2026

• Nnt Or Sensitivity: Not applicable. Key thematic clusters identified: renal cell carcinoma, augmented reality, image-guided surgery, indocyanine green, machine learning, registration, deep learning, and bladder cancer. Leading countries: Italy, USA, Netherlands, China, Japan. Leading institutions: University of Turin, Azienda Ospedaliero-Universitaria San Luigi Gonzaga, IRCCS Fondazione del Piemonte per l'Oncologia, Netherlands Cancer Institute, Leiden University Medical Center.

• Confidence Interval: Not reported (descriptive bibliometric analysis)

Clinical Application

The bibliometric findings are useful for research prioritisation and grant strategy rather than direct clinical implementation. Identified frontiers (deep learning-based segmentation, surgical video understanding, skill assessment, augmented reality) represent active areas where prospective clinical validation studies are needed before routine adoption. Australia has a well-established robotic surgical programme, particularly in uro-oncology (radical prostatectomy, partial nephrectomy, radical cystectomy), supported by the da Vinci platform across major public and private centres. The TGA has not yet approved specific AI-enhanced intraoperative decision-support tools for robotic urology as standalone regulated devices. The RACGP and relevant specialist colleges (USANZ — Urological Society of Australia and New Zealand) have not issued specific guidelines on AI integration in robotic surgery. The PBS does not currently list AI-specific surgical adjuncts. This bibliometric study signals that Australian institutions should consider participating in multicenter international consortia for dataset sharing and prospective validation, particularly given Australia's relatively small publication footprint in this space compared to the identified leading nations. The identified research gaps (standardised datasets, model interpretability, prospective validation) align with priorities that the Australian Centre for Health Services Innovation and the Medical Research Future Fund could strategically address. Urological surgeons, surgical robotics researchers, health technology assessment bodies, and healthcare administrators evaluating the maturity and direction of AI-enhanced robotic surgery programmes. Not directly applicable to individual patient care decisions.

Abstract

To systematically characterize global publication trends, collaboration patterns, intellectual foundations, research hotspots, and emerging frontiers in artificial intelligence (AI)-enhanced robotic and robot-assisted surgery in urology. Publications were retrieved from the Web of Science Core Collection (WoSCC) using a topic search strategy that included three keyword groups: robot-assisted surgery, urologic diseases or procedures, and AI-related technologies. English-language articles and reviews were included. Meeting abstracts, conference proceedings, editorials, letters, non-English publications, studies unrelated to urologic robotic surgery, and studies without substantive AI or intelligent algorithmic content were excluded. RStudio was used for descriptive statistics and annual publication trend visualization. VOSviewer was used for auxiliary bibliometric network construction and visualization. CiteSpace was used to construct collaboration networks, co-citation networks, keyword co-occurrence maps, cluster maps, timeline and time-zone maps, and citation burst maps. A total of 401 records were initially retrieved, and 253 publications were finally included after screening by document type, language, and topical relevance. These comprised 199 articles (78.66%) and 54 reviews (21.34%). Publications in this field began in 2004 and increased rapidly after 2018, reaching a peak of 53 publications in 2025. Because 2026 was an incomplete retrieval year, only 23 publications were recorded. Italy, the United States, the Netherlands, China, and Japan were the leading contributing countries. The University of Turin, Azienda Ospedaliero-Universitaria San Luigi Gonzaga, IRCCS Fondazione del Piemonte per l'Oncologia, the Netherlands Cancer Institute, and Leiden University Medical Center were the most productive institutions. Keyword clustering showed that the major research hotspots included renal cell carcinoma, augmented reality, image-guided surgery, indocyanine green, machine learning, registration, deep learning, and bladder cancer. AI-enhanced robotic surgery in urology has evolved from early research on registration, navigation, and image-guided surgery toward a surgical intelligence stage characterized by augmented reality, three-dimensional reconstruction, machine learning-based prediction, deep learning-based segmentation, surgical video understanding, and skill assessment. Future studies should prioritize multicenter standardized datasets, sharing of intraoperative video and robotic platform data, model interpretability, prospective validation, and integration into real-world clinical workflows.

References

1. 1.Jiao, P., Chen, S., Yang, J., Wang, L., Cheng, K., Si, X., & Yang, L. (2026). From robot assistance to surgical intelligence: global research trends and emerging frontiers of artificial intelligence-enhanced robotic surgery in urology. Journal of Robotic Surgery. PubMed ID: 42503526.