Mapping the evolution of deep learning and computer vision in robotic surgery: a bibliometric analysis of surgical...

Deep learning and computer vision are increasingly embedded in robotic surgery, yet the development and translational direction of this research domain remain incompletely characterized. We conducted a bibliometric and visualization analysis of publications retrieved from the Web of Science Core Collection using Bibliometrix/Biblioshiny, VOSviewer, and CiteSpace. A total of 1,186 documents published between 2010 and 2026 across 356 sources were included. Scientific output increased rapidly, with an annual growth rate of 16.09% and a peak of 216 publications in 2025. The field involved 5,296 authors, and international collaboration accounted for 30.69% of publications. IEEE Robotics and Automation Letters was the most productive and locally influential source. China and the United States were the leading contributors, with China showing the most rapid recent expansion and the United States retaining the highest citation impact. Citation-burst and keyword analyses identified U-Net, residual learning, transformer architectures, and foundation-model-enabled segmentation as major methodological drivers. The conceptual structure evolved from image guidance, registration, and navigation toward surgical video intelligence, instrument perception, workflow understanding, autonomous assistance, and clinical translation. Instrument perception emerged as a central link between algorithmic development and operative application. Future progress will require diverse multi-institutional datasets, external and prospective validation, integrated scene understanding, and rigorous evaluation of intelligent assistance within real robotic surgical workflows.