Learning curves and training in robotic organ transplantation: bibliometric mapping with kidney transplantation as the principal clinical model.

Literature on learning and training in robotic transplantation spans multiple procedures, but its evidence base is strongly kidney-dominant and direct bibliometric findings need to be distinguished from clinical interpretation. We aimed to map the bibliometric development of literature explicitly addressing learning, proficiency, experience, and training in robotic transplantation and, secondarily, to interpret the clinical meaning of the mapped evidence across transplant procedures. The Web of Science Core Collection (WoSCC) was searched through August 11, 2026 using a predefined strategy that intersected robotic transplant or donor procedures with learning-related terms. English-language Articles and Review Articles were analyzed with R/Bibliometrix-Biblioshiny and CiteSpace 6.4.R1. Bibliometric findings were interpreted using an author-derived four-domain framework covering technical acquisition, procedural efficiency, clinical stabilization, and program-level implementation/transfer; this framework was not treated as a bibliometrically derived or validated proficiency model. Of 284 retrieved records, 167 publications were included after document-type and language screening. The corpus spanned 2002-2026, 61 sources, 908 authors, and 2,975 cited references; annual growth was 14.35%, mean citations were 17.75 per document, and international coauthorship was 31.14%. Publication growth accelerated after 2018. The United States led corresponding-author output (62 publications) and country citations (1,193), while the University of Florence was the leading affiliation (47 publications). Kidney transplantation was the dominant clinical model: 'kidney transplantation' was the most frequent all-keyword term (50 occurrences), and robot-assisted kidney transplantation formed the largest cited-reference cluster. Later temporal signals included explicit learning curves, donor nephrectomy, training, and living-donor liver transplantation. Bibliometric mapping shows that the learning-explicit robotic transplantation literature is strongly kidney-dominant, with evidence from other organs representing smaller and clinically distinct extensions. On the basis of this mapped literature, we propose - rather than validate - a multidomain interpretation in which technical efficiency, transplant-specific process control, clinical outcomes, and program-level transfer may evolve differently. Case-number thresholds should therefore be reported with their endpoint, baseline expertise, procedure, and case mix, and should not be used as portable credentialing standards without organ-specific and multicenter validation.