ArticleJournal of robotic surgery2026
Mapping the integration of artificial intelligence in knee replacement surgery: a data-driven bibliometric analysis with emphasis on robotic innovation.
Article in Journal of robotic surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Artificial intelligence (AI) is increasingly reshaping the landscape of orthopedic surgery, with notable applications in knee replacement surgery (KRS), particularly in robotic-assisted interventions. AI-driven tools are enhancing preoperative planning, intraoperative precision, and postoperative outcome prediction. Despite growing literature, no comprehensive bibliometric evaluation has mapped the evolution, collaborative structures, and thematic focus of AI in KRS—especially regarding robotic technologies. A bibliometric analysis was conducted using the Scopus database, including English-language original research articles published up to March 22, 2025. The search combined AI-related keywords with “knee replacement” and “knee arthroplasty.” Bibliometric tools—VOSviewer and Bibliometrix—were employed to explore publication trends, prolific authors, global collaboration, conceptual structures, and thematic evolution, with special attention to robotic applications. The final dataset comprised 4,216 articles, with an annual growth rate of 13.17%, peaking in 2024. The United States led in output, followed by Japan and the UK. Bradford’s Law revealed six core journals, including The Journal of Arthroplasty and Knee Surgery, Sports Traumatology, Arthroscopy. Lotka’s Law confirmed a highly dispersed authorship, with Mont MA as the most productive author. Robust collaborations were observed in North America and Europe, with increasing contributions from Asia. Conceptual mapping identified “total knee arthroplasty” and “machine learning” as core themes, while robotics, personalized surgical alignment, and predictive analytics emerged as dominant research frontiers. This study provides the first bibliometric map of AI integration in KRS, highlighting global contributions, conceptual trends, and the rising prominence of robotic-assisted technologies. These findings offer actionable insights for orthopedic surgeons, clinical researchers, and innovators aiming to harness AI and robotics to advance knee arthroplasty practices.
Indexed as
Identifiers
41554979What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.