Abstract Artificial intelligence (AI) is fundamentally transforming the landscape of plastic surgery, yet the structural architecture of its most influential scholarship has not been systematically characterized. This study presents a bibliometric and visual analysis of the 100 most-cited English-language AI publications in plastic surgery. Scopus was searched from database inception through January 15th, 2026. Eligible articles underwent dual independent screening in Covidence, and the 100 most-cited publications were analyzed using R (v4.4.1) and VOSviewer (v1.6.18) for citation metrics, authorship networks, geographic and institutional contributions, journal distribution, and thematic categorization. Of the 3,827 retrieved records, 357 met the full inclusion criteria, and the top 100 were identified. These articles collectively received 2,701 citations (average: 27.01±22.19), with a marked post-2022 publication surge accounting for 79% of the studies, with 2024 contributing to 34% of that share. Patient education and large language models-based consultation constituted the dominant thematic cluster (35%), followed by ethical and governance considerations (17%) and outcomes prediction and risk modeling (13%). Aesthetic surgery represented the most prolific specialty (34%), with craniofacial and breast reconstruction each contributing to 21%. Aesthetic Plastic Surgery (n = 16) and the Aesthetic Surgery Journal (n = 10) led journal representation. The United States dominated global scholarly output, while Peninsula Health in Australia emerged as the most impactful institution, with Rozen, WM, and Seth, I jointly leading the analysis as top authors. This analysis demonstrates the intellectual topography of AI scholarship in plastic surgery, identifying prevailing thematic concentrations and underexplored domains, and offering a contemporary framework for future research priorities and international collaboration.
Mokhtar et al. (Fri,) studied this question.