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<p>Molecular imaging-guided robotic-assisted surgery (<i>MIRS</i>) represents a transformative convergence of imaging, robotics, and computational intelligence. Although the field is rapidly advancing, global research patterns and thematic progression remain insufficiently characterized. This study provides a comprehensive bibliometric evaluation to delineate the knowledge structure, conceptual transitions, and emerging research frontiers within <i>MIRS</i>. Total of 2984 Scopus-indexed documents (1982–2025) were analyzed using Bibliometrix and VOSviewer. Quantitative indicators, Lotka’s and Bradford’s laws, co-authorship networks, keyword co-occurrence, and Callon’s centrality–density mapping were applied to identify collaboration patterns, core publication sources, influential authors, and thematic evolution. The <i>MIRS</i> literature showed exponential growth (15.18% annual rate), with 55.2% of all publications produced between 2021–2025. The United States led in productivity (1056 documents; 24,909 citations), followed by China, the United Kingdom, Italy, and Germany, collectively accounting for 51.6% of total citations. Lotka’s Law confirmed a highly skewed authorship pattern, with &lt; 1% of researchers producing more than ten papers. Bradford’s Law identified 20–30 core journals, including the Journal of Robotic Surgery and International Journal of Surgery. Keyword networks emphasized dominant themes—robotic surgery, image-guided surgery, indocyanine green—and highlighted emerging integration of artificial intelligence and deep learning. Collaboration analysis revealed eight major clusters, largely Western-centric. Thematic mapping demonstrated a shift from foundational robotic and imaging techniques toward computationally enhanced, semi-autonomous surgical systems. This study underscores the rapid maturation, technological convergence, and expanding interdisciplinarity of <i>MIRS</i>. The progression toward AI-augmented, data-driven precision surgery signals an upcoming paradigm shift, guiding future research toward automation, intelligent navigation, and more equitable global collaboration.</p>

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Global bibliometric analysis of molecular imaging-guided robotic-assisted surgery: publication dynamics, thematic evolution, authorship patterns, and disciplinary landscape development (1982–2025)

  • 期刊论文

  • 发表日期：

    2025年12月11日

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  • Manal Mohamed Elhassan Taha,
  • Siddig Ibrahim Abdelwahab,
  • Khaled A. Sahli,
  • Ahmad Assiri,
  • Abdullah Farasani,
  • Marwa Qadri,
  • Abdulaziz Alarifi,
  • Amani Khardali,
  • Jobran M. Moshi,
  • Saeed Alshahrani,
  • Hussam M. Shubaily

摘要

Molecular imaging-guided robotic-assisted surgery (MIRS) represents a transformative convergence of imaging, robotics, and computational intelligence. Although the field is rapidly advancing, global research patterns and thematic progression remain insufficiently characterized. This study provides a comprehensive bibliometric evaluation to delineate the knowledge structure, conceptual transitions, and emerging research frontiers within MIRS. Total of 2984 Scopus-indexed documents (1982–2025) were analyzed using Bibliometrix and VOSviewer. Quantitative indicators, Lotka’s and Bradford’s laws, co-authorship networks, keyword co-occurrence, and Callon’s centrality–density mapping were applied to identify collaboration patterns, core publication sources, influential authors, and thematic evolution. The MIRS literature showed exponential growth (15.18% annual rate), with 55.2% of all publications produced between 2021–2025. The United States led in productivity (1056 documents; 24,909 citations), followed by China, the United Kingdom, Italy, and Germany, collectively accounting for 51.6% of total citations. Lotka’s Law confirmed a highly skewed authorship pattern, with < 1% of researchers producing more than ten papers. Bradford’s Law identified 20–30 core journals, including the Journal of Robotic Surgery and International Journal of Surgery. Keyword networks emphasized dominant themes—robotic surgery, image-guided surgery, indocyanine green—and highlighted emerging integration of artificial intelligence and deep learning. Collaboration analysis revealed eight major clusters, largely Western-centric. Thematic mapping demonstrated a shift from foundational robotic and imaging techniques toward computationally enhanced, semi-autonomous surgical systems. This study underscores the rapid maturation, technological convergence, and expanding interdisciplinarity of MIRS. The progression toward AI-augmented, data-driven precision surgery signals an upcoming paradigm shift, guiding future research toward automation, intelligent navigation, and more equitable global collaboration.