Artificial Intelligence-Driven Mapping of Global Microsurgery Research: Dual Automation for Data and Classification

Abstract

INTRODUCTION: Microsurgery is one of the most technically demanding and rapidly advancing domains in modern surgery. Despite its transformative role across reconstructive and restorative disciplines, global patterns of microsurgical research productivity and human capital remain poorly defined. METHODS: We performed the first large-scale, artificial intelligence (AI)-powered bibliometric analysis of microsurgery research published between 2010 and 2024 across 20 high-impact journals. Using a double-layer artificial intelligence framework-automated web scraping for large-scale data extraction and text-mining for content classification-we retrieved titles, abstracts, author affiliations, and countries of origin from PubMed. First author counts were used as a proxy for national human capital. Research productivity was contextualized by World Bank income classification and normalized to population size. RESULTS: A total of 11,561 publications and 7533 first authors from 74 countries were identified. Global output increased by 59.3% (611 to 973 publications) from 2010 to 2024, with the steepest growth occurring after 2019. High-income countries produced 69.5% of all publications, followed by upper-middle-income countries (21.8%). The United States dominated global production (33.7% of publications; 34.8% of first authors), followed by Japan, China, the United Kingdom, Taiwan, and South Korea. Taiwan and Switzerland achieved the highest per capita productivity (2.06 and 2.17 publications per 100,000 population, respectively). The global mean productivity was 1.53 publications per first author (range: 1.0-4.75). CONCLUSIONS: This study provides the first comprehensive quantification of microsurgery research global productivity in relation to national human capital, revealing a high concentration of output in a small number of high-income countries and substantial regional disparities. By integrating web scraping with AI-based text mining, we enabled high-fidelity data extraction at scale, establishing a reproducible framework for large-scale research in surgery. AI-enabled analytics have the potential to transform evidence generation, guide equitable research investment, and accelerate the future of global microsurgical innovation.

Ähnliche Arbeiten