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

Area of Science:

• Microsurgery
• Surgical Research
• Bibliometrics

Background:

• Microsurgery is a highly advanced surgical field with global impact.
• Current understanding of global microsurgical research productivity and human capital is limited.

Purpose of the Study:

• To quantify global microsurgery research productivity and human capital.
• To identify patterns and disparities in research output across nations.

Main Methods:

• AI-powered bibliometric analysis of 11,561 publications (2010-2024) from 20 journals.
• Automated web scraping and text mining for data extraction.
• National human capital proxied by first author counts, normalized by population and income level.

Main Results:

• Global microsurgery output increased by 59.3% from 2010-2024.
• High-income countries produced 69.5% of publications; the US led in output.
• Taiwan and Switzerland showed highest per capita productivity.

Conclusions:

• Microsurgery research is concentrated in a few high-income countries, with significant regional inequalities.
• An AI-driven framework enables scalable, high-fidelity research analysis.
• AI analytics can guide equitable investment and accelerate global microsurgical innovation.