Bibliometric analysis of ChatGPT and plastic surgery research: Insights from diverse search strategies and co-word analysis

Abstract

Background

The rise of artificial intelligence in healthcare, particularly the development of large language models like ChatGPT, has opened new avenues for innovation in medical fields, including plastic surgery. ChatGPT offers potential applications in patient education, surgical planning, and decision-making support, making it an important research subject. However, there has been limited investigation into its impact on plastic surgery. The objective of this study was to investigate the progress of research on ChatGPT and plastic surgery, focusing on key contributors and emerging topics within the field.

Methods

Five distinct search strategies were employed to analyze relevant publications from the Scopus database.

Results

The analysis identified and presented the top authors, universities, countries, sponsors, and journals (within each search strategy). The co-authorship networks of authors, universities, and countries are graphically presented. The authors’ performance was depicted by various indicators, such as total publications, citations, h-index, g-index, and m-index. A co-word analysis revealed the focus of the papers, which were presented in 15 groups. This multifaceted approach provides a detailed understanding of key themes in the field.

Conclusion

This report offers a comprehensive overview of the current state of research at the intersection of ChatGPT and plastic surgery.