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A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis

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Published on: March 8, 2022

Artificial Intelligence in melanoma research: a bibliometric analysis.

Yongjun Guo¹, Xiaolou Huang^2,3, Fang Chen⁴

¹Department of Orthopedic Surgery, Huguosi Hospital of Traditional Chinese Medicine Affiliated to Beijing University of Chinese Medicine, Beijing, China.

International Journal of Surgery (London, England)

|November 11, 2025

Summary

Artificial Intelligence (AI) significantly advances melanoma research, particularly in diagnosis and immunotherapy. This field has seen rapid growth since 2017, with key contributions from the US and China.

Keywords:

artificial intelligence bibliometric bibliometrix R citeSpace melanoma vOSviewer

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Area of Science:

Bibliometric analysis of scientific literature.
Focus on Artificial Intelligence (AI) and melanoma research.

Background:

Melanoma is a lethal skin cancer with diagnostic and therapeutic challenges.
Artificial Intelligence (AI) offers potential for improved melanoma early diagnosis, prognosis, and treatment.
A need exists for comprehensive bibliometric analysis in AI and melanoma research.

Purpose of the Study:

To conduct a detailed bibliometric analysis of publications on Artificial Intelligence (AI) in melanoma research.
To identify publication trends, key contributors, core journals, and research hotspots.

Main Methods:

Extraction of 1,476 publications from Web of Science Core Collection using "AI" and "melanoma" keywords.
Bibliometric analysis utilizing VOSviewer, CiteSpace, and bibliometrix software.
Analysis of co-authorship, citation patterns, keywords, and journal impact.

Main Results:

A significant surge in publications occurred after 2017, reaching 260 by 2024.
The United States and China lead in publication output, with Germany as a top contributing institution and country.
Key research areas identified include AI-assisted diagnosis and AI-integrated immunotherapy.

Conclusions:

AI in melanoma research has experienced exponential growth since 2017.
Future research should explore diverse datasets, explainable AI, and interdisciplinary collaboration.
Emphasis on translating AI research findings into clinical practice is recommended.