Artificial Intelligence in melanoma research: a bibliometric analysis
Clinical Snapshot
PICO Framework
| P — Population | Scientific literature on artificial intelligence applications in melanoma research |
| I — Intervention | Bibliometric analysis using VOSviewer, CiteSpace, and bibliometrix |
| C — Comparator | Not applicable (descriptive bibliometric study) |
| O — Outcomes | Publication trends, geographical distribution, institutional productivity, journal analysis, keyword co-occurrence, research hotspots |
Bottom Line
This bibliometric analysis reveals explosive growth in artificial intelligence applications for melanoma research since 2017, with the United States and China leading publication output. The study identifies two key research hotspots: AI-assisted diagnosis and AI-integrated immunotherapy. For Australian clinicians, this analysis highlights the rapid advancement of AI tools that may soon impact melanoma diagnosis and treatment. The authors recommend focusing future research on diverse datasets, explainable AI systems, and cross-disciplinary collaboration to facilitate clinical translation. Given Australia's high melanoma burden and advanced dermatology research infrastructure, Australian institutions are well-positioned to contribute to and benefit from these emerging AI technologies. However, clinicians should remain aware that bibliometric analysis reflects research activity rather than clinical efficacy, and robust clinical validation remains essential before implementing AI tools in practice.
Key Findings
Effect Size: Not applicable for bibliometric analysis
Primary Outcome: Exponential growth in AI-melanoma publications since 2017, reaching 260 publications in 2024
Nnt Or Sensitivity: 1476 total publications analysed from global literature
Confidence Interval: Not reported
Clinical Application
High feasibility for guiding research priorities and identifying collaboration opportunities Highly relevant given Australia's high melanoma incidence rates and established dermatology AI research programs. Could inform TGA regulatory approaches and guide research funding priorities through NHMRC Melanoma researchers, dermatologists, oncologists, and AI developers working in skin cancer
Abstract
BACKGROUND: As one of the most lethal skin cancers, melanoma has encountered many obstacles in diagnosis and therapy. Artificial intelligence (AI) can help improve early diagnosis, prognosis, and treatment of melanoma. However, there is a lack of detailed and accurate bibliometric analysis of the field. METHODS: All publications were extracted from Web of Science Core Collection based on AI and melanoma terms. Bibliometric analysis was conducted on 1476 articles/reviews by using VOSviewer, CiteSpace, and bibliometrix for co-authorship, citation, keyword, and journal analysis. RESULTS: There was a sudden increase in publications each year after 2017 and reached 260 in 2024. The United States (337 publications) and China (292 publications) ranked top in publication productivity. Germany was the top institution and author country. IEEE Access (57 publications) and Diagnostics (54 publications) were core journals. The two most prominent research hotspots were AI-assisted diagnosis and AI-integrated immunotherapy according to the keyword analysis. CONCLUSION: AI in melanoma research has exploded since 2017. It is recommended that future research focus on various datasets, explainable AI, and cross-disciplinary cooperation to promote the transformation of achievements into clinical practice.
References
- 1.Guo, Y., Huang, X., Chen, F., Ma, J., Lv, Y., Yang, T., Guo, Q., & Sun, Y. (2026). Artificial Intelligence in melanoma research: a bibliometric analysis. International Journal of Surgery. https://doi.org/10.1097/JS9.0000000000003879
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