Bibliometric methods were used to analyze publications on the use of artificial intelligence (AI) in skin cancer from 2010 to 2022, aiming to explore current publication trends and future directions. A comprehensive search using four terms, “artificial intelligence,” “machine learning,” “deep learning,” and “skin cancer,” was performed in the Web of Science database for original English language publications on AI in skin cancer from 2010 to 2022. We visually analyzed publication, citation, and coupling information, focusing on authors, countries and regions, publishing journals, institutions, and core keywords.
The analysis of 989 publications revealed a consistent year-on-year increase in publications from 2010 to 2022 (0.51% versus 33.57%). The United States, India, and China emerged as the leading contributors. IEEE Access was identified as the most prolific journal in this area. Key journals and influential authors were highlighted. Examination of the top 10 most cited publications highlights the significant potential of AI in oncology. Co-citation network analysis identified four primary categories of classical literature on AI in skin tumors. Keyword analysis indicated that "melanoma," "classification," and "deep learning" were the most prevalent keywords, suggesting that deep learning for melanoma diagnosis and grading is the current research focus. The term “pigmented skin lesions” showed the strongest burst and longest duration, whereas “texture” was the latest emerging keyword. AI represents a rapidly growing area of research in skin cancer with the potential to significantly improve skin cancer management. Future research will likely focus on machine learning and deep learning technologies for screening and diagnostic purposes.
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