AI trends, collaboration, and gaps in dermatology: a bibliometric analysis from 2004 to 2022

Abstract

BACKGROUND: Despite advances in artificial intelligence (AI), its complexity and integration into healthcare remain significant challenges. Current literature lacks clarity on which non-medical disciplines should be engaged in applying AI to dermatology, and which dermatologic diseases remain underexplored. This bibliometric analysis aims to identify impactful interdepartmental collaborations and highlight gaps in AI dermatology research.METHODS: We conducted a bibliometric analysis of AI-related dermatology publications from 2004 to 2022 using PubMed. A total of 85 articles were included. Microsoft Excel was used to evaluate collaboration patterns, publication trends, and citation metrics.RESULTS: There has been a notable rise in AI-dermatology publications over the past decade, with North American and European institutions contributing most prominently. Articles with interdepartmental collaboration received more citations than single-discipline efforts. Common collaborators with medicine included biomedical data science, biomedical informatics, biomedical engineering, bioengineering, and applied mathematics. While the application of AI in dermatology has expanded beyond skin cancer, several conditions - such as vitiligo, morphea, and rare genodermatoses - remain largely unexplored.CONCLUSIONS: These findings may guide students and researchers in identifying leading institutions, authors, and potential collaborators from outside traditional medical fields. This paper also serves as a reference for dermatologic conditions not yet studied with AI. Future work should prioritize underrepresented skin diseases and foster cross-disciplinary partnerships to advance innovation in dermatologic care.

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