Li Y, Yanping Bai, Lina Asihaer
Background: Basal cell carcinoma (BCC), one of the most prevalent skin cancers, still faces substantial challenges in timely diagnosis and optimal management. Artificial intelligence (AI) holds promise for improving early detection, risk stratification, and treatment decision-making in BCC. However, detailed and comprehensive bibliometric analyses in this field remain scarce. Methods: Publications related to AI and BCC were retrieved from the Web of Science Core Collection, Scopus, and Embase using predefined keyword strategies. All relevant records were exported, and 226 publications were ultimately included for analysis after screening and deduplication. Bibliometric analyses were performed using VOSviewer, CiteSpace, and the bibliometrix R package to characterize co-authorship networks, citations, keyword co-occurrence patterns, and journal distributions. Results: Annual publication output increased markedly after 2019, reaching 42 publications in 2025. The United States (43 publications) and China (36 publications) were the most productive countries, with the United States also hosting many of the leading institutions and authors. According to Bradford's law of scattering, 13 core journals were identified; among them, Diagnostics (9 publications) and Skin Research and Technology (8 publications) had the highest output. Keyword analyses indicated that research hotspots center on deep learning-driven dermoscopic and digital pathology image analysis, primarily for classification and segmentation in computer-aided diagnosis of BCC. Conclusion: AI research in BCC has expanded rapidly since 2019. Future studies should prioritize multicenter, cross-device, and cross-population validation of multimodal AI systems and their integration into routine clinical practice to improve early detection and overall management of BCC.