ReviewFrontiers in oncology2026
Global trends and academic landscapes of AI applications in basal cell carcinoma research: a bibliometric analysis.
Review in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.