Zheng RJ, Li DL, Lin HM, Wang JF, Luo YM, Tang Y, Li F, Hu Y, Su S. Bibliometrics of artificial intelligence applications in hepatobiliary surgery from 2014 to 2024. World J Gastrointest Surg 2025; 17(5): 104728 [PMID: 40502490 DOI: 10.4240/wjgs.v17.i5.104728]
Reader's ID:
02905103
Submitted on:
June 02, 2025, 11:11
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Reader Comments:
This bibliometric study systematically examines artificial intelligence applications in hepatobiliary surgery from 2014-2024, offering a quantitative perspective on this evolving field. The research effectively maps global publication patterns, collaborative networks, and research priorities. The title accurately defines the scope, while the abstract concisely summarizes objectives, methodology, and key findings. Keywords demonstrate appropriate thematic alignment, and the background section adequately contextualizes AI's integration into hepatobiliary practice, noting its clinical relevance for diagnostic and therapeutic applications.
Methodologically, the study employs Web of Science data with visualization tools (CiteSpace/VOSviewer) to analyze authorship, institutional contributions, national productivity, and keyword evolution. However, the search strategy shows limitations through its predominant focus on "CNN" (Convolutional Neural Networks), overlooking emerging architectures like Transformer models that are gaining prominence in medical AI research. Results clearly demonstrate China's leadership in publication volume alongside the United States' influence in citation impact, with network visualizations effectively illustrating these geographical patterns. The discussion reasonably outlines AI's role in imaging diagnostics and tumor segmentation, though greater analytical depth regarding clinical implementation challenges - such as model validation requirements and integration with surgical workflows - would strengthen its practical relevance.
Presentation issues require attention as Figure 3 suffers from resolution limitations affecting text legibility, while Figures 10-11 contain non-essential graphical elements that reduce interpretive clarity. Strategic labeling could enhance data communication in these visualizations. Statistical approaches are appropriately applied for bibliometric analysis, and referenced literature shows generally authoritative coverage. Minor language issues exist, including inconsistent quotation usage in the abstract (e.g., "classification", "CT and "diagnosis") and occasional terminology variations, though these do not substantially hinder comprehension.
Collectively, this work provides a structured, data-informed overview of AI research in hepatobiliary surgery during the specified decade. It documents the field's development trajectory, identifies primary research themes and key contributors, and establishes baseline patterns of international collaboration. The analysis offers researchers a useful reference point for understanding current research landscapes and potential partnership opportunities. With refinements addressing methodological scope, analytical depth, and visual presentation, the study could serve as a more comprehensive benchmark for future investigations in this domain.
Reply from the Editorial Office:
First, thank you very much for your professional comments on the article published in World Journal of Gastrointestinal Surgery.
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