Li Xue, Zhang Wenzheng, Fang Jiliang, Li Chunzhi, Lin Hongsheng, Xu Yun, Yang Yufei, Wang Xueqian
Department of Oncology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Graduate School, Beijing University of Chinese Medicine, Beijing, China.
Int J Surg. 2025 Jul 15. doi: 10.1097/JS9.0000000000003059.
Timely detection and intervention for pulmonary nodules play a vital role in decreasing lung cancer-related deaths. Nevertheless, the precise differentiation between benign and malignant nodules continues to face a major clinical challenge. With the rapid progress of artificial intelligence (AI), significant improvements have been made in the detection, classification, and clinical decision-making related to pulmonary nodules. Although scholarly interest in this domain has surged in recent years, there is still a lack of comprehensive bibliometric studies that systematically map its current landscape and evolution. This study seeks to explore emerging research trends, highlight thematic focus areas, and analyze patterns of collaboration within the field of AI-assisted pulmonary nodule research over the past 20 years.
A literature search was conducted in the Web of Science Core Collection to collect relevant studies published from 2005 to 2024 concerning the application of AI in pulmonary nodules. Bibliometric analysis was carried out using tools such as CiteSpace, VOSviewer, and the Online Analysis Platform of Literature Metrology to examine contributions from countries, institutions, authors, journals, keywords, and references.
A total of 1,657 relevant publications were retrieved, reflecting a consistent upward trend in research output over the past two decades, with a marked acceleration observed after 2014. The leading contributors in terms of publication volume were China, the United States, and India. Shanghai Jiao Tong University stood out as the most prolific research institution. Analysis of keyword co-occurrence revealed several prominent thematic clusters, notably centered around Deep Convolutional Neural Network models, major diameter, lung nodule detection, false-positive reduction, cancer diagnosis, quantitative-semantic models, double reading, and clinical utility studies.
This bibliometric study offers a thorough assessment of the scholarly landscape concerning AI applications in pulmonary nodule research, underscoring major developments and key contributors. The insights gained may serve as a strategic reference for researchers in the medical and AI fields, facilitating informed future directions. Notably, the intersection of AI and pulmonary nodule research is concentrated in the following areas: 1. Application of AI in pulmonary nodule detection and classification; 2. AI in malignancy risk prediction and growth modeling; 3.AI-driven development of drug efficacy evaluation metrics may be a future direction for pulmonary nodule treatment research.
肺结节的及时检测与干预对于降低肺癌相关死亡率起着至关重要的作用。然而，良性与恶性结节的精确鉴别仍然面临重大临床挑战。随着人工智能（AI）的迅速发展，在肺结节的检测、分类及临床决策方面已取得显著进展。尽管近年来该领域的学术关注度激增，但仍缺乏系统描绘其当前格局与发展演变的综合文献计量学研究。本研究旨在探讨过去20年中人工智能辅助肺结节研究领域的新兴研究趋势，突出主题重点领域，并分析合作模式。
在科学网核心合集进行文献检索，以收集2005年至2024年发表的有关人工智能在肺结节中应用的相关研究。使用CiteSpace、VOSviewer和文献计量在线分析平台等工具进行文献计量分析，以考察国家、机构、作者、期刊、关键词及参考文献的贡献。
共检索到1657篇相关出版物，反映出过去二十年研究产出呈持续上升趋势，2014年后增速明显加快。发文量领先的国家是中国、美国和印度。上海交通大学是最多产的研究机构。关键词共现分析揭示了几个突出的主题聚类，尤其围绕深度卷积神经网络模型、最大直径、肺结节检测、假阳性减少、癌症诊断、定量语义模型、双重读片及临床效用研究。
本文献计量学研究全面评估了人工智能在肺结节研究中的学术格局，强调了主要进展和关键贡献者。所得见解可为医学和人工智能领域的研究人员提供战略参考，助力未来做出明智的研究方向决策。值得注意的是，人工智能与肺结节研究的交叉集中在以下领域：1. 人工智能在肺结节检测与分类中的应用；2. 人工智能在恶性风险预测与生长建模中的应用；3. 人工智能驱动的药物疗效评估指标发展可能是肺结节治疗研究的未来方向。
