This study presents a comprehensive approach that addresses the challenges of scientometric analysis in the rapidly evolving field of Artificial Intelligence (AI). By combining search terms related to AI with the advanced language processing capabilities of generative pre-trained transformers (GPT), we developed a highly accurate method for identifying and analyzing AI-related articles in the Web of Science (WoS) database. Our multi-step approach included filtering articles based on WoS citation topics, category, keyword screening, and GPT classification. We evaluated the effectiveness of our method through precision and recall calculations, finding that our combined approach captured around 94% of AI-related articles in the entire WoS corpus with a precision of 90%. Following this, we analyzed the publication volume trends, revealing a continuous growth pattern from 2013 to 2022 and an increasing degree of interdisciplinarity. We conducted citation analysis on the top countries and institutions and identified common research themes using keyword analysis and GPT. This study demonstrates the potential of our approach to facilitate accurate scientometric analysis, by providing insights into the growth, interdisciplinary nature, and key players in the field.

翻译：本研究提出一种综合方法，旨在解决快速发展的人工智能（AI）领域中科学计量分析面临的挑战。通过将AI相关搜索词与生成式预训练Transformer（GPT）的高级语言处理能力相结合，我们开发出一种高精度方法，用于识别和分析Web of Science（WoS）数据库中的AI相关文献。我们的多步骤方法包括基于WoS引用主题、类别、关键词筛选及GPT分类进行文献过滤。通过精确率和召回率计算评估方法有效性，发现该综合方法在WoS全部文献中捕获约94%的AI相关文章，精确率达90%。在此基础上，我们分析了出版数量趋势，揭示出2013至2022年持续增长的模式以及日益增强的跨学科性。我们针对主要国家和机构进行引文分析，并利用关键词分析和GPT识别共同研究主题。本研究通过揭示该领域的增长态势、跨学科特性及核心参与者，展示了该方法在促进精准科学计量分析方面的潜力。