In a world that has rapidly transformed through the advent of artificial intelligence (AI), our systematic review, guided by the PRISMA protocol, investigates a decade of AI research, revealing insights into its evolution and impact. Our study, examining 3,767 articles, has drawn considerable attention, as evidenced by an impressive 63,577 citations, underscoring the scholarly community's profound engagement. Our study reveals a collaborative landscape with 18,189 contributing authors, reflecting a robust network of researchers advancing AI and machine learning applications. Review categories focus on systematic reviews and bibliometric analyses, indicating an increasing emphasis on comprehensive literature synthesis and quantitative analysis. The findings also suggest an opportunity to explore emerging methodologies such as topic modeling and meta-analysis. We dissect the state of the art presented in these reviews, finding themes throughout the broad scholarly discourse through thematic clustering and BERTopic modeling. Categorization of study articles across fields of research indicates dominance in , followed by . Subject categories reveal interconnected clusters across various sectors, notably in healthcare, engineering, business intelligence, and computational technologies. Semantic analysis via BERTopic revealed nineteen clusters mapped to themes such as and . Future research directions are suggested, emphasizing the need for intersectional bias mitigation, holistic health approaches, AI's role in environmental sustainability, and the ethical deployment of generative AI.

中文翻译：

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揭示人工智能应用的动态：使用科学计量学和 BERTopic 建模进行评论回顾


在人工智能 (AI) 的出现迅速改变的世界中，我们在 PRISMA 协议的指导下进行系统回顾，调查了十年来的人工智能研究，揭示了对其演变和影响的见解。我们的研究审查了 3,767 篇文章，引起了相当大的关注，令人印象深刻的 63,577 次引用证明了这一点，凸显了学术界的深刻参与。我们的研究揭示了 18,189 名贡献作者的合作格局，反映了推进人工智能和机器学习应用的强大研究人员网络。综述类别侧重于系统综述和文献计量分析，表明越来越重视综合文献综合和定量分析。研究结果还表明有机会探索主题建模和荟萃分析等新兴方法。我们剖析了这些评论中呈现的最新技术，通过主题聚类和 BERTopic 建模在广泛的学术讨论中寻找主题。跨研究领域的研究文章的分类表明， 占主导地位，其次是 。学科类别揭示了各个领域的相互关联的集群，特别是在医疗保健、工程、商业智能和计算技术领域。通过 BERTopic 进行的语义分析揭示了映射到 和 等主题的 19 个簇。提出了未来的研究方向，强调缓解交叉偏见、整体健康方法、人工智能在环境可持续性中的作用以及生成人工智能的道德部署的必要性。