By consolidating scattered knowledge, the literature review provides a comprehensive understanding of the investigated topic. However, excessive reviews, especially in the booming field of pattern analysis and machine intelligence (PAMI), raise concerns for both researchers and reviewers. In response to these concerns, this Analysis aims to provide a thorough review of reviews in the PAMI field from diverse perspectives. First, large language model-empowered bibliometric indicators are proposed to evaluate literature reviews automatically. To facilitate this, a meta-data database dubbed RiPAMI, and a topic dataset are constructed, which are utilized to obtain statistical characteristics of PAMI reviews. Unlike traditional bibliometric measurements, the proposed article-level indicators provide real-time and field-normalized quantified assessments of reviews without relying on user-defined keywords. Second, based on these indicators, the study presents comparative analyses of different reviews, unveiling the characteristics of publications across various fields, periods, and journals. The newly emerging AI-generated literature reviews are also appraised, and the observed differences suggest that most AI-generated reviews still lag behind human-authored reviews in several aspects. Third, we briefly provide a subjective evaluation of representative PAMI reviews and introduce a paper structure-based typology of literature reviews. This typology may improve the clarity and effectiveness for scholars in reading and writing reviews, while also serving as a guide for AI systems in generating well-organized reviews. Finally, this Analysis offers insights into the current challenges of literature reviews and envisions future directions for their development.

翻译：通过整合分散的知识，文献综述能够提供对所研究主题的全面理解。然而，过多的综述性文章，尤其是在蓬勃发展的模式分析与机器智能（PAMI）领域，引发了研究人员和审稿人的担忧。针对这些问题，本文旨在从多元视角对PAMI领域的综述进行深入回顾。首先，我们提出了基于大语言模型的文献计量指标，用于自动评估文献综述。为此，构建了名为RiPAMI的元数据库及一个主题数据集，以获取PAMI综述的统计特征。与传统文献计量方法不同，本文提出的文章级指标无需依赖用户定义的关键词，即可对综述进行实时且领域归一化的量化评估。其次，基于这些指标，本研究对不同综述进行了比较分析，揭示了跨领域、跨时期及跨期刊的出版特征。本文还对新兴的AI生成文献综述进行了评估，观察到的差异表明，大多数AI生成的综述在多个方面仍落后于人类撰写的综述。第三，我们简要提供了对代表性PAMI综述的主观评价，并引入了一种基于论文结构的文献综述类型学。这一类型学可能有助于学者更清晰有效地阅读和撰写综述，同时为AI系统生成结构良好的综述提供指导。最后，本文对当前文献综述面临的主要挑战进行了分析，并展望了其未来的发展方向。