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.

对模式分析与机器智能领域的文献综述进行了彻底的综述，在提出大型语言模型为基础的文献计量指标用于自动评估综述的同时，对不同综述进行了对比分析，并评估了人工智能生成的综述与人工综述的差异，同时还介绍了基于文献结构的综述分类方法，为学者阅读和撰写综述提供了指导，为人工智能系统生成有组织的综述提供了参考，同时还展望了综述发展的未来方向。