The rapid advancements in Pattern Analysis and Machine Intelligence (PAMI) have led to an overwhelming expansion of scientific knowledge, spawning numerous literature reviews aimed at collecting and synthesizing fragmented information. This paper presents a thorough analysis of these literature reviews within the PAMI field, and tries to address three core research questions: (1) What are the prevalent structural and statistical characteristics of PAMI literature reviews? (2) What strategies can researchers employ to efficiently navigate the growing corpus of reviews? (3) What are the advantages and limitations of AI-generated reviews compared to human-authored ones? To address the first research question, we begin with a narrative overview to highlight common preferences in composing PAMI reviews, followed by a statistical analysis to quantitatively uncover patterns in these preferences. Our findings reveal several key insights. First, fewer than 20% of PAMI reviews currently comply with PRISMA standards, although this proportion is gradually increasing. Second, there is a moderate positive correlation between the quality of references and the scholarly impact of reviews, emphasizing the importance of reference selection. To further assist researchers in efficiently managing the rapidly growing number of literature reviews, we introduce four novel, real-time, article-level bibliometric indicators that facilitate the screening of numerous reviews. Finally, our comparative analysis reveals that AI-generated reviews currently fall short of human-authored ones in accurately evaluating the academic significance of newly published articles and integrating rich visual elements, which limits their practical utility. Overall, this study provides a deeper understanding of PAMI literature reviews by uncovering key trends, evaluating current practices, and highlighting areas for future improvement.

翻译：模式分析与机器智能（PAMI）领域的快速发展导致了科学知识的急剧膨胀，催生了大量旨在收集和整合碎片化信息的文献综述。本文对该领域内的文献综述进行了全面分析，并试图回答三个核心研究问题：（1）PAMI文献综述普遍的结构与统计特征是什么？（2）研究者可以采取哪些策略来高效驾驭日益增长的综述文献库？（3）与人工撰写的综述相比，AI生成的综述有何优势与局限？针对第一个研究问题，我们首先通过叙述性概述来阐明撰写PAMI综述时的常见偏好，随后进行统计分析以定量揭示这些偏好的模式。我们的发现揭示了若干关键见解。首先，目前仅有不到20%的PAMI综述符合PRISMA标准，尽管这一比例正在逐步上升。其次，参考文献的质量与综述的学术影响力之间存在中等程度的正相关，这强调了参考文献选择的重要性。为了进一步帮助研究者高效管理快速增长的大量文献综述，我们引入了四个新颖的、实时的、文章级别的文献计量指标，以促进对众多综述的筛选。最后，我们的比较分析表明，AI生成的综述目前在准确评估新发表文章的学术意义以及整合丰富的视觉元素方面尚不及人工撰写的综述，这限制了其实际应用价值。总体而言，本研究通过揭示关键趋势、评估当前实践并指出未来改进方向，为深入理解PAMI文献综述提供了新的视角。