A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence

TL;DR

This paper conducts a comprehensive analysis of literature reviews in Pattern Analysis and Machine Intelligence, revealing organizational patterns, gaps, and evaluating AI-generated reviews to improve future review practices.

Contribution

It introduces RiPAMI, a large database of reviews, and develops strategies for navigating reviews and assessing AI-generated review quality.

Findings

Identified structural regularities and gaps in existing reviews.

Developed indicator-guided navigation strategies.

Evaluated AI-generated reviews, noting strengths and weaknesses.

Abstract

The rapid growth of research in Pattern Analysis and Machine Intelligence (PAMI) has rendered literature reviews essential for consolidating and interpreting knowledge across its many subfields. In this work, we present a comprehensive tertiary analysis of PAMI reviews along three complementary dimensions: (i) identifying structural and statistical regularities in existing surveys; (ii) developing quantitative strategies that help researchers navigate and prioritize within the expanding review corpus; and (iii) critically assessing emerging AI-generated review systems. To support this study, we construct RiPAMI, a large-scale database containing more than 3,000 review articles, and combine narrative synthesis with statistical analysis to capture structural and content-level features. Our analyses reveal distinctive organizational patterns as well as persistent gaps in current review practices. Building on these insights, we propose practical, article-level strategies for indicator-guided navigation that move beyond simple citation counts. Finally, our evaluation of state-of-the-art AI-generated reviews indicates encouraging advances in coherence and organization, yet also highlights enduring weaknesses in reference retrieval, coverage of recent work, and the incorporation of visual elements. Together, these findings provide both a critical appraisal of existing review practices and a forward-looking perspective on how AI-generated reviews can evolve into trustworthy, customizable, and transformative complements to traditional human-authored surveys.