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Artificial Intelligence for Literature Reviews: Opportunities and Challenges

Francisco Bolanos, Angelo Salatino, Francesco Osborne, Enrico Motta

TL;DR

This paper surveys how AI is applied to semi-automate systematic literature reviews, focusing on screening and extraction, and evaluates 21 AI-enhanced tools using a framework of 23 general features plus 11 AI-specific features. It documents current trends, limitations (notably outdated NLP methods, usability gaps, and lack of standard evaluation frameworks), and lays out best practices and future directions including LLMs, knowledge graphs, RAG, and living reviews. It also analyzes 11 recent LLM-based tools for literature search and writing, highlighting potential integration into SLR tools. The findings provide a practical guide for researchers selecting tools and for developers aiming to build more trustworthy, transparent, and effective AI-assisted SLR systems.

Abstract

This manuscript presents a comprehensive review of the use of Artificial Intelligence (AI) in Systematic Literature Reviews (SLRs). A SLR is a rigorous and organised methodology that assesses and integrates previous research on a given topic. Numerous tools have been developed to assist and partially automate the SLR process. The increasing role of AI in this field shows great potential in providing more effective support for researchers, moving towards the semi-automatic creation of literature reviews. Our study focuses on how AI techniques are applied in the semi-automation of SLRs, specifically in the screening and extraction phases. We examine 21 leading SLR tools using a framework that combines 23 traditional features with 11 AI features. We also analyse 11 recent tools that leverage large language models for searching the literature and assisting academic writing. Finally, the paper discusses current trends in the field, outlines key research challenges, and suggests directions for future research.

Artificial Intelligence for Literature Reviews: Opportunities and Challenges

TL;DR

This paper surveys how AI is applied to semi-automate systematic literature reviews, focusing on screening and extraction, and evaluates 21 AI-enhanced tools using a framework of 23 general features plus 11 AI-specific features. It documents current trends, limitations (notably outdated NLP methods, usability gaps, and lack of standard evaluation frameworks), and lays out best practices and future directions including LLMs, knowledge graphs, RAG, and living reviews. It also analyzes 11 recent LLM-based tools for literature search and writing, highlighting potential integration into SLR tools. The findings provide a practical guide for researchers selecting tools and for developers aiming to build more trustworthy, transparent, and effective AI-assisted SLR systems.

Abstract

This manuscript presents a comprehensive review of the use of Artificial Intelligence (AI) in Systematic Literature Reviews (SLRs). A SLR is a rigorous and organised methodology that assesses and integrates previous research on a given topic. Numerous tools have been developed to assist and partially automate the SLR process. The increasing role of AI in this field shows great potential in providing more effective support for researchers, moving towards the semi-automatic creation of literature reviews. Our study focuses on how AI techniques are applied in the semi-automation of SLRs, specifically in the screening and extraction phases. We examine 21 leading SLR tools using a framework that combines 23 traditional features with 11 AI features. We also analyse 11 recent tools that leverage large language models for searching the literature and assisting academic writing. Finally, the paper discusses current trends in the field, outlines key research challenges, and suggests directions for future research.
Paper Structure (29 sections, 5 figures, 5 tables)

This paper contains 29 sections, 5 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Methodology
  4. Meta-review of previous surveys
  5. Survey of SLR Tools
  6. Features overview
  7. AI features
  8. General features
  9. Results
  10. The Role of AI in the Screening Phase
  11. The Role of AI in the Extraction Phase
  12. General features
  13. Outstanding SLR tools
  14. Threats to validity
  15. Research Challenges
  16. ...and 14 more sections

Figures (5)

  • Figure 1: PRISMA Diagram of our SLR about AI-enhanced SLR Tools.
  • Figure 2: Examples of interfaces for paper classification.
  • Figure 3: Examples of the tagging process of Colandr. This figure is courtesy of colandrgd.
  • Figure 4: Examples of interactive interfaces for pre-screening.
  • Figure 5: Examples of interactive interface for the post-screening.