Automating Systematic Literature Reviews with Retrieval-Augmented Generation: A Comprehensive Overview
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This study examines Retrieval-Augmented Generation (RAG) in large language models (LLMs) and their significant application for undertaking systematic literature reviews (SLRs). RAG-based LLMs can potentially automate tasks like data extraction, summarization, and trend identification. However, while LLMs are exceptionally proficient in generating human-like text and interpreting complex linguistic nuances, their dependence on static, pre-trained knowledge can result in inaccuracies and hallucinations. RAG mitigates these limitations by integrating LLMs’ generative capabilities with the precision of real-time information retrieval. We review in detail the three key processes of the RAG framework—retrieval, augmentation, and generation. We then discuss applications of RAG-based LLMs to SLR automation and highlight future research topics, including integration of domain-specific LLMs, multimodal data processing and generation, and utilization of multiple retrieval sources. We propose a framework of RAG-based LLMs for automating SRLs, which covers four stages of SLR process: literature search, literature screening, data extraction, and information synthesis. Future research aims to optimize the interaction between LLM selection, training strategies, RAG techniques, and prompt engineering to implement the proposed framework, with particular emphasis on the retrieval of information from individual scientific papers and the integration of these data to produce outputs addressing various aspects such as current status, existing gaps, and emerging trends.
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RAG facilitates dynamic literature review by retrieving and summarizing recent, domain-relevant publications in response to specific clinical or research queries.
“RAGfacilitates dynamic literature review by retrieving and summarizing recent, domainrelevant publications in response to specific clinical or research queries [67].”
From Retrieval-Augmented Generation (RAG) in Healthcare: A Comprehensive Review (2025) · cites this paper as [67]
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Organização e Arquitetura da Informação em Large Language Models
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- Authors
- Han, B., Susnjak, T., & Mathrani, A.
- Affiliations
- Massey University (NZ)
- School of Mathematical and Computational Sciences, Massey University, Auckland 0632, New Zealand
- Corresponding author
- Mathrani, Anuradha
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2024-10-09
- Volume
- 14
- Issue
- 19
- Pages
- 9103
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W4403248625
- Cited by
- 68
- References
- 36
- Citations by year
- 2026: 38 · 2025: 30
- FWCI
- 2.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- retrieval-augmented generation, systematic reviews, large language models, data extraction, literature screening, information synthesis, hallucinations, prompt engineering, multimodal processing, literature searching
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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