# Artificial Intelligence for Literature Reviews: Opportunities and Challenges

arXiv: https://arxiv.org/abs/2402.08565 · HTML: https://mlsanity.com/paper/2402.08565/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 237 references that are in the map.
- First posted on arXiv 13 Feb 2024.

## Builds on (most cited first, 30 of 39)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Graph of Thoughts: Solving Elaborate Problems with Large Language Models](https://mlsanity.com/paper/2308.09687/) (2023)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [Large Language Model Guided Tree-of-Thought](https://mlsanity.com/paper/2305.08291/) (2023)
- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019)
- [BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval](https://mlsanity.com/paper/2105.07921/) (2021)
- [Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2303.13948/) (2023)
- [Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references](https://mlsanity.com/paper/1402.4578/) (2014)
- [Structured information extraction from complex scientific text with fine-tuned large language models](https://mlsanity.com/paper/2212.05238/) (2022)
- [Scientific Fact-Checking: A Survey of Resources and Approaches](https://mlsanity.com/paper/2305.16859/) (2023)
- [Open Source Software for Efficient and Transparent Reviews](https://mlsanity.com/paper/2006.12166/) (2020)
- [Investigating and Designing for Trust in AI-powered Code Generation Tools](https://mlsanity.com/paper/2305.11248/) (2023)
- [A Computational Inflection for Scientific Discovery](https://mlsanity.com/paper/2205.02007/) (2022)
- [AGATHA: Automatic Graph-mining And Transformer based Hypothesis generation Approach](https://mlsanity.com/paper/2002.05635/) (2020)
- [Successful Combination of Database Search and Snowballing for Identification of Primary Studies in Systematic Literature Studies](https://mlsanity.com/paper/2307.02612/) (2023)
- [A Survey on Text Simplification](https://mlsanity.com/paper/2008.08612/) (2020)
- [A Survey on Deep Learning Event Extraction: Approaches and Applications](https://mlsanity.com/paper/2107.02126/) (2021)
- [Large-scale comparison of bibliographic data sources: Scopus, Web of Science, Dimensions, Crossref, and Microsoft Academic](https://mlsanity.com/paper/2005.10732/) (2020)
- [Artificial intelligence to automate the systematic review of scientific literature](https://mlsanity.com/paper/2401.10917/) (2024)

## Built on by (most cited first, 30 of 30)

- [Automating Research Synthesis with Domain-Specific Large Language Model Fine-Tuning](https://mlsanity.com/paper/2404.08680/) (2024)
- [Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models](https://mlsanity.com/paper/2501.05382/) (2025)
- [Toward Automated Simulation Research Workflow through LLM Prompt Engineering Design](https://mlsanity.com/paper/2408.15512/) (2024)
- [Large Language Models for Scholarly Ontology Generation: An Extensive Analysis in the Engineering Field](https://mlsanity.com/paper/2412.08258/) (2024)
- [A Survey on Knowledge Organization Systems of Research Fields: Resources and Challenges](https://mlsanity.com/paper/2409.04432/) (2024)
- [Cutting Through the Clutter: The Potential of LLMs for Efficient Filtration in Systematic Literature Reviews](https://mlsanity.com/paper/2407.10652/) (2024)
- [A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences](https://mlsanity.com/paper/2412.03531/) (2024)
- [Optimizing Large Language Models for ESG Activity Detection in Financial Texts](https://mlsanity.com/paper/2502.21112/) (2025)
- [PROMPTHEUS: A Human-Centered Pipeline to Streamline SLRs with LLMs](https://mlsanity.com/paper/2410.15978/) (2024)
- [Divided by discipline? A systematic literature review on the quantification of online sexism and misogyny using a semi-automated approach](https://mlsanity.com/paper/2409.20204/) (2024)
- [Modelling and Classifying the Components of a Literature Review](https://mlsanity.com/paper/2508.04337/) (2025)
- [Leveraging Large Language Models for Generating Research Topic Ontologies: A Multi-Disciplinary Study](https://mlsanity.com/paper/2508.20693/) (2025)
- [L-PRISMA: An Extension of PRISMA in the Era of Generative Artificial Intelligence (GenAI)](https://mlsanity.com/paper/2603.19236/) (2026)
- [The use of GPT-4o and Other Large Language Models for the Improvement and Design of Self-Assessment Scales for Measurement of Interpersonal Communication Skills](https://mlsanity.com/paper/2409.14050/) (2024)
- [Leveraging LLMs for Semi-Automatic Corpus Filtration in Systematic Literature Reviews](https://mlsanity.com/paper/2510.11409/) (2025)
- [From Verification Burden to Trusted Collaboration: Design Goals for LLM-Assisted Literature Reviews](https://mlsanity.com/paper/2512.11661/) (2025)
- [AwesomeLit: Towards Hypothesis Generation with Agent-Supported Literature Research](https://mlsanity.com/paper/2603.22648/) (2026)
- [CEKER: A Generalizable LLM Framework for Literature Analysis with a Case Study in Unikernel Security](https://mlsanity.com/paper/2412.10904/) (2024)
- [A Comparative Study of Task Adaptation Techniques of Large Language Models for Identifying Sustainable Development Goals](https://mlsanity.com/paper/2506.15208/) (2025)
- [HySemRAG: A Hybrid Semantic Retrieval-Augmented Generation Framework for Automated Literature Synthesis and Methodological Gap Analysis](https://mlsanity.com/paper/2508.05666/) (2025)
- [Hallucination vs interpretation: rethinking accuracy and precision in AI-assisted data extraction for knowledge synthesis](https://mlsanity.com/paper/2508.09458/) (2025)
- [Charting the Future of Scholarly Knowledge with AI: A Community Perspective](https://mlsanity.com/paper/2509.02581/) (2025)
- [Conversational No-code, Multi-agentic Disease Module Identification and Drug Repurposing Prediction with ChatDRex](https://mlsanity.com/paper/2511.21438/) (2025)
- [Introducing ORKG ASK: an AI-driven Scholarly Literature Search and Exploration System Taking a Neuro-Symbolic Approach](https://mlsanity.com/paper/2512.16425/) (2025)
- [TIB AIssistant: a Platform for AI-Supported Research Across Research Life Cycles](https://mlsanity.com/paper/2512.16442/) (2025)
- [How Do LLMs Encode Scientific Quality? An Empirical Study Using Monosemantic Features from Sparse Autoencoders](https://mlsanity.com/paper/2602.19115/) (2026)
- [LLMs in Qualitative Research: Opportunities, Limitations, and Practical Considerations](https://mlsanity.com/paper/2605.16538/) (2026)
- [Smaller, Younger, and More Impactful: How AI-Assisted Writing Transforms Research Teams](https://mlsanity.com/paper/2605.27404/) (2026)
- [Report on CHIIR 2026 Workshop on Generative AI and Academic Search (GAI&AS)](https://mlsanity.com/paper/2606.08936/) (2026)
- [Benchmarking Resource-Efficient LLMs for Research Topic Ontology Generation in the Biomedical Field](https://mlsanity.com/paper/2607.17902/) (2026)

## Abstract (arXiv)

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.