The emergence of large language models as tools in literature reviews: a large language model-assisted systematic review
OBJECTIVES: This study aims to summarize the usage of large language models (LLMs) in the process of creating a scientific review by looking at the methodological papers that describe the use of LLMs in review automation and the review papers that mention they were made with the support of LLMs. MATERIALS AND METHODS: The search was conducted in June 2024 in PubMed, Scopus, Dimensions, and Google Scholar by human reviewers. Screening and extraction process took place in Covidence with the help o
Record details
Published: 7 May 2025
Source: OpenAlex
Category: Research
Topics: Jobs & economy
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Six Institutional Intervention Areas to Support Ethical and Effective Student Use of Generative AI in Higher Education: A Narrative Review
OpenAlex · 16 January 2026
AI Ethics
OpenAlex · 23 March 2020
Google Workspace 2025 to 2030
UK Contracts Finder — AI procurement · 17 June 2025
Unlocking Britain’s next era of productivity: Building a nation of AI trailblazers
Google AI Blog · 30 June 2026
New York City educators and industry leaders gathered at Google’s offices to shape the future of AI in classrooms.
Google AI Blog · 1 July 2026
Big Brand Jobs Scam Targets Marketing Pros' Google Accounts
Dark Reading (AI security) · 7 July 2026
How to cite this record
ethics.ai (7 May 2025), “The emergence of large language models as tools in literature reviews: a large language model-assisted systematic review,” evidence record 9738, https://ethics.ai/record/9738 (originally published by OpenAlex).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.