Generative artificial intelligence use in evidence synthesis: A systematic review
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Abstract
Introduction
With the increasing accessibility of tools such as ChatGPT, Copilot, DeepSeek, Dall-E, and Gemini, generative artificial intelligence (GenAI) has been poised as a potential, research timesaving tool, especially for synthesising evidence. Our objective was to determine whether GenAI can assist with evidence synthesis by assessing its performance using its accuracy, error rates, and time savings compared to the traditional expert-driven approach.
Methods
To systematically review the evidence, we searched five databases on 17 January 2025, synthesised outcomes reporting on the accuracy, error rates, or time taken, and appraised the risk-of-bias using a modified version of QUADAS-2.
Citation impact
44
total citations
- FWCI
- 21.23
- Percentile
- 100%
- References
- 32
Citations per year
Authors
10Topics & keywords
Topics
Keywords
- Computer science
- Generative grammar
- Artificial intelligence
- Machine learning
- Natural language processing
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