Evaluating the effectiveness of large language models in abstract screening: a comparative analysis

While LLM tools are not yet ready to completely replace human experts in abstract screening, they show great promise in revolutionizing the process. They can serve as autonomous AI reviewers, contribute to collaborative workflows with human experts, and integrate with hybrid approaches to develop custom tools for increased efficiency. As technology continues to advance, LLMs are poised to play an increasingly important role in abstract screening, reshaping the workflow of systematic review and meta-analysis studies.

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Publication Details

Journal
Systematic Reviews
Published
2024-08-21
DOI
https://doi.org/10.1186/s13643-024-02609-x
Citations
87
Primary Topic
Meta-analysis and systematic reviews
Type
article
Field-Weighted Citation Impact
40.13
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article

Evaluating the effectiveness of large language models in abstract screening: a comparative analysis

Xianming Tan, Michael Li, Jian‐Ping Sun
87 citations
Systematic Reviews
Meta-analysis and systematic reviews
40.13
article

Evaluating the effectiveness of large language models in abstract screening: a comparative analysis

Xianming Tan, Michael Li, Jian‐Ping Sun
article en
87 citations

Abstract

While LLM tools are not yet ready to completely replace human experts in abstract screening, they show great promise in revolutionizing the process. They can serve as autonomous AI reviewers, contribute to collaborative workflows with human experts, and integrate with hybrid approaches to develop custom tools for increased efficiency. As technology continues to advance, LLMs are poised to play an increasingly important role in abstract screening, reshaping the workflow of systematic review and meta-analysis studies.

Systematic ReviewsVol. 13(1)
University of North Carolina at Chapel Hill (US), University of North Carolina at Greensboro (US)
Quality Education
Openalex Percentile: Top 0%
Meta-analysis and systematic reviews
40.13
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