Screening articles for systematic reviews with ChatGPT
Abstract
Systematic reviews (SRs) provide valuable evidence for guiding new research directions. However, the manual effort involved in selecting articles for inclusion in an SR is error-prone and time-consuming. While screening articles has traditionally been considered challenging to automate, the advent of large language models offers new possibilities. In this paper, we discuss the effect of using ChatGPT on the SR process. In particular, we investigate the effectiveness of different prompt strategies for automating the article screening process using five real SR datasets. Our results show that ChatGPT can reach up to 82% accuracy. The best performing prompts specify exclusion criteria and avoid negative shots. However, prompts should be adapted to different corpus characteristics.
Cite this
Eugene Syriani, István Dávid (2024). Screening articles for systematic reviews with ChatGPT. Journal of Computer Languages. https://doi.org/10.1016/j.cola.2024.101287
@article{eugene2024screening,
title = {Screening articles for systematic reviews with ChatGPT},
author = {Syriani, Eugene and Dávid, István},
year = {2024},
journal = {Journal of Computer Languages},
doi = {10.1016/j.cola.2024.101287}
}Citations in this library
Stored only when both papers are in the corpus — 81 citations exist upstream.