Can machine learning help accelerate article screening for systematic reviews? Yes, when article separability in embedding space is high.

ABSTRACT: Systematic reviews play important roles but manual efforts can be time-consuming given a growing literature. There is a need to use and evaluate automated strategies to accelerate systematic reviews. Here, we comprehensively tested machine learning (ML) models from classical and deep learning model families. We also assessed the performance of prompt engineering via few-shot learning of GPT-3.5 and GPT-4 large language models (LLMs). We further attempted to understand when ML models can help automate screening. These ML models were applied to actual datasets of systematic reviews in education. Results showed that the performance of classical and deep ML models varied widely across datasets, ranging from 1.2 to 75.6% of work saved at 95% recall. LLM prompt engineering produced similarly wid

SUBMITTER: Ali F 

PROVIDER: S-EPMC12621506 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature