Integrating Large Language Models into Systematic Review Screening

Large Language Models (LLMs) have recently emerged as a powerful option for partially automating the labor-intensive process of screening articles in systematic reviews. Unlike traditional semi-automated platforms that rely on iterative human feedback, LLM-based pipelines can operate in a zero-shot or few-shot manner, classifying abstracts according to predefined criteria. This paper offers a step-by-step methodology for researchers, librarians, and students seeking to incorporate LLMs—such as GPT-4—into systematic reviews. It discusses required software and data preprocessing, presents various prompt strategies, and emphasizes the importance of human oversight to maintain rigorous quality control. The proposed framework aims to provide best practices and offer guidance on managing costs, reproducibility, and prompt refinement. By following these guidelines, review teams can substantially reduce screening workloads without compromising the comprehensive nature of evidence-based research.