Information, Journal Year: 2025, Volume and Issue: 16(5), P. 378 - 378

Published: May 1, 2025

Systematic reviews require labor-intensive screening processes—an approach prone to bottlenecks, delays, and scalability constraints in large-scale reviews. Large Language Models (LLMs) have recently emerged as a powerful alternative, capable of operating zero-shot or few-shot modes classify abstracts according predefined criteria without requiring continuous human intervention like semi-automated platforms. This review focuses on the central challenges that users biomedical field encounter when integrating LLMs—such GPT-4—into evidence-based research. It examines critical requirements for software data preprocessing, discusses various prompt strategies, underscores continued need oversight maintain rigorous quality control. By drawing current practices cost management, reproducibility, refinement, this article highlights how teams can substantially reduce workloads compromising comprehensiveness inquiry. The findings presented aim balance strengths LLM-driven automation with structured checks, ensuring systematic retain their methodological integrity while leveraging efficiency gains made possible by recent advances artificial intelligence.

Language: Английский