Systematic reviews (SRs) serve as a core methodology in evidence-based medicine (EBM), providing critical evidence for clinical practice and health decision-making. However, the manual screening of titles and abstracts in SRs is labor-intensive and time-consuming, becoming a major bottleneck in research efficiency. Recent advancements in artificial intelligence (AI), particularly large language models (LLMs), have introduced new opportunities and transformations in this field. This article provided an overview of the current status of intelligent screening for titles and abstracts in systematic reviews, with a focus on the application and effectiveness of LLMs. It aims to provide recommendations for users and developers, facilitating the better integration of automation algorithms into the SR process.