Systematic literature reviews are essential for academic research, but manually sifting through thousands of papers is a tedious, time-consuming process. Imagine speeding up that process from weeks to mere minutes. That's the promise of a new approach using Large Language Models (LLMs) as intelligent filters, cutting through the clutter and identifying relevant research with remarkable speed and accuracy. Researchers tested several leading LLMs, including open-source models like Llama3 and commercial models like GPT-4, to automate the initial filtering stage of a literature review. They found that while individual LLMs performed well, a “consensus voting” system, where multiple LLMs had to agree on a paper's relevance, dramatically reduced errors. This method achieved near-perfect accuracy in identifying truly relevant papers, even surpassing human performance in some cases. The implications are huge. This AI-assisted approach could free up researchers to focus on the more complex aspects of literature reviews, like analysis and synthesis, while reducing fatigue and improving overall productivity. While there are challenges, like potential biases in the LLMs and the need for human oversight, this research points to a future where AI significantly streamlines academic research, making it faster, more efficient, and potentially more comprehensive.