Imagine a world where medical breakthroughs happen faster, where clinical trials are analyzed instantly, and where the latest research is synthesized on demand. This is the promise of automated meta-analysis, a field that aims to use artificial intelligence to revolutionize how we understand and apply scientific evidence. A new research paper explores the potential of Large Language Models (LLMs) to automatically extract numerical results from Randomized Controlled Trials (RCTs), the gold standard for medical research. The key question: Can AI handle the complex task of sifting through research papers and pulling out the critical data needed for meta-analysis? The researchers found that massive LLMs, like GPT-4, show surprising promise, especially when dealing with straightforward outcomes like mortality rates. These powerful AI models can process vast amounts of text, identify key information, and even perform basic calculations. However, the study also reveals significant challenges. LLMs struggle when faced with more complex outcome measures or when the data requires interpretation or inference. For example, if a study reports results at multiple time points, the LLM might extract data from the wrong time point, leading to inaccurate conclusions. Another hurdle is the ambiguity of human language. LLMs can be tripped up by nuanced phrasing or inconsistent reporting styles across different research papers. While LLMs can't yet replace human researchers, they offer a powerful tool to accelerate the process. Imagine AI pre-screening studies, identifying key data points, and even generating initial drafts of meta-analyses. This would free up human experts to focus on the more complex tasks of interpretation, quality assessment, and clinical decision-making. The future of automated meta-analysis is bright, but there's still work to be done. Improving LLMs' ability to handle complex data, reason through ambiguity, and perform more sophisticated calculations will be crucial. As AI continues to evolve, the dream of on-demand, automated meta-analysis may soon become a reality, transforming the landscape of medical research and accelerating the pace of scientific discovery.