The rapid advancement of large language models (LLMs) has opened new boundaries in the extraction and synthesis of medical knowledge, particularly within evidence synthesis. This paper reviews the state-of-the-art applications of LLMs in the biomedical domain, exploring their effectiveness in automating complex tasks such as evidence synthesis and data extraction from a biomedical corpus of documents. While LLMs demonstrate remarkable potential, significant challenges remain, including issues related to hallucinations, contextual understanding, and the ability to generalize across diverse medical tasks. We highlight critical gaps in the current research literature, particularly the need for unified benchmarks to standardize evaluations and ensure reliability in real-world applications. In addition, we propose directions for future research, emphasizing the integration of state-of-the-art techniques such as retrieval-augmented generation (RAG) to enhance LLM performance in evidence synthesis. By addressing these challenges and utilizing the strengths of LLMs, we aim to improve access to medical literature and facilitate meaningful discoveries in healthcare.

翻译：大型语言模型（LLMs）的快速发展为医学知识的提取与综合，特别是在证据综合领域，开辟了新的边界。本文综述了LLMs在生物医学领域的最新应用，探讨了其在自动化复杂任务（如证据综合和从生物医学文献语料库中提取数据）方面的有效性。尽管LLMs展现出显著潜力，但仍存在重大挑战，包括与幻觉、上下文理解以及在不同医学任务间的泛化能力相关的问题。我们强调了当前研究文献中的关键空白，特别是对统一基准的需求，以标准化评估并确保实际应用的可靠性。此外，我们提出了未来研究方向，强调集成检索增强生成（RAG）等先进技术以提升LLMs在证据综合中的性能。通过应对这些挑战并利用LLMs的优势，我们旨在改善对医学文献的获取，并促进医疗健康领域的有意义发现。