综述大模型在生物医学知识提取中的应用与挑战
A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences
- 系统梳理大模型在生物医学文献知识提取中的最新进展
- 指出幻觉、上下文理解等关键问题影响实际应用可靠性
- 适合关注AI辅助医学研究的科研人员和临床决策者
大语言模型（LLMs）的快速发展为生物医学领域的知识提取与综合带来了新突破，尤其在证据合成方面。本文综述了当前大模型在生物医学领域应用的最新进展，探讨其在自动化处理复杂任务如从生物医学文档语料库中提取证据与数据方面的有效性。尽管大模型展现出巨大潜力，但仍面临幻觉、上下文理解不足以及跨多样化医疗任务泛化能力弱等挑战。文章指出现有研究的关键空白，特别是缺乏统一基准来标准化评估并保障真实场景下的可靠性。此外，提出未来研究方向，强调融合检索增强生成（RAG）等前沿技术以提升大模型在证据合成中的表现。通过解决这些挑战并发挥大模型优势，有望提升医学文献可及性，推动医疗健康领域的有意义发现。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。