Biomedical research yields a wealth of information, much of which is only accessible through the literature. Consequently, literature search is an essential tool for building on prior knowledge in clinical and biomedical research. Although recent improvements in artificial intelligence have expanded functionality beyond keyword-based search, these advances may be unfamiliar to clinicians and researchers. In response, we present a survey of literature search tools tailored to both general and specific information needs in biomedicine, with the objective of helping readers efficiently fulfill their information needs. We first examine the widely used PubMed search engine, discussing recent improvements and continued challenges. We then describe literature search tools catering to five specific information needs: 1. Identifying high-quality clinical research for evidence-based medicine. 2. Retrieving gene-related information for precision medicine and genomics. 3. Searching by meaning, including natural language questions. 4. Locating related articles with literature recommendation. 5. Mining literature to discover associations between concepts such as diseases and genetic variants. Additionally, we cover practical considerations and best practices for choosing and using these tools. Finally, we provide a perspective on the future of literature search engines, considering recent breakthroughs in large language models such as ChatGPT. In summary, our survey provides a comprehensive view of biomedical literature search functionalities with 36 publicly available tools.

翻译：生物医学研究产生了丰富的信息，其中许多仅可通过文献获取。因此，文献检索是临床和生物医学研究中构建先前知识的重要工具。尽管人工智能的最新进展将功能扩展到了基于关键词的搜索之外，但这些进展可能对临床医生和研究人员而言并不熟悉。为此，我们针对生物医学中一般性和特定性的信息需求，对文献检索工具进行了综述，旨在帮助读者高效满足其信息需求。我们首先审视了广泛使用的PubMed搜索引擎，讨论了其近期改进和持续存在的挑战。随后，我们描述了针对五种特定信息需求的文献检索工具：1. 识别高质量临床研究以支持循证医学；2. 检索与基因相关的信息以支持精准医学和基因组学；3. 按含义搜索，包括自然语言问题；4. 通过文献推荐定位相关文章；5. 挖掘文献以发现疾病与遗传变异等概念间的关联。此外，我们还涵盖了选择和运用这些工具的实用考量与最佳实践。最后，我们基于ChatGPT等大型语言模型的最新突破，对文献搜索引擎的未来进行了展望。总之，我们的综述提供了36个公开可用的生物医学文献检索功能的全面视图。