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.
RAG-BioQA: A Retrieval-Augmented Generation Framework for Long-Form Biomedical Question Answering
Department of CSE Emory University Georgia, USA（计算机科学与工程系 埃默里大学 美国佐治亚州）；Department of CSE SRM University AP Andhra Pradesh, India（计算机科学与工程系 SRM大学AP 安得拉邦印度）；Department of CSE Trine University Michigan, USA（计算机科学与工程系 特林大学 密歇根州美国）
Ensembles of Large Language Models for Identifying EQ-5D Studies in PubMed Based on Their Abstracts
Doctoral School of Applied Informatics and Applied Mathematics, Obuda University（欧布达大学应用信息学与应用数学博士学院）；John von Neumann Faculty of Informatics, Obuda University（欧布达大学约翰·冯·诺伊曼信息学学院）；Doctoral School of Innovation Management, Obuda University（欧布达大学创新管理博士学院）
Delving into LLM-assisted writing in biomedical publications through excess vocabulary
Hertie Institute for AI in Brain Health, University of Tübingen, Germany（人工智能与脑健康赫尔蒂研究所、图宾根大学，德国）；Northwestern University, Evanston, Illinois, USA（西北大学，埃文斯顿，伊利诺伊州，美国）
AIONER: All-in-one scheme-based biomedical named entity recognition using deep learning
National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH)（美国国立卫生研究院国家医学图书馆国家生物技术信息中心）；School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）
MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrieval
National Center for Biotechnology Information (NCBI)（国家生物技术信息中心）；National Library of Medicine (NLM)（国家医学图书馆）；National Institutes of Health (NIH)（国立卫生研究院）
MedCPT 利用 2.55 亿条 PubMed 点击日志进行对比预训练，首次实现生物医学零样本语义检索，并在六个 IR 任务上超越 GPT-3 规模基线模型。
LLMs are not Zero-Shot Reasoners for Biomedical Information Extraction
ASUS Intelligent Cloud Services (AICS)（ASUS智能云服务）；Imperial College London（伦敦帝国学院）；University of Manchester（曼彻斯特大学）；National University of Singapore（新加坡国立大学）
A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks
LIA, Avignon Université（阿维尼翁大学 LIA 实验室）；Zenidoc（Zenidoc 公司）；Nantes Université（南特大学）；École Centrale Nantes（南特中央理工学院）；CNRS（法国国家科学研究中心）；LS2N, UMR 6004（南特数字科学实验室）
Hierarchical Dual-Strategy Unlearning for Biomedical and Healthcare Intelligence Using Imperfect and Privacy-Sensitive Medical Data
Department of Neurosurgery, Qilu Hospital of Shandong University and Institute of Brain and Brain-Inspired Science（齐鲁医院山东大学神经外科和脑科学与脑启发科学研究院）；Department of Neurosurgery, The Affiliated Hospital of Qingdao University（青岛大学附属医院神经外科）；Department of Neurosurgery, Peking Union Medical College Hospital（北京地坛医院神经外科）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；College of Artificial Intelligence, Dalian Maritime University（大连海事大学人工智能学院）；University of Colorado Boulder（科罗拉多大学波尔德分校）；Zhengzhou University（郑州大学）；The University of Hong Kong（香港大学）；Georgia Institute of Technology（佐治亚理工学院）
R-BERT-CNN: Drug-target interactions extraction from biomedical literature
University of Helsinki（赫尔辛基大学）
在这项研究中，我们提出了我们的工作参与BioCreative VII挑战的DrugProt任务。药物-靶点相互作用（DTI）是药物发现和再利用的关键，通常从实验文章中手动提取。PubMed上有超过3200万篇生物医学文章，从如此庞大的知识库中手动提取DTI具有挑战性。为了解决这个问题，我们为Track 1提供了一个解决方案，旨在提取药物和蛋白质实体之间的10种相互作用。我们应用了一个Ensemble分类器模型，该模型结合了BioMed-RoBERTa（一种最先进的语言模型）和卷积神经网络（CNN）来提取这些关系。尽管BioCreative VII DrugProt测试语料库中存在类别不平衡，但与挑战中其他提交的平均值相比，我们的模型实现了良好的性能，微F1得分为55.67%（BioCreative VI ChemProt测试语料库为63%）。结果显示了深度学习在提取各种类型的DTI方面的潜力。
Discovering novel drug-supplement interactions using a dietary supplements knowledge graph generated from the biomedical literature
University of Minnesota（明尼苏达大学）；University of Illinois（伊利诺伊大学）；The University of Manchester（曼彻斯特大学）；Pontifical Catholic University of Rio de Janeiro（里约热内卢天主教大学）；The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）
RanAT4BIE: Random Adversarial Training for Biomedical Information Extraction
Department of Data Science and Big Data Technology, Hainan University, Haikou 570228, China（数据科学与大数据技术学院，海南大学，海口570228，中国）；Department of Mathematics, Hainan University, Haikou 570228, China（数学学院，海南大学，海口570228，中国）
Graph-Aware Late Chunking for Retrieval-Augmented Generation in Biomedical Literature
Methodological Implementation Research, Ottawa Hospital Research Institute（方法学实施研究，渥太华医院研究所在职）；School of Engineering Design and Teaching Innovation, University of Ottawa（工程设计与教学创新学院，渥太华大学）
BioConceptVec: creating and evaluating literature-based biomedical concept embeddings on a large scale
National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH)（美国国立卫生研究院国立医学图书馆国家生物技术信息中心）
