Automatic Question Answering (QA) has been successfully applied in various domains such as search engines and chatbots. Biomedical QA (BQA), as an emerging QA task, enables innovative applications to effectively perceive, access and understand complex biomedical knowledge. There have been tremendous developments of BQA in the past two decades, which we classify into 5 distinctive approaches: classic, information retrieval, machine reading comprehension, knowledge base and question entailment approaches. In this survey, we introduce available datasets and representative methods of each BQA approach in detail. Despite the developments, BQA systems are still immature and rarely used in real-life settings. We identify and characterize several key challenges in BQA that might lead to this issue, and discuss some potential future directions to explore.
BELIEF: Structured Evidence Modeling and Uncertainty-Aware Fusion for Biomedical Question Answering
School of Computer Science and Technology, Zhejiang University of Science and Technology（浙江理工大学计算机科学与技术学院）；College of Artificial Intelligence, Zhejiang University（浙江大学人工智能学院）；Zhejiang Key Laboratory of Biomedical Intelligent Computing Technology, Zhejiang University of Science and Technology（浙江理工大学生物医学智能计算技术重点实验室）
Overview of BioASQ 2023: The eleventh BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
National Center for Scientific Research “Demokritos”（希腊德谟克利特国家科学研究中心）；Aristotle University of Thessaloniki（亚里士多德大学）；Barcelona Supercomputing Center（巴塞罗那超级计算中心）
Data Augmentation for Biomedical Factoid Question Answering
Athens University of Economics and Business（雅典经济与商业大学）；Institute for Language and Speech Processing, Research Center ‘Athena’（“雅典娜”研究中心语言与语音处理研究所）；Institute of Informatics and Telecommunications, NCSR ‘Demokritos’（国家科学研究中心“德谟克利特”信息与电信研究所）
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）
Overview of BioASQ 2020: The eighth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
National Center for Scientific Research “Demokritos”（“德谟克利特”国家科学研究中心）；Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）；National and Kapodistrian University of Athens（雅典国立卡波迪斯特里安大学）；Barcelona Supercomputing Center（巴塞罗那超级计算中心）
Overview of BioASQ 2026: The fourteenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
National Center for Scientific Research “Demokritos”（德谟克利特国家科学研究中心）；Barcelona Supercomputing Center（巴塞罗那超级计算中心）；Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学）；Artificial Intelligence Research Institute（人工智能研究院）；HSE University（高等经济大学）；Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）
Emerging Challenges in Personalized Medicine: Assessing Demographic Effects on Biomedical Question Answering Systems
University of Colorado Boulder（科罗拉多大学博尔德分校）；University of Colorado Denver（科罗拉多大学丹佛分校）；Memorial Healthcare System（纪念医疗系统）；Johannes Gutenberg University Mainz（美因茨约翰内斯·古腾堡大学）
NeuroSym-BioCAT: Leveraging Neuro-Symbolic Methods for Biomedical Scholarly Document Categorization and Question Answering
TIB—Leibniz Information Centre for Science and Technology（莱布尼茨科学与技术信息中心（TIB））；L3S Research Center, Leibniz University Hannover（汉诺威莱布尼茨大学L3S研究中心）；Daffodil International University（水仙国际大学）
Overview of BioASQ 2022: The tenth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
National Center for Scientific Research “Demokritos”（希腊德谟克利特国家科学研究中心）；Aristotle University of Thessaloniki（亚里士多德大学塞萨洛尼基分校）；Barcelona Supercomputing Center（巴塞罗那超级计算中心）
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Korea University（高丽大学）；Clova AI Research, Naver Corp（Naver公司Clova人工智能研究院）
随着生物医学文档数量的快速增长，生物医学文本挖掘变得越来越重要。随着自然语言处理（NLP）的发展，从生物医学文献中提取有价值的信息越来越受到研究人员的欢迎，深度学习推动了有效的生物医学文本挖掘模型的发展。然而，直接应用自然语言处理的进步，生物医学文本挖掘往往会产生不满意的结果，由于一个词的分布偏移从一般领域语料库的生物医学语料库。在这篇文章中，我们研究了最近引入的预训练语言模型BERT如何适用于生物医学语料库。我们介绍了BioBERT（Bidirectional Encoder Representations from Transformers for Biomedical Text Mining），这是一个在大规模生物医学语料库上预训练的特定领域语言表示模型。在任务之间具有几乎相同的架构，BioBERT在生物医学语料库上进行预训练时，在各种生物医学文本挖掘任务中的性能大大优于BERT和以前的最先进模型。虽然BERT获得的性能与以前的国家的最先进的模型，生物BERT显着优于他们在以下三个代表性的生物医学文本挖掘任务：生物医学命名实体识别（0.62%的F1分数提高），生物医学关系抽取（2.80%的F1分数提高）和生物医学问答（12.24%的MRR提高）。我们的分析结果表明，生物医学语料库上的预训练BERT有助于它理解复杂的生物医学文本。我们在https：//github.com/na…
MedHopQA: A Disease-Centered Multi-Hop Reasoning Benchmark and Evaluation Framework for LLM-Based Biomedical Question Answering
National Library of Medicine, Division of Intramural Research（国家医学图书馆，院内研究部）；University of Illinois at Urbana-Champaign, Department of Computer Science（伊利诺伊大学厄巴纳-香槟分校计算机科学系）；University of Michigan Medical School（密歇根大学医学院）
BioMedSearch: A Multi-Source Biomedical Retrieval Framework Based on LLMs
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Johns Hopkins University（约翰霍普金斯大学）；Henan University（河南大学）
DeepRAG: Integrating Hierarchical Reasoning and Process Supervision for Biomedical Multi-Hop QA
Department of Information Science, University of Pittsburgh（信息科学系，匹兹堡大学）；Department of Biomedical Informatics, University of Pittsburgh（生物医学信息学系，匹兹堡大学）；Department of Health Information Management and ISP, University of Pittsburgh（健康信息管理与信息科学系，匹兹堡大学）
