survey

Biomedical Question Answering: A Survey of Approaches and Challenges

Authors:

Guangzhi Xiong,

Songfang Huang,

Sheng YuAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 55, Issue 2

Article No.: 35, Pages 1 - 36

https://doi.org/10.1145/3490238

Published: 18 January 2022 Publication History

Abstract

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 five 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 we discuss some potential future directions to explore.

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Index Terms

Biomedical Question Answering: A Survey of Approaches and Challenges
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 2

February 2023

803 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3505209

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 18 January 2022

Accepted: 01 September 2021

Revised: 01 August 2021

Received: 01 March 2021

Published in CSUR Volume 55, Issue 2

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https://doi.org/10.2196/52482
Hu JHuang ZGe XShen YXu YZhang ZZhou GWang JLu SYu YWan CZhang XHuang RLiu YCheng G(2024)Development and application of Chinese medical ontology for diabetes mellitusBMC Medical Informatics and Decision Making10.1186/s12911-023-02405-y24:1Online publication date: 19-Jan-2024
https://doi.org/10.1186/s12911-023-02405-y
Fang YChen YJiang ZXiao JGe Y(2024)Effective and Reliable Domain-Specific Knowledge Question Answering2024 IEEE International Conference on e-Business Engineering (ICEBE)10.1109/ICEBE62490.2024.00044(238-243)Online publication date: 11-Oct-2024
https://doi.org/10.1109/ICEBE62490.2024.00044
Chen MHe GWu J(2024)ZDDR: A Zero-Shot Defender for Adversarial Samples Detection and RestorationIEEE Access10.1109/ACCESS.2024.335656812(39081-39094)Online publication date: 2024
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Tran HYang ZYao ZYu H(2024)BioInstruct: instruction tuning of large language models for biomedical natural language processingJournal of the American Medical Informatics Association10.1093/jamia/ocae12231:9(1821-1832)Online publication date: 4-Jun-2024
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Jin QYang YChen QLu Z(2024)GeneGPT: augmenting large language models with domain tools for improved access to biomedical informationBioinformatics10.1093/bioinformatics/btae07540:2Online publication date: 10-Feb-2024
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Li QHu ZWang YLi LFan YKing IJia GWang SSong LLi Y(2024)Progress and opportunities of foundation models in bioinformaticsBriefings in Bioinformatics10.1093/bib/bbae54825:6Online publication date: 26-Oct-2024
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