Biomedical Question Answering: A Survey of Approaches and Challenges

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 tremendou...

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Bibliographic Details
Published in:ACM computing surveys 2023-03, Vol.55 (2), p.1-36
Main Authors: Jin, Qiao, Yuan, Zheng, Xiong, Guangzhi, Yu, Qianlan, Ying, Huaiyuan, Tan, Chuanqi, Chen, Mosha, Huang, Songfang, Liu, Xiaozhong, Yu, Sheng
Format: Article
Language:English
Subjects:
Computer science
Information retrieval
Knowledge bases (artificial intelligence)
Questions
Search engines
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Summary: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.
ISSN:0360-0300
1557-7341
DOI:10.1145/3490238