Biomedical Question Answering: A Survey of Approaches and Challenges

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 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.

arXiv commentIn submission to ACM Computing Surveys

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• Matching Patients to Clinical Trials with Large Language Models
• GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information
• Benchmarking large language models for biomedical natural language processing applications and recommendations
• PubMed and Beyond: Biomedical Literature Search in the Age of Artificial Intelligence
• BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension Task
• Large-Scale Knowledge Synthesis and Complex Information Retrieval from Biomedical Documents
• Development of an Extractive Clinical Question Answering Dataset with Multi-Answer and Multi-Focus Questions
• Recent Advances in Automated Question Answering In Biomedical Domain