  Question Answering (QA) is a benchmark Natural Language Processing (NLP) task
where models predict the answer for a given question using related documents,
images, knowledge bases and question-answer pairs. Automatic QA has been
successfully applied in various domains like search engines and chatbots.
However, for specific domains like biomedicine, QA systems are still rarely
used in real-life settings. Biomedical QA (BQA), as an emerging QA task,
enables innovative applications to effectively perceive, access and understand
complex biomedical knowledge. In this work, we provide a critical review of
recent efforts in BQA. We comprehensively investigate prior BQA approaches,
which are classified into 6 major methodologies (open-domain, knowledge base,
information retrieval, machine reading comprehension, question entailment and
visual QA), 4 topics of contents (scientific, clinical, consumer health and
examination) and 5 types of formats (yes/no, extraction, generation,
multi-choice and retrieval). In the end, we highlight several key challenges of
BQA and explore potential directions for future works.
