  Objective: Few-shot learning (FSL) methods require small numbers of labeled
instances for training. As many medical topics have limited annotated textual
data in practical settings, FSL-based natural language processing (NLP) methods
hold substantial promise. We aimed to conduct a systematic review to explore
the state of FSL methods for medical NLP. Materials and Methods: We searched
for articles published between January 2016 and August 2021 using
PubMed/Medline, Embase, ACL Anthology, and IEEE Xplore Digital Library. To
identify the latest relevant methods, we also searched other sources such as
preprint servers (eg., medRxiv) via Google Scholar. We included all articles
that involved FSL and any type of medical text. We abstracted articles based on
data source(s), aim(s), training set size(s), primary method(s)/approach(es),
and evaluation method(s). Results: 31 studies met our inclusion criteria-all
published after 2018; 22 (71%) since 2020. Concept extraction/named entity
recognition was the most frequently addressed task (13/31; 42%), followed by
text classification (10/31; 32%). Twenty-one (68%) studies reconstructed
existing datasets to create few-shot scenarios synthetically, and MIMIC-III was
the most frequently used dataset (7/31; 23%). Common methods included FSL with
attention mechanisms (12/31; 39%), prototypical networks (8/31; 26%), and
meta-learning (6/31; 19%). Discussion: Despite the potential for FSL in
biomedical NLP, progress has been limited compared to domain-independent FSL.
This may be due to the paucity of standardized, public datasets, and the
relative underperformance of FSL methods on biomedical topics. Creation and
release of specialized datasets for biomedical FSL may aid method development
by enabling comparative analyses.
