  Advancements in wearable sensor technologies and the digitization of medical
records have contributed to the unprecedented ubiquity of biomedical time
series data. Data-driven models have tremendous potential to assist clinical
diagnosis and improve patient care by improving long-term monitoring
capabilities, facilitating early disease detection and intervention, as well as
promoting personalized healthcare delivery. However, accessing extensively
labeled datasets to train data-hungry deep learning models encounters many
barriers, such as long-tail distribution of rare diseases, cost of annotation,
privacy and security concerns, data-sharing regulations, and ethical
considerations. An emerging approach to overcome the scarcity of labeled data
is to augment AI methods with human-like capabilities to leverage past
experiences to learn new tasks with limited examples, called few-shot learning.
This survey provides a comprehensive review and comparison of few-shot learning
methods for biomedical time series applications. The clinical benefits and
limitations of such methods are discussed in relation to traditional
data-driven approaches. This paper aims to provide insights into the current
landscape of few-shot learning for biomedical time series and its implications
for future research and applications.
