  As a subset of machine learning, meta-learning, or learning to learn, aims at
improving the model's capabilities by employing prior knowledge and experience.
A meta-learning paradigm can appropriately tackle the conventional challenges
of traditional learning approaches, such as insufficient number of samples,
domain shifts, and generalization. These unique characteristics position
meta-learning as a suitable choice for developing influential solutions in
various healthcare contexts, where the available data is often insufficient,
and the data collection methodologies are different. This survey discusses
meta-learning broad applications in the healthcare domain to provide insight
into how and where it can address critical healthcare challenges. We first
describe the theoretical foundations and pivotal methods of meta-learning. We
then divide the employed meta-learning approaches in the healthcare domain into
two main categories of multi/single-task learning and many/few-shot learning
and survey the studies. Finally, we highlight the current challenges in
meta-learning research, discuss the potential solutions, and provide future
perspectives on meta-learning in healthcare.
