  The challenge of learning a new concept, object, or a new medical disease
recognition without receiving any examples beforehand is called Zero-Shot
Learning (ZSL). One of the major issues in deep learning based methodologies
such as in Medical Imaging and other real-world applications is the requirement
of large annotated datasets prepared by clinicians or experts to train the
model. ZSL is known for having minimal human intervention by relying only on
previously known or trained concepts plus currently existing auxiliary
information. This makes the ZSL applicable in many real-world scenarios, from
unknown object detection in autonomous vehicles to medical imaging and
unforeseen diseases such as COVID-19 Chest X-Ray (CXR) based diagnosis. We
introduce a novel and broaden solution called Few/one-shot learning, and
present the definition of the ZSL problem as an extreme case of the few-shot
learning. We review over fundamentals and the challenging steps of Zero-Shot
Learning, including state-of-the-art categories of solutions, as well as our
recommended solution, motivations behind each approach, their advantages over
each category to guide both clinicians and AI researchers to proceed with the
best techniques and practices based on their applications. We then review
through different datasets inducing medical and non-medical images, the variety
of splits, and the evaluation protocols proposed so far. Finally, we discuss
the recent applications and future directions of ZSL. We aim to convey a useful
intuition through this paper towards the goal of handling complex learning
tasks more similar to the way humans learn. We mainly focus on two applications
in the current modern yet challenging era: coping with an early and fast
diagnosis of COVID-19 cases, and also encouraging the readers to develop other
similar AI-based automated detection/recognition systems using ZSL.
