  Recent advancements in artificial intelligence (AI) have facilitated its
widespread adoption in primary medical services, addressing the demand-supply
imbalance in healthcare. Vision Transformers (ViT) have emerged as
state-of-the-art computer vision models, benefiting from self-attention
modules. However, compared to traditional machine-learning approaches,
deep-learning models are complex and are often treated as a "black box" that
can cause uncertainty regarding how they operate. Explainable Artificial
Intelligence (XAI) refers to methods that explain and interpret machine
learning models' inner workings and how they come to decisions, which is
especially important in the medical domain to guide the healthcare
decision-making process. This review summarises recent ViT advancements and
interpretative approaches to understanding the decision-making process of ViT,
enabling transparency in medical diagnosis applications.
