Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care
It tackles the need for interpretability in AI-driven medical diagnosis to guide healthcare decisions, but it is incremental as it reviews existing methods rather than proposing new ones.
This review addresses the problem of black-box deep learning models like Vision Transformers (ViT) in medical imagery diagnosis by summarizing recent advancements in ViT and interpretative methods from Explainable AI (XAI) to enable transparency in decision-making.
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.