Health is a state of complete physical, mental, and social well-being and not merely the absence of disease and infirmity. Artificial intelligence (AI) has recently been widely used in health and related fields. In the past, AI has shown itself as a complex tool and a solution assisting medical professionals in diagnosing various diseases. However, AIs are still black boxes that do not help decision-making. The poor explainability causes distrust from clinicians/doctors who train to make an explainable diagnosis.

Thus, there is an urgent need for novel methodologies to improve the explainability of existing AI methods used routinely in clinical practices. Explainable deep learning (DL) methods will help interpret the diagnosis for patients and physicians. This Special Issue highlights advances in explainable AI theories and models in health. Both conventional and new explainable AI-related papers are welcome.

Prof. Dr. Yudong Zhang
Prof. Dr. Juan Manuel Gorriz
Dr. Zhengchao Dong
Topic Editors

Keywords
oncological imaging
tumor detection and diagnosis
omics
supervised and unsupervised learning
kernel methods
deep neural networks
mathematical modeling
graph neural network
attention neural network
healthcare
disease diagnosis