Current Challenges and Future Research Directions in Multimodal Explainable Artificial Intelligence

As Artificial Intelligence (AI) continues to advance and find applications in various domains, the need for explainable AI becomes crucial. In the field of multimodal explainable AI (MXAI), which deals with multiple types of data, challenges arise in defining terminology, utilizing attention mechanisms, generalizing methods, extending explanations to more modalities, estimating causal explanations, and removing bias. Addressing these challenges is essential for improving transparency and trustworthiness in critical domains like healthcare.