The field of Artificial Intelligence in healthcare is moving towards a greater emphasis on explainability and transparency. Recent developments have highlighted the need for AI systems to provide human-interpretable explanations for their decision-making processes, particularly in high-stakes applications such as medical diagnosis and treatment. Researchers are exploring various techniques to address this challenge, including the development of explainable AI methods and hybrid approaches that combine statistical learning with expert rule-based knowledge. These innovations have the potential to increase clinician trust in AI systems and improve patient outcomes. Notable papers in this area include the introduction of xHAIM, a framework that leverages Generative AI to enhance both prediction and explainability, and EAGLE, a novel deep learning framework that enables efficient alignment of generalized latent embeddings for multimodal survival prediction with interpretable attribution analysis. These advancements are critical to bridging the gap between advanced AI capabilities and practical healthcare deployment.