The field of explainable AI is moving towards developing more transparent and trustworthy models for high-stakes applications, such as forensic age estimation, bone health classification, and infection prevention and control. Researchers are exploring novel architectures, such as Vision Transformers and mixture of experts, to improve model performance and interpretability. The use of techniques like autoencoders, variational autoencoders, and prototype-based learning is becoming increasingly popular for providing multi-faceted diagnostic insights and explicit analysis of model decisions. Furthermore, the integration of explainable AI with other technologies, such as blockchain, is being investigated to ensure safe data exchange and comprehensible AI-driven clinical decision-making. Noteworthy papers in this area include: An Autoencoder and Vision Transformer-based Interpretability Analysis, which introduces a framework for enhancing performance and transparency in forensic age estimation. ProtoMedX, which proposes a multi-modal model for bone health classification that provides explanations that can be visually understood by clinicians. Blockchain-Enabled Explainable AI for Trusted Healthcare Systems, which introduces a framework that incorporates blockchain and explainable AI methodologies to ensure data-level trust and decision-level trust in healthcare systems.