The field of explainable AI (XAI) is making significant strides in biomedical signal analysis and agricultural applications. Recent developments have focused on creating more transparent and interpretable models for diagnosing diseases and analyzing signals. This shift towards XAI is crucial for building trust in AI-driven decision-making systems, particularly in high-stakes fields like healthcare and agriculture. Noteworthy papers in this area include the introduction of a lightweight model for ECG segmentation, which achieves high accuracy while providing clear explanations of its decision-making process. Another notable work is the development of a framework for generating counterfactual ECGs, which enhances the interpretability of AI-ECG models. Additionally, the application of XAI techniques to cough spectrograms has shown promise in distinguishing between different respiratory conditions. In agricultural applications, the use of XAI has enabled the development of more accurate and transparent models for plant disease diagnosis, such as the FloraSyntropy-Net framework. Overall, these advancements demonstrate the potential of XAI to improve the reliability and effectiveness of AI systems in biomedical and agricultural fields.