The recent developments in the field of neural network interpretability have seen a shift towards integrating concept-based explanations with traditional saliency methods. This approach aims to provide more comprehensive insights into model decisions by bridging the gap between local and global explanations. Innovations like Visual-TCAV are leading the way by combining concept activation vectors with saliency maps, enabling both the identification of concept locations within images and their attribution to specific class predictions. This dual capability is crucial for addressing transparency concerns and mitigating biases in model outputs. Additionally, there is a growing focus on the trade-offs between model efficiency and interpretability, particularly in resource-constrained environments. Studies are exploring how quantization techniques during training can influence both the accuracy and the clarity of saliency maps, emphasizing the need for careful parameter selection to balance these factors. Furthermore, the stability and fidelity of saliency maps are being rigorously examined, with research highlighting the impact of Gaussian smoothing on these properties. Theoretical and empirical evidence suggests a trade-off between stability and fidelity, which is critical for the practical application of these maps in real-world scenarios. Lastly, explainable models in oncology are advancing with the introduction of graph-based approaches that provide interpretable insights into patient risk predictions, contributing to more transparent and trustworthy precision medicine.