The insights from research on automated data labeling can be leveraged to improve the interpretability and explainability of deep learning models in medical imaging applications by incorporating transparency and traceability into the labeling process. By documenting the iterative refinement steps and the rationale behind label adjustments, researchers can create a trail of explanations for how the final annotations were derived. This transparency enhances the interpretability of the deep learning models by providing insights into the data labeling process and the decision-making behind label adjustments. Additionally, utilizing techniques such as attention mechanisms or saliency maps can help highlight the key features in medical images that contribute to the final annotations, further improving the explainability of the deep learning models. By integrating these insights into the model development pipeline, researchers can enhance the trustworthiness and utility of deep learning models in medical imaging applications.