The iterative refinement strategy proposed for automated data labeling in facial landmark detection can be extended to other medical imaging tasks by adapting the process to suit the specific requirements of tasks like organ segmentation or lesion identification. For organ segmentation, the iterative refinement strategy can involve initial automated labeling of organ boundaries followed by iterative adjustments based on feedback mechanisms and advanced algorithms. This iterative process can refine the segmentation boundaries, ensuring accuracy and efficiency in identifying organ structures within medical images. Similarly, for lesion identification, the strategy can start with automated labeling of potential lesion areas and then iteratively refine these labels based on model predictions and feedback loops. By continuously improving the accuracy of lesion identification through iterations, the model can enhance its ability to detect and classify lesions in medical images effectively.