One potential limitation of applying the iterative refinement strategy in real-world clinical settings is the need for extensive computational resources and time for each iteration, which may hinder the efficiency of the labeling process. Additionally, the reliance on automated algorithms for labeling may introduce biases or errors that could impact the accuracy of the final annotations. To address these challenges, it is essential to optimize the algorithms used for automated labeling to reduce computational overhead and streamline the iterative process. Implementing quality control measures and validation steps at each iteration can help identify and correct any biases or errors introduced by the automated labeling algorithms. Moreover, involving domain experts in the iterative refinement process can provide valuable insights and ensure the accuracy and clinical relevance of the labeled data.