While deep learning approaches offer significant advantages in capturing complex patterns in aging-related degradation, they also have potential limitations that need to be addressed in future research. One limitation is the interpretability of deep learning models, as they often function as black boxes, making it challenging to understand the underlying factors driving degradation predictions. Future research could focus on developing explainable AI techniques to enhance model transparency and interpretability. Additionally, deep learning models may require large amounts of labeled data for training, which can be challenging to obtain in healthcare settings. Research efforts could explore techniques for transfer learning or semi-supervised learning to mitigate data scarcity issues. Moreover, deep learning models may struggle with handling rare or outlier cases, emphasizing the need for robust outlier detection mechanisms and data preprocessing strategies. Addressing these limitations can enhance the reliability and applicability of deep learning approaches in capturing the full complexity of aging-related degradation.