Despite their potential, current algorithms have limitations. They require large, high-quality datasets to be effective, which are not always available. Additionally, the "black box" nature of some algorithms, especially deep learning models, makes it difficult to understand how they arrive at specific decisions. This lack of transparency can be a hurdle in clinical settings where interpretability is crucial.
