Despite their advantages, computational methods in histology face several challenges. One significant challenge is the need for high-quality, annotated datasets to train machine learning models. Additionally, the integration of computational tools into clinical workflows requires careful validation and standardization. There are also concerns about the interpretability of AI models, as complex algorithms can sometimes act as "black boxes," making it difficult to understand their decision-making processes.
