Lack of Interpretability: One of the biggest challenges to apply machine-learning models, particularly deep learning, to leukemia diagnosis is interpretability of those machine learning models. The majority of machine learning, like Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN), is considered a black box as a result of their multi-tiered systems. As these models excel at detecting patterns and generating predictive guesses comparably (or even better) than physicians, their methods may be obfuscated to their human counterparts. Such a lack of interpretability is particularly troubling in clinical settings, where the ability understand how a diagnosis was reached is essential for trust building both with healthcare providers and patients. Clinicians need to understand why a model chooses a particular leukemia subtype or treatment. This can be a particularly important issue when making life-altering choices, like choosing aggressive chemotherapy or a marrow bone transplant. Researchers are actively working on methods for Explainable AI (XAI) to address this issue and enhance interpretability. Tools like saliency maps, feature importance rankings, and attention mechanisms enable clinicians to see how particular input features, say specific gene mutations or cell surface markers, played into the model’s decision.