Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method

Explainability is essential for medical AI: The paper demonstrates that using LIME (Local Interpretable Model-Agnostic Explanations) to explain AI predictions is "critical for overcoming the 'black box' nature of AI, where decisions made by models are often opaque and unaccountable." This is particularly important in healthcare, where doctors need to understand why an AI system makes a diagnosis before trusting it with patient care.

High accuracy alone isn't sufficient: While the InceptionV3 model achieved 98.38% accuracy in detecting acute lymphoblastic leukemia, the researchers validated these results using explainability methods. This suggests that responsible AI development requires both strong performance metrics and transparency about how those results were achieved.

AI can address real healthcare challenges: The paper notes that traditional diagnostic methods for leukemia are "time-consuming, costly, and prone to errors." By automating detection through AI, this research shows how responsible AI implementation could improve both efficiency and reliability in medical diagnosis, potentially improving survival rates for children and teenagers with ALL.

Multiple AI models should be tested and compared: The study evaluated several transfer learning models (ResNet101V2, VGG19, InceptionV3, and InceptionResNetV2) rather than relying on a single approach. This comparative methodology reflects responsible AI practice by identifying which model performs best rather than deploying the first acceptable solution.