Cancer is a complex and life-threatening disease that often requires multifaceted approaches for diagnosis and treatment. Traditional machine learning models, while powerful, operate as "black boxes," offering little insight into their decision-making processes. This lack of transparency can be problematic in a medical setting where understanding the rationale behind a diagnosis or treatment recommendation is crucial. XAI addresses this issue by making AI systems' decisions more interpretable and justifiable.
