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Traditional machine learning models—especially complex ones like deep neural networks—often operate as “black boxes.” They take input (such as imaging data or lab results) and produce output (like a diagnosis or risk score) without offering a clear rationale behind the decision. This lack of transparency poses serious risks in medical settings where accountability, ethics, and patient safety are paramount.