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However, several challenges remain, including the black box nature of AI systems (such as issues of model interpretability and liability in AI-assisted diagnosis), reliance on high-quality annotated data, limited generalizability across real-world clinical settings (including model performance stability across novel scenarios, devices, diverse populations, and disease stages), and the potential for algorithmic bias (leading to diagnostic disparities due to overrepresentation of certain demographic groups in training data)

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