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The article discusses the challenges of developing accurate and generalizable AI models for medical applications due to the increasing complexity of healthcare datasets. It introduces the concept of shortcut learning, where AI models learn to solve a task based on spurious correlations in the data rather than the task itself, and proposes a bias-corrected external accuracy estimate to better predict model performance.
