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Even state-of-the-art medical AI models like MedCLIP are vulnerable to real-world shortcuts that can compromise diagnostic reliability.
Students learned that annotation disagreement is a valuable insight into the complexity of data interpretation, not merely a flaw in labeling.
Adding more data from a new scanner can actually hurt your model by causing it to learn spurious correlations, even though clinical experts believe scanner variation is a key source of diversity.