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Medical image reasoning claims are often inflated by shortcuts, with accuracy plummeting to 5.21% when these cues are mitigated.
Current vision-language models can generate fluent medical explanations, but struggle to ground them in relevant visual evidence, especially in emotionally charged scenarios.