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FRAME reveals that up to 41% of racial performance differences in medical imaging may stem from sampling variation rather than actual bias, challenging conventional fairness assessments.
Self-supervised learning outperforms clinical supervision in aligning medical image representations, challenging assumptions about model interchangeability.
CANDOR reveals that even top-performing encoders misclassify nearly 18.4% of positive cases, challenging the notion of their reliability in clinical settings.
Scaling clinical LLMs doesn't guarantee safety: high-risk errors persist even with advanced RAG and max-context prompting, highlighting the critical role of evidence quality and deployment strategy.
