BiasCareVL is a bias-aware vision-language framework trained on 3.44 million medical samples that outperforms prior methods on clinical tasks like diagnosis and segmentation while aiming for equitable performance under data imbalances.
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Foundation models excel at pattern recognition in biomedical imaging but lack causal reasoning, robustness, and safety for real-world use, so they should augment rather than replace clinical expertise according to the proposed REAL-FM assessment framework.
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Bias-constrained multimodal intelligence for equitable and reliable clinical AI
BiasCareVL is a bias-aware vision-language framework trained on 3.44 million medical samples that outperforms prior methods on clinical tasks like diagnosis and segmentation while aiming for equitable performance under data imbalances.
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Foundation Models in Biomedical Imaging: Turning Hype into Reality
Foundation models excel at pattern recognition in biomedical imaging but lack causal reasoning, robustness, and safety for real-world use, so they should augment rather than replace clinical expertise according to the proposed REAL-FM assessment framework.