Context alignment in medical VLMs raises AUC from 0.918 to 0.925, cuts hallucinated keywords from 1.14 to 0.25, shortens explanations to 15.3 words, and maintains calibrated uncertainty without raising model confidence.
Multimodal computing in healthcare: Enhancing clinical decision-making through data fusion
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Towards Responsible Multimodal Medical Reasoning via Context-Aligned Vision-Language Models
Context alignment in medical VLMs raises AUC from 0.918 to 0.925, cuts hallucinated keywords from 1.14 to 0.25, shortens explanations to 15.3 words, and maintains calibrated uncertainty without raising model confidence.