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.
Aor: Anatomical ontology-guided reason- ing for medical large multimodal model in chest x-ray inter- pretation
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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.