MEDIC-AD: Towards Medical Vision-Language Model's Clinical Intelligence

• URL: http://arxiv.org/abs/2603.27176v1
• Date: Sat, 28 Mar 2026 07:26:40 GMT
• Title: MEDIC-AD: Towards Medical Vision-Language Model's Clinical Intelligence
• Abstract summary: Current medical Vision-Language Models (VLMs) still lack mechanisms that translate their broad knowledge into clinically actionable outputs.<n>We present MEDIC-AD, a clinically oriented VLM that strengthens these three capabilities through a stage-wise framework.<n>We show that MEDIC-AD steadily boosts performance across anomaly detection, symptom tracking, and anomaly segmentation.
• Score: 17.512220295924816
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lesion detection, symptom tracking, and visual explainability are central to real-world medical image analysis, yet current medical Vision-Language Models (VLMs) still lack mechanisms that translate their broad knowledge into clinically actionable outputs. To bridge this gap, we present MEDIC-AD, a clinically oriented VLM that strengthens these three capabilities through a stage-wise framework. First, learnable anomaly-aware tokens (<Ano>) encourage the model to focus on abnormal regions and build more discriminative lesion centered representations. Second, inter image difference tokens (<Diff>) explicitly encode temporal changes between studies, allowing the model to distinguish worsening, improvement, and stability in disease burden. Finally, a dedicated explainability stage trains the model to generate heatmaps that highlight lesion-related regions, offering clear visual evidence that is consistent with the model's reasoning. Through our staged design, MEDIC-AD steadily boosts performance across anomaly detection, symptom tracking, and anomaly segmentation, achieving state-of-the-art results compared with both closed source and medical-specialized baselines. Evaluations on real longitudinal clinical data collected from real hospital workflows further show that MEDIC-AD delivers stable predictions and clinically faithful explanations in practical patient-monitoring and decision-support workflows

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